data analysis for market research

How to Do Market Research: The Complete Guide

Learn how to do market research with this step-by-step guide, complete with templates, tools and real-world examples.

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Market research is the systematic process of gathering, analyzing and interpreting information about a specific market or industry.

What are your customers’ needs? How does your product compare to the competition? What are the emerging trends and opportunities in your industry? If these questions keep you up at night, it’s time to conduct market research.

Market research plays a pivotal role in your ability to stay competitive and relevant, helping you anticipate shifts in consumer behavior and industry dynamics. It involves gathering these insights using a wide range of techniques, from surveys and interviews to data analysis and observational studies.

In this guide, we’ll explore why market research is crucial, the various types of market research, the methods used in data collection, and how to effectively conduct market research to drive informed decision-making and success.

What is market research?

The purpose of market research is to offer valuable insight into the preferences and behaviors of your target audience, and anticipate shifts in market trends and the competitive landscape. This information helps you make data-driven decisions, develop effective strategies for your business, and maximize your chances of long-term growth.

Why is market research important? 

By understanding the significance of market research, you can make sure you’re asking the right questions and using the process to your advantage. Some of the benefits of market research include:

• Informed decision-making: Market research provides you with the data and insights you need to make smart decisions for your business. It helps you identify opportunities, assess risks and tailor your strategies to meet the demands of the market. Without market research, decisions are often based on assumptions or guesswork, leading to costly mistakes.
• Customer-centric approach: A cornerstone of market research involves developing a deep understanding of customer needs and preferences. This gives you valuable insights into your target audience, helping you develop products, services and marketing campaigns that resonate with your customers.
• Competitive advantage: By conducting market research, you’ll gain a competitive edge. You’ll be able to identify gaps in the market, analyze competitor strengths and weaknesses, and position your business strategically. This enables you to create unique value propositions, differentiate yourself from competitors, and seize opportunities that others may overlook.
• Risk mitigation: Market research helps you anticipate market shifts and potential challenges. By identifying threats early, you can proactively adjust their strategies to mitigate risks and respond effectively to changing circumstances. This proactive approach is particularly valuable in volatile industries.
• Resource optimization: Conducting market research allows organizations to allocate their time, money and resources more efficiently. It ensures that investments are made in areas with the highest potential return on investment, reducing wasted resources and improving overall business performance.
• Adaptation to market trends: Markets evolve rapidly, driven by technological advancements, cultural shifts and changing consumer attitudes. Market research ensures that you stay ahead of these trends and adapt your offerings accordingly so you can avoid becoming obsolete. 

As you can see, market research empowers businesses to make data-driven decisions, cater to customer needs, outperform competitors, mitigate risks, optimize resources and stay agile in a dynamic marketplace. These benefits make it a huge industry; the global market research services market is expected to grow from $76.37 billion in 2021 to $108.57 billion in 2026 . Now, let’s dig into the different types of market research that can help you achieve these benefits.

Types of market research 

• Qualitative research
• Quantitative research
• Exploratory research
• Descriptive research
• Causal research
• Cross-sectional research
• Longitudinal research

Despite its advantages, 23% of organizations don’t have a clear market research strategy. Part of developing a strategy involves choosing the right type of market research for your business goals. The most commonly used approaches include:

1. Qualitative research

Qualitative research focuses on understanding the underlying motivations, attitudes and perceptions of individuals or groups. It is typically conducted through techniques like in-depth interviews, focus groups and content analysis — methods we’ll discuss further in the sections below. Qualitative research provides rich, nuanced insights that can inform product development, marketing strategies and brand positioning.

2. Quantitative research

Quantitative research, in contrast to qualitative research, involves the collection and analysis of numerical data, often through surveys, experiments and structured questionnaires. This approach allows for statistical analysis and the measurement of trends, making it suitable for large-scale market studies and hypothesis testing. While it’s worthwhile using a mix of qualitative and quantitative research, most businesses prioritize the latter because it is scientific, measurable and easily replicated across different experiments.

3. Exploratory research

Whether you’re conducting qualitative or quantitative research or a mix of both, exploratory research is often the first step. Its primary goal is to help you understand a market or problem so you can gain insights and identify potential issues or opportunities. This type of market research is less structured and is typically conducted through open-ended interviews, focus groups or secondary data analysis. Exploratory research is valuable when entering new markets or exploring new product ideas.

4. Descriptive research

As its name implies, descriptive research seeks to describe a market, population or phenomenon in detail. It involves collecting and summarizing data to answer questions about audience demographics and behaviors, market size, and current trends. Surveys, observational studies and content analysis are common methods used in descriptive research. 

5. Causal research

Causal research aims to establish cause-and-effect relationships between variables. It investigates whether changes in one variable result in changes in another. Experimental designs, A/B testing and regression analysis are common causal research methods. This sheds light on how specific marketing strategies or product changes impact consumer behavior.

6. Cross-sectional research

Cross-sectional market research involves collecting data from a sample of the population at a single point in time. It is used to analyze differences, relationships or trends among various groups within a population. Cross-sectional studies are helpful for market segmentation, identifying target audiences and assessing market trends at a specific moment.

7. Longitudinal research

Longitudinal research, in contrast to cross-sectional research, collects data from the same subjects over an extended period. This allows for the analysis of trends, changes and developments over time. Longitudinal studies are useful for tracking long-term developments in consumer preferences, brand loyalty and market dynamics.

Each type of market research has its strengths and weaknesses, and the method you choose depends on your specific research goals and the depth of understanding you’re aiming to achieve. In the following sections, we’ll delve into primary and secondary research approaches and specific research methods.

Primary vs. secondary market research

Market research of all types can be broadly categorized into two main approaches: primary research and secondary research. By understanding the differences between these approaches, you can better determine the most appropriate research method for your specific goals.

Primary market research 

Primary research involves the collection of original data straight from the source. Typically, this involves communicating directly with your target audience — through surveys, interviews, focus groups and more — to gather information. Here are some key attributes of primary market research:

• Customized data: Primary research provides data that is tailored to your research needs. You design a custom research study and gather information specific to your goals.
• Up-to-date insights: Because primary research involves communicating with customers, the data you collect reflects the most current market conditions and consumer behaviors.
• Time-consuming and resource-intensive: Despite its advantages, primary research can be labor-intensive and costly, especially when dealing with large sample sizes or complex study designs. Whether you hire a market research consultant, agency or use an in-house team, primary research studies consume a large amount of resources and time.

Secondary market research 

Secondary research, on the other hand, involves analyzing data that has already been compiled by third-party sources, such as online research tools, databases, news sites, industry reports and academic studies.

Here are the main characteristics of secondary market research:

• Cost-effective: Secondary research is generally more cost-effective than primary research since it doesn’t require building a research plan from scratch. You and your team can look at databases, websites and publications on an ongoing basis, without needing to design a custom experiment or hire a consultant. 
• Leverages multiple sources: Data tools and software extract data from multiple places across the web, and then consolidate that information within a single platform. This means you’ll get a greater amount of data and a wider scope from secondary research.
• Quick to access: You can access a wide range of information rapidly — often in seconds — if you’re using online research tools and databases. Because of this, you can act on insights sooner, rather than taking the time to develop an experiment. 

So, when should you use primary vs. secondary research? In practice, many market research projects incorporate both primary and secondary research to take advantage of the strengths of each approach.

One rule of thumb is to focus on secondary research to obtain background information, market trends or industry benchmarks. It is especially valuable for conducting preliminary research, competitor analysis, or when time and budget constraints are tight. Then, if you still have knowledge gaps or need to answer specific questions unique to your business model, use primary research to create a custom experiment. 

Market research methods

• Surveys and questionnaires
• Focus groups
• Observational research
• Online research tools
• Experiments
• Content analysis
• Ethnographic research

How do primary and secondary research approaches translate into specific research methods? Let’s take a look at the different ways you can gather data: 

1. Surveys and questionnaires

Surveys and questionnaires are popular methods for collecting structured data from a large number of respondents. They involve a set of predetermined questions that participants answer. Surveys can be conducted through various channels, including online tools, telephone interviews and in-person or online questionnaires. They are useful for gathering quantitative data and assessing customer demographics, opinions, preferences and needs. On average, customer surveys have a 33% response rate , so keep that in mind as you consider your sample size.

2. Interviews

Interviews are in-depth conversations with individuals or groups to gather qualitative insights. They can be structured (with predefined questions) or unstructured (with open-ended discussions). Interviews are valuable for exploring complex topics, uncovering motivations and obtaining detailed feedback. 

3. Focus groups

The most common primary research methods are in-depth webcam interviews and focus groups. Focus groups are a small gathering of participants who discuss a specific topic or product under the guidance of a moderator. These discussions are valuable for primary market research because they reveal insights into consumer attitudes, perceptions and emotions. Focus groups are especially useful for idea generation, concept testing and understanding group dynamics within your target audience.

4. Observational research

Observational research involves observing and recording participant behavior in a natural setting. This method is particularly valuable when studying consumer behavior in physical spaces, such as retail stores or public places. In some types of observational research, participants are aware you’re watching them; in other cases, you discreetly watch consumers without their knowledge, as they use your product. Either way, observational research provides firsthand insights into how people interact with products or environments.

5. Online research tools

You and your team can do your own secondary market research using online tools. These tools include data prospecting platforms and databases, as well as online surveys, social media listening, web analytics and sentiment analysis platforms. They help you gather data from online sources, monitor industry trends, track competitors, understand consumer preferences and keep tabs on online behavior. We’ll talk more about choosing the right market research tools in the sections that follow.

6. Experiments

Market research experiments are controlled tests of variables to determine causal relationships. While experiments are often associated with scientific research, they are also used in market research to assess the impact of specific marketing strategies, product features, or pricing and packaging changes.

7. Content analysis

Content analysis involves the systematic examination of textual, visual or audio content to identify patterns, themes and trends. It’s commonly applied to customer reviews, social media posts and other forms of online content to analyze consumer opinions and sentiments.

8. Ethnographic research

Ethnographic research immerses researchers into the daily lives of consumers to understand their behavior and culture. This method is particularly valuable when studying niche markets or exploring the cultural context of consumer choices.

How to do market research

• Set clear objectives
• Identify your target audience
• Choose your research methods
• Use the right market research tools
• Collect data
• Analyze data 
• Interpret your findings
• Identify opportunities and challenges
• Make informed business decisions
• Monitor and adapt

Now that you have gained insights into the various market research methods at your disposal, let’s delve into the practical aspects of how to conduct market research effectively. Here’s a quick step-by-step overview, from defining objectives to monitoring market shifts.

1. Set clear objectives

When you set clear and specific goals, you’re essentially creating a compass to guide your research questions and methodology. Start by precisely defining what you want to achieve. Are you launching a new product and want to understand its viability in the market? Are you evaluating customer satisfaction with a product redesign? 

Start by creating SMART goals — objectives that are specific, measurable, achievable, relevant and time-bound. Not only will this clarify your research focus from the outset, but it will also help you track progress and benchmark your success throughout the process. 

You should also consult with key stakeholders and team members to ensure alignment on your research objectives before diving into data collecting. This will help you gain diverse perspectives and insights that will shape your research approach.

2. Identify your target audience

Next, you’ll need to pinpoint your target audience to determine who should be included in your research. Begin by creating detailed buyer personas or stakeholder profiles. Consider demographic factors like age, gender, income and location, but also delve into psychographics, such as interests, values and pain points.

The more specific your target audience, the more accurate and actionable your research will be. Additionally, segment your audience if your research objectives involve studying different groups, such as current customers and potential leads.

If you already have existing customers, you can also hold conversations with them to better understand your target market. From there, you can refine your buyer personas and tailor your research methods accordingly.

3. Choose your research methods

Selecting the right research methods is crucial for gathering high-quality data. Start by considering the nature of your research objectives. If you’re exploring consumer preferences, surveys and interviews can provide valuable insights. For in-depth understanding, focus groups or observational research might be suitable. Consider using a mix of quantitative and qualitative methods to gain a well-rounded perspective. 

You’ll also need to consider your budget. Think about what you can realistically achieve using the time and resources available to you. If you have a fairly generous budget, you may want to try a mix of primary and secondary research approaches. If you’re doing market research for a startup , on the other hand, chances are your budget is somewhat limited. If that’s the case, try addressing your goals with secondary research tools before investing time and effort in a primary research study. 

4. Use the right market research tools

Whether you’re conducting primary or secondary research, you’ll need to choose the right tools. These can help you do anything from sending surveys to customers to monitoring trends and analyzing data. Here are some examples of popular market research tools:

• Market research software: Crunchbase is a platform that provides best-in-class company data, making it valuable for market research on growing companies and industries. You can use Crunchbase to access trusted, first-party funding data, revenue data, news and firmographics, enabling you to monitor industry trends and understand customer needs.

• Survey and questionnaire tools: SurveyMonkey is a widely used online survey platform that allows you to create, distribute and analyze surveys. Google Forms is a free tool that lets you create surveys and collect responses through Google Drive.
• Data analysis software: Microsoft Excel and Google Sheets are useful for conducting statistical analyses. SPSS is a powerful statistical analysis software used for data processing, analysis and reporting.
• Social listening tools: Brandwatch is a social listening and analytics platform that helps you monitor social media conversations, track sentiment and analyze trends. Mention is a media monitoring tool that allows you to track mentions of your brand, competitors and keywords across various online sources.
• Data visualization platforms: Tableau is a data visualization tool that helps you create interactive and shareable dashboards and reports. Power BI by Microsoft is a business analytics tool for creating interactive visualizations and reports.

5. Collect data

There’s an infinite amount of data you could be collecting using these tools, so you’ll need to be intentional about going after the data that aligns with your research goals. Implement your chosen research methods, whether it’s distributing surveys, conducting interviews or pulling from secondary research platforms. Pay close attention to data quality and accuracy, and stick to a standardized process to streamline data capture and reduce errors. 

6. Analyze data

Once data is collected, you’ll need to analyze it systematically. Use statistical software or analysis tools to identify patterns, trends and correlations. For qualitative data, employ thematic analysis to extract common themes and insights. Visualize your findings with charts, graphs and tables to make complex data more understandable.

If you’re not proficient in data analysis, consider outsourcing or collaborating with a data analyst who can assist in processing and interpreting your data accurately.

7. Interpret your findings

Interpreting your market research findings involves understanding what the data means in the context of your objectives. Are there significant trends that uncover the answers to your initial research questions? Consider the implications of your findings on your business strategy. It’s essential to move beyond raw data and extract actionable insights that inform decision-making.

Hold a cross-functional meeting or workshop with relevant team members to collectively interpret the findings. Different perspectives can lead to more comprehensive insights and innovative solutions.

8. Identify opportunities and challenges

Use your research findings to identify potential growth opportunities and challenges within your market. What segments of your audience are underserved or overlooked? Are there emerging trends you can capitalize on? Conversely, what obstacles or competitors could hinder your progress?

Lay out this information in a clear and organized way by conducting a SWOT analysis, which stands for strengths, weaknesses, opportunities and threats. Jot down notes for each of these areas to provide a structured overview of gaps and hurdles in the market.

9. Make informed business decisions

Market research is only valuable if it leads to informed decisions for your company. Based on your insights, devise actionable strategies and initiatives that align with your research objectives. Whether it’s refining your product, targeting new customer segments or adjusting pricing, ensure your decisions are rooted in the data.

At this point, it’s also crucial to keep your team aligned and accountable. Create an action plan that outlines specific steps, responsibilities and timelines for implementing the recommendations derived from your research. 

10. Monitor and adapt

Market research isn’t a one-time activity; it’s an ongoing process. Continuously monitor market conditions, customer behaviors and industry trends. Set up mechanisms to collect real-time data and feedback. As you gather new information, be prepared to adapt your strategies and tactics accordingly. Regularly revisiting your research ensures your business remains agile and reflects changing market dynamics and consumer preferences.

Online market research sources

As you go through the steps above, you’ll want to turn to trusted, reputable sources to gather your data. Here’s a list to get you started:

• Crunchbase: As mentioned above, Crunchbase is an online platform with an extensive dataset, allowing you to access in-depth insights on market trends, consumer behavior and competitive analysis. You can also customize your search options to tailor your research to specific industries, geographic regions or customer personas.

• Academic databases: Academic databases, such as ProQuest and JSTOR , are treasure troves of scholarly research papers, studies and academic journals. They offer in-depth analyses of various subjects, including market trends, consumer preferences and industry-specific insights. Researchers can access a wealth of peer-reviewed publications to gain a deeper understanding of their research topics.
• Government and NGO databases: Government agencies, nongovernmental organizations and other institutions frequently maintain databases containing valuable economic, demographic and industry-related data. These sources offer credible statistics and reports on a wide range of topics, making them essential for market researchers. Examples include the U.S. Census Bureau , the Bureau of Labor Statistics and the Pew Research Center .
• Industry reports: Industry reports and market studies are comprehensive documents prepared by research firms, industry associations and consulting companies. They provide in-depth insights into specific markets, including market size, trends, competitive analysis and consumer behavior. You can find this information by looking at relevant industry association databases; examples include the American Marketing Association and the National Retail Federation .
• Social media and online communities: Social media platforms like LinkedIn or Twitter (X) , forums such as Reddit and Quora , and review platforms such as G2 can provide real-time insights into consumer sentiment, opinions and trends. 

Market research examples

At this point, you have market research tools and data sources — but how do you act on the data you gather? Let’s go over some real-world examples that illustrate the practical application of market research across various industries. These examples showcase how market research can lead to smart decision-making and successful business decisions.

Example 1: Apple’s iPhone launch

Apple ’s iconic iPhone launch in 2007 serves as a prime example of market research driving product innovation in tech. Before the iPhone’s release, Apple conducted extensive market research to understand consumer preferences, pain points and unmet needs in the mobile phone industry. This research led to the development of a touchscreen smartphone with a user-friendly interface, addressing consumer demands for a more intuitive and versatile device. The result was a revolutionary product that disrupted the market and redefined the smartphone industry.

Example 2: McDonald’s global expansion

McDonald’s successful global expansion strategy demonstrates the importance of market research when expanding into new territories. Before entering a new market, McDonald’s conducts thorough research to understand local tastes, preferences and cultural nuances. This research informs menu customization, marketing strategies and store design. For instance, in India, McDonald’s offers a menu tailored to local preferences, including vegetarian options. This market-specific approach has enabled McDonald’s to adapt and thrive in diverse global markets.

Example 3: Organic and sustainable farming

The shift toward organic and sustainable farming practices in the food industry is driven by market research that indicates increased consumer demand for healthier and environmentally friendly food options. As a result, food producers and retailers invest in sustainable sourcing and organic product lines — such as with these sustainable seafood startups — to align with this shift in consumer values. 

The bottom line? Market research has multiple use cases and is a critical practice for any industry. Whether it’s launching groundbreaking products, entering new markets or responding to changing consumer preferences, you can use market research to shape successful strategies and outcomes.

Market research templates

You finally have a strong understanding of how to do market research and apply it in the real world. Before we wrap up, here are some market research templates that you can use as a starting point for your projects:

• Smartsheet competitive analysis templates : These spreadsheets can serve as a framework for gathering information about the competitive landscape and obtaining valuable lessons to apply to your business strategy.
• SurveyMonkey product survey template : Customize the questions on this survey based on what you want to learn from your target customers.
• HubSpot templates : HubSpot offers a wide range of free templates you can use for market research, business planning and more.
• SCORE templates : SCORE is a nonprofit organization that provides templates for business plans, market analysis and financial projections.
• SBA.gov : The U.S. Small Business Administration offers templates for every aspect of your business, including market research, and is particularly valuable for new startups. 

Strengthen your business with market research

When conducted effectively, market research is like a guiding star. Equipped with the right tools and techniques, you can uncover valuable insights, stay competitive, foster innovation and navigate the complexities of your industry.

Throughout this guide, we’ve discussed the definition of market research, different research methods, and how to conduct it effectively. We’ve also explored various types of market research and shared practical insights and templates for getting started. 

Now, it’s time to start the research process. Trust in data, listen to the market and make informed decisions that guide your company toward lasting success.

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How to use data analysis methods and techniques to create customer-centric marketing campaigns

Putting on your creativity cap to strategize a new marketing campaign is exciting—but what makes your hard work really feel worth it is when it resonates with your audience and performs well. 

While some marketers might be lucky enough to find a winning formula on the first try, you need to use data analysis methods to guide your campaign strategy.

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Putting customers at the center of your decision-making through data analysis helps you create relevant campaigns that connect with your audience and hit KPI goals. This guide reviews five data analysis methods marketers need to make informed decisions .

The types of data analysis a marketer uses depend on what you want to learn and which data type you have. Marketers use quantitative data—like website traffic totals—and qualitative data—like customer interview transcripts—in data analysis. There are also times when you should combine quant and qual data. 

Five data analysis methods marketers use include

Descriptive analytics to summarize quantitative data

Inferential analysis to create and test customer hypotheses

Regression analysis to compare the relationship between variables

Content analysis to quantify text-based insights

Predictive analysis to anticipate trends and learn about customer behavior 

5 popular data analysis methods for marketers

Marketers use data analytics to review performance, prioritize campaign updates, and understand customers. As you’ll see in the data analysis techniques below, the method you choose depends on what you learn and the data you collect. 

The types of data marketers most commonly use are:

Now that you know the different types of information marketers collect, use the methods below in your data analysis process to get the best results, prioritize product updates, and inform your business decisions. 

1. Descriptive analysis

When you want to measure what happened in the past, use descriptive analytics . This data analysis method summarizes quantitative data results, like how many likes a social media post got or your newsletter sign-up rate . Popular descriptive analysis methods include average, median, mode, and simply comparing survey response rates of a multiple-choice question. 

How marketers use descriptive analysis:

Benchmark organic traffic each month to understand the impact of content marketing 

Compare campaign engagement and conversion results to quarterly goals

Use surveys to measure how prevalent a goal or problem is in your customer base 

Benefits and challenges of descriptive analytics:

Many people have some experience with straightforward analysis methods like calculating an average or ranking percentage response, which makes this method quick to implement

Data collection becomes easy through existing web analytics or short surveys

Quantitative data is objective, which means there’s no room for differing interpretations

There are limitations to descriptive analytics:

The process measures an outcome but doesn’t describe why customers chose the response or behaved a certain way

You may lack context about a problem if you only look at one particular data point

You need enough data points to have statistical significance if you want to apply decisions to your entire audience confidently

❓ Pro tip: use premade survey questions to easily collect customer feedback.

Asking your audience questions with multiple-choice surveys is a great way to collect quantitative data—assuming you ask the right questions. Your research questions need to be specific enough to get relevant data but not so detailed that you accidentally lead customers to the answer you want to hear. 

Use premade survey questions and leverage Hotjar AI to generate survey questions based on your goals to collect the right data. 

Here are some survey questions marketers can use:

Where did you first hear about us?

Where else did you hear about us?

What do you use our product/service for?

Which industry does your company belong to?

How relevant is the content you found on our website?

What was your first impression of our product?

Browse 30+ ready-to-use survey templates .

Traffic attribution surveys reveal every channel customers hear about you through, instead of only seeing the referral source of your website analytics

2. Inferential analysis

Sometimes, you have a hunch but want to back up your ideas with data. Inferential analysis lets you hypothesize about your customers’ preferences and motivations by using a mix of multiple quantitative or qualitative data points . You create an inference by stacking insights observed at the same time. 

For example, an Engagement Zone heatmap combines interaction data on a website page—like clicks and scrolling—to highlight the elements visitors engage with the most. If your heatmap reveals that a particular image or headline draws visitors in, you can use it in a future campaign to test if it drives engagement across other channels.

How marketers use inferential analysis:

Ask multiple questions in a customer interview and combine common themes to create a product narrative

Research customer preferences or priorities between item categories with surveys

Compare on-page customer feedback from different referral sources to learn about your audience coming from each traffic source

Benefits and challenges of inferential analysis:

Comparing customer responses across multiple questions or touchpoints gives you a fuller understanding of user behavior

Weighing customer responses based on specific conditions—like customer segments with the highest average order value—helps you prioritize which feedback and suggestions to implement

Creating hypotheses from actual customer interactions provides campaign ideas you may not have thought about before

There are drawbacks to inferential analysis:

Your hypothesis-building can be subjective, so look for multiple customer responses or data points that validate an assumption instead of relying on a single insight

You need to collect and manage multiple data sets, which can be time-consuming

An inference is an informed guess, so you still need to test your hypothesis with A/B testing

📹 Pro tip: follow up your A/B testing analysis with recordings. 

Let’s say your A/B testing reveals that one campaign landing page far outperforms the other. Do you know why? Dig into why particular copy or design was so compelling using recordings —video playbacks of how visitors behaved on your site. Then, you can apply what you learned to future campaigns. 

Hotjar Recordings lets you see what customers see

3. Regression analysis

Regression analysis is a powerful statistical analysis method that measures the relationship between data points, like comparing whether increased marketing spending is related to more revenue . The basic process of regression analysis involves plotting your two variables on a chart and then seeing how far those points stray from the regression line. There's a correlation if the data sits close to the line. 

Since regression analysis includes multiple variables and some equations, it’s common to use a spreadsheet add-in or a tool like Tableau or The R Project .

Linear regression analysis can help answer the question, ‘Does more SEO investment lead to more sales?’ Image via PracticalEcommerce  

How marketers use regression analysis:

Discover which blogs shared on what social media channels resulted in the highest website traffic to update your social sharing strategy

Compare email engagement metrics to website sales to measure the potential impact of the channel

Learn which customer segment is happiest with your company and product through a survey to refine your targeting and messaging

Regression analysis benefits and challenges:

Measure how variables relate to one another to prove marketing impact

Evaluate what to do more of—for example, if you find a correlation between an investment or campaign and increased sales

Analyze large data sets using regression analysis tools and spreadsheets

There are downsides to regression analysis:

The process is a bit more complicated than simply checking your Google Analytics dashboard, so you’ll need a specialized tool or spreadsheet

Correlation isn’t causation, and you might not account for all potential variables that affect an outcome

A few outliers can easily skew results

4. Content analysis

Content analysis turns qualitative insights into quantifiable results to help you make conclusions about customer perspectives, perceptions, and motivations . For example, you can count how many open-ended survey question responses mentioned particular themes to rank their importance to your audience. 

Pull content analysis data from open-ended surveys, recordings of real website interactions, interviews, reviews, testimonials, social comments, and brand mentions. You could even run a content analysis on competitor reviews to find what their customers dislike to position your brand against it.

How marketers use content analysis:

Compare repeating themes across customer interviews

Map the most common customer journey steps by watching recordings to learn how customers navigate your brand’s website before purchasing

Review testimonials to discover what stands out to customers to use in future campaigns

Benefits and challenges of content analysis:

You can pull from a wide range of data sources depending on what you already have access to and the time you have to research

Quantifying responses turns subjective responses into objective numbers

It’s easier to share customer response summaries with stakeholders than sharing multiple clips or large qualitative data sets

There are obstacles with content analysis:

Manual text analysis is slow, but there are tools like Lexalytics that help

There’s still some subjectivity involved since you decide how to group responses

Reducing long responses to simple ideas can leave valuable insights behind

🗂️ Pro tip: keep your insights organized with Hotjar.

Content analysis is time consuming (but impactful!) at the best of times, but you’ll quickly get confused without organization. Hotjar Highlights lets you save and share specific insights, like a high click rate on an image (from a heatmap) or a recording snippet from your newsletter page, to stay organized and collaborate with your team.

5. Predictive analysis

Predictive analytics anticipates future trends or analyzes customer behaviors with big data sets, predictive models, artificial intelligence (AI), and machine learning tools. In other words, it’s a bit advanced. However, marketers can unlock powerful insights, like when L’Oréal and Synthesio used AI to forecast beauty trends.

If you don’t want to work with a specialized agency or consultant, there are predictive analytics tools that help marketers without advanced data analysis skills pull insights from customer data.

How marketers use predictive analytics:

Uncover new customer segments based on small differentiating behaviors and psychographics

Find related products to recommend to customers based on past purchases for personalized experiences 

Anticipate trends in your industry to create innovative campaigns

Benefits and challenges of predictive analytics:

You can review vast amounts of quantitative data faster than previously possible with technology like machine learning and AI

Nuanced customer insights and trend data give you a competitive advantage 

Easily analyze customer behavior at scale, as opposed to manually reviewing a few interview transcripts

There are drawbacks to predictive analytics:

The output is only as good as the raw data input, so incomplete or inaccurate data within a large dataset can skew results

Collecting the volume and variety of data you need for predictive analytics can be time consuming

You’ll likely need to use a specialized tool or work with a data analyst

🚦 Pro tip: monitor customer behavior with Hotjar Trends.

Hotjar Trends lets you visualize your customer behavior metrics so you can spot trends easily. 

For example, you can compare how customer segments interact with your pricing page to spot frustration or confusion that signals your marketing funnel needs an extra step. While trends don’t predict the future, continuous tracking gives you an early signal of wins or challenges. 

Hotjar makes data visualization easy, with pie charts, line graphs, or bar graphs that give you a high-level understanding of user behavior

Combine data analysis with empathy to create effective campaigns

When you’re knee-deep in spreadsheets and up to your eyes in statistics, it’s easy to view customers as just numbers on the screen. Leading with empathy and curiosity will give you a new perspective on data analysis methods. 

If you have a question, ask your customers in a survey. If you want to understand their motivations, chat with them in an interview. If you want to see how they move through your website’s marketing funnel, watch a recording of their behavior. 

Your best strategies and campaigns come from a blend of data and humanity. Simply begin with a question or hypothesis and start investigating and analyzing. 

Use customer-centric insights to drive your marketing campaigns

Hotjar’s tools give you a direct line to customers to learn about their goals, challenges, and preferences, so you create successful campaigns.

FAQs about data analysis methods for marketers

Why should marketers use data analytics.

Marketers use data analysis to review performance and understand customers, so they can create relevant campaigns.

What types of data do marketers use?

Marketers use numbers-based quantitative data, text-based qualitative data, or a combination of both for data analysis.

Which data analysis methods can marketers use?

Five data analysis methods marketers use are

Descriptive analytics

Inferential analysis

Regression analysis

Content analysis

Predictive analysis

Data analysis process

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Data analysis tools

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How to do Market Analysis in 6 Easy Steps

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Knowing how to do market analysis is pivotal for many roles, benefiting any organization, regardless of its size, scope, or sector.

Regular market analysis levels up your individual ability to spot potential opportunities, stay on top of current trends, and gives you insights into the competitive landscape .

This article will cover why you need to analyze a market frequently and shows you how to do a basic market analysis in 6 straightforward steps.

What is a market analysis?

Market analysis is the process of gathering data about a target market . It examines the competitive landscape, consumers, and conditions that impact the marketplace.

The benefits of market analysis

Here are eight reasons why a regular market analysis is beneficial:

• Understand the competitive landscape
• Spot trends in your market
• Uncover opportunities for growth or diversification
• Reduce either risk or cost for launching new products or services
• Develop a deeper understanding of a target audience
• Enhance marketing efforts or discover ways to change
• Analyze business performance within a market
• Identify new segments of a market to target

Why you should conduct a market analysis

Aside from the benefits we’ve already listed, reviewing and redoing your market analysis regularly is important . Here’s why.

• Markets shift
• Consumer behaviors change
• New players enter existing markets
• Disruptive technologies and enhancements to rival offerings can shift the landscape
• External events impact market conditions that drive changes

If you already know how to do market analysis, ask yourself how frequently you undertake the task: is it annually or quarterly? And consider the time it takes and the tools you used to obtain your information.

With this in mind, we’ll walk you through the most effective market analysis methods. Showing you the steps to take, with market analysis examples, to bring these steps to life.

How to conduct a market analysis

These six steps break down how to analyze a market into easy-to-follow, digestible stages.

Before you start: Use a framework to record your findings. There are plenty of visualization tools, but a basic excel sheet will be fine if you want to keep it simple. Why? Because when you return to review this analysis and repeat this exercise, you’ll want to have everything recorded in a single place. It will save you time and make any future comparisons easier.

Step 1 – Market segmentation

What: Whether you want to enter a new market , launch a new product, or simply assess opportunities for an existing business, this first step in the market analysis process is crucial yet often overlooked.

Why: Market segmentation helps you identify the core segments of a market to target. By identifying the portion of a market your products will be suitable for, you can accurately define the market size and better understand your potential customers’ specific needs and preferences.

How: There are multiple ways you can segment a market, and the right approach will depend on your product, its customers, and its target profiles.

Here, we can see how a segment is built using Similarweb’s website segment feature. I specifically want to view the credit card sector in the US, a market made up of pure players (think Amex or Visa ) and individual players with credit card lines as one of their segments (think Wells Fargo or USAA ). By splitting up a market like this, I can analyze the areas of business I care about more for my market analysis.

So, instead of viewing data that encompasses the other lines of business the likes of Wells Fargo and the USAA handle, such as loans, I get to hone in on their credit card segments only.

This is just one example of market segmentation. You can also segment a market based on consumer needs, ideal consumer profiles, regions, and other demographic data.

Step 2 – Market sizing

What: Market sizing determines your target market’s potential volume or sales revenue. It’s an essential component of market analysis that uses either secondary or primary research to explore the actual size of the market you are in or wish to enter. 

Total Addressable Market (TAM) – This gives you the complete value of the overall market and the first step in the market sizing process . Let’s say we want to analyze the US credit card market, the TAM would account for the whole of this market. Service Addressable Market (SAM) looks at potential audience volumes for a product or service in a target region. Sticking with the credit card sector example, this could be the total value of the credit card business that specifically targets the ‘poor credit rating’ segment of this market. Share of Market (SOM) – Also known as ‘service addressable market,’ it represents the proportion of your SAM that you are likely to achieve. SOM is always lower than SAM, taking a range of estimates based on the previous year’s performance or current market share + project growth to arrive at this figure.

Why: Market sizing helps businesses understand the size of their opportunity. By understanding the size and scope of a market, companies can better assess the potential profitability of the market. Tracking market share over time can also show who wins or loses at any given time.

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Market analysis example: market sizing

Using a metric known as traffic share , we can estimate the potential market size by showing the total reachable audience you have or could have with a product or service.

Using Similarweb Industry Analysis , I can see a real-time snapshot of my market’s performance. With it, I can see the total number of people in a market (unique visitors) and establish how much of that share I have or will target this year.

When sizing a market, it’s easy to fall into the habit of analyzing the market quarterly or annually. But often, the best insights are dynamic in nature. They appear to show shifts, sometimes unexpectedly or can indicate growth and changing behaviors as the year progresses. This is why we place a high emphasis on continuing a market analysis throughout the year.

Here, we’re looking at traffic share changes over time using Similarweb’s market trends. You can see the impact of Snychrony’s growth (in green) as they gain traction, along with USAA (purple). At the start of the year, these two players had no impact on the market. By the end of 2022, they’re showing gains and would be two key competitors to track when you reach step 4 of the market analysis process.

Those analyzing a market annually would miss out on key insights that show the rise of these two emerging players. At the end of the year, they’ve already grabbed a chunk of the market and, if they continue on the same trajectory, will continue to do so in 2023.

With the right tools, you get a dynamic view of the market data you need, allowing you to change tactics when markets shift.

Step 3 – Market trends

What: Reviewing current trends is key to any good market analysis. As we all know, trends can rise and fall at a moment’s notice. This is why this activity, in particular, is one you should routinely perform.

Why: Keeping a finger on the pulse can help you adapt and flex, at the right time, in the right way. Market trends give you insights into the current market situation and potential opportunities and challenges. Doing so can help you identify areas for growth, spot potential risks, and plan effective strategies. Market trends can also provide valuable information about customer preferences, competition, and economic and technological developments. By monitoring these trends, businesses can stay ahead of the curve and make informed decisions that will benefit their bottom line.

You may have heard about ChatGPT in the press ; this is an example of a highly-disruptive technology that has the potential to completely shift an entire market; many, in fact. It managed to gain over 1 million users within its first week on the market. And it’s a great example of why regular market trends analysis should occur.

How: There are lots of ways new market trends can surface. Consumer behavior , economic trends, technological advancements, and the competitive landscape can impact how markets behave. Legal and regulatory changes can also influence trends and changes too.

Staying up to date with industry news and legislation changes is useful. But it takes time, and it’s not always the most effective way to know when consumer sentiment changes.

Market research surveys are one way to understand customer attitudes and needs and how they shift over time. However, it’s not the most effective way to inform your market analysis. Particularly when you want real-time market intel.

Market analysis example: trend detection 

Similarweb analyzes billions of data signals daily to deliver game-changing insights about any market, region, or individual company. So, as we look at how to do market analysis, I wanted to share a practical example of how clients use Similarweb to spot trends in a market.

Wonderbly , a global business, provides personalized books, serving over 6 million customers. To grow its business, it conducts regular market analysis. As part of this process, they analyzed seasonal trending keywords within Similarweb. Let’s look at what it found out and how it impacted the business.

Wonderbly was able to spot an emerging category (anniversaries and weddings) that was not currently catered for within its own product set. In addition to being able to capitalize on seasonal trends in its market, it achieved a 69% revenue in books purchased by a more mature demographic and a completely new audience for its business.

Read more: Wonderbly’s market analysis success story .

Step 4 – Competitive analysis

What: A competitive analysis involves collecting and reviewing data about key industry players, rivals, or emerging stars in your market. It unpacks and tracks their activities and successes, letting you see what’s working, how they go to market and the various marketing strategies they use to attract and retain customers.

Why: Regardless of your size or scope, understanding the competitive landscape is key. Your target audience knows your competitors and will likely size up the pros and cons of buying from thesm before considering whether to do business with you. A robust competitive analysis can help you refine your own offerings, make informed pricing decisions, show where you can beat out your rivals, and identify areas for improvement or diversification.

How: A tried and trusted tool for this process is the well-known SWOT analysis . It lets you map and view what and how each competitor takes its products to market. Considering things like pricing, positioning, marketing, services, and more. A competitive matrix is another tool used to visualize data about rivals in a market.

To do it, download our free competitive analysis framework . Then, pick five competitors in your market to track. Complete each section, and analyze the results to discover your biggest opportunities.

Step 5 – Develop strategies

What: Use the results of your market analysis to make data-driven decisions .

Why: When you read a post about how to do market analysis, the chances are you’ve got a goal in mind. Perhaps you want to explore a new market before deciding if it’s ripe for entry. You may want to introduce a new product or service or acquire an existing company. Whatever your goal is, ensure you put the insights and data you’ve obtained to good use.

How: Create a list of potential opportunities, then build strategies around each. Here, you might evaluate potential ideas based on project costs or timeframes. Once you’ve clearly mapped out each opportunity, and understand the potential impact it will have, along with associated costs and timeframes, you can think strategically about which ideas to move forward with from both a short and long-term perspective.

Pro Tip: Use a framework to record, capture, and review the data you’ve collected about market segmentation, size, trends, and key competitors. You can draw inspiration from our downloadable competitive analysis frameworks. However, what’s key is that you systematically record your findings and review them regularly.

Step 6 – Monitor the market

What: Keep track of your market and its key players; monitor changes over time.

Why: We know markets shift, whether they’re impacted by consumer behaviors, external factors, or something else. So, it’s important to monitor changes over time.

How: We may be a little biased, but Similarweb gives you a real-time bird-eye view of your market. Letting you dive into the details and unpick changes and tactics whenever you need. With it, you can track key growth metrics, marketing channels, emerging players, trending topics , and much more.

Using the Industry Analysis tab in Similarweb Research Intelligence , I can identify the market leaders and rising stars quickly. Here, I immediately see a company to track, Synchrony . As an emerging player showing exponential growth (2700%), I’ll take my market analysis a step further by investigating their successes later.

Similarweb shows me key insights, such as website traffic , the marketing channels it’s getting traffic from, audience demographics , geography , organic search insights, and more. As part of any good market analysis, the ability to spot rising players and unpack their successes can be crucial, particularly when they’re showing such growth.

Analyzing a market: Conclusions

Learning how to do market analysis is the first step. Aside from analyzing the results and making key strategic decisions, the ability to track changes over time is key. Similarweb makes it easy to segment, size, and analyze a market fast. With it, you can spot opportunities, benchmark your performance, and monitor shifts and changes as they happen, not a month or quarter later.

What are the 4 types of market analysis?

The four types of market analysis are market segmentation, market sizing, market trends, and competitive analysis.

What are the five components of market analysis?

The five components of market analysis are: customer segmentation , customer needs and trends, competitors, market size and trend, and pricing.

What makes a good market analysis?

A good market analysis should include accurate, up-to-date data, clear objectives, and a thorough market and customer needs analysis.

Is market analysis the same as a SWOT analysis?

No, market analysis and SWOT analysis are not the same. While a SWOT analysis evaluates an organization’s strengths, weaknesses, opportunities, and threats, a market analysis focuses on the external environment, such as customer needs, market trends, and competitors.

by Liz March

Digital Research Specialist

Liz March has 15 years of experience in content creation. She enjoys the outdoors, F1, and reading, and is pursuing a BSc in Environmental Science.

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Data Analytics in Marketing Research: Definition, Types, Process, and More

Data Analytics is a critical function affecting all aspects of the business. This article covers broad data analytic topics for those new to the area of data analytics. At Sawtooth Software, we focus on marketing research and primary data collection through survey research, so this article specifically calls out the use of data analytics in marketing sciences.

Before diving deep into the breadth of data analytics, let’s summarize key takeaways you will gain from this guide:

With that introduction, let’s dive deeper into the field of Data Analytics .

Table of Contents

What Is Data Analytics?

At its core, Data Analytics involves the computational analysis of data or statistics. Data can involve numeric values, text, graphics, video or audio files. The value of data analytics lies in its ability to transform vast amounts of raw, often unstructured data into actionable insights. These insights can then guide decision-making, optimize operations, and unveil opportunities for innovation.

Consider a retail business that leverages data analytics to understand customer purchasing patterns, preferences, and behaviors. By analyzing sales data, customer feedback, social media trends, along with primary survey data, the business can tailor its product offerings, improve customer service, predict future trends, and optimize products and pricing for new or existing products. This practical application underscores the transformative power of data analytics in driving business strategy and growth.

Data Analytics vs. Data Science

While often used interchangeably, Data Analytics and Data Science involve nuanced differences, with complementary roles within an organization. Data Analytics focuses on processing and performing statistical analysis on existing datasets. In contrast, Data Science typically involves heavier programming, developing algorithms, and model-building to derive additional insights to solve complex problems and predict future outcomes. Data scientists often leverage machine learning and AI (Artificial Intelligence) in building algorithms, models, and applications.

The impact of both fields on Decision-Making is important. Data analytics provides a more immediate, focused insight primarily aimed at enhancing operational efficiency and answering specific questions. Data Science, on the other hand, dives deeper into predictive analysis, machine learning, and AI to forecast future trends and behaviors.

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Types of Data Analysis

Data Analysis can be broadly categorized into four main types, each serving a unique purpose in the data analytics landscape. Understanding these types helps you to apply the right analytical approach to your data to derive meaningful conclusions and strategies.

Descriptive Analytics

This type of analytics focuses on the “what” and is the most basic and commonly used. For market research surveys, descriptive analytics summarizes responses to demographic, psychographic, attitudinal, brand usage data, and the like. For historical data, it aims to provide a clear picture of what has happened in the past by summarizing such things as sales data, operations data, advertising data, and website click traffic. Descriptive analytics answers the "What happened?" question by analyzing key performance indicators (KPIs) and metrics. For example, a business might use descriptive analytics to understand its sales trends, customer engagement levels, or production efficiencies over the past year.

Diagnostic Analytics

Moving beyond the “what” to understand the “why,” diagnostic analytics involves a deeper dive into data to examine patterns of association or correlation, with the hope to uncover root causes of attitudes, preference, events or trends. It employs techniques such as correlation analysis, t-tests, chi-square tests, key drivers analysis, and tree-based analysis (such as CART or random forests). For customer satisfaction research key drivers analysis tries to explain how overall customer satisfaction or loyalty can be improved by improving the features or elements of the product or service delivery. An organization might also leverage diagnostic analytics to identify why certain groups of respondents are more likely to be price sensitive or why customer churn increased in a specific period.

Predictive Analytics

This forward-looking analysis leverages data and models that can predict future outcomes. Conjoint analysis is a widely used predictive analytics approach for studying how changes to product features and prices affect demand. MaxDiff (best-worst scaling) is often used to assess which product claims will likely increase new product trial, or which side effects would most discourage patients from undergoing a cancer treatment therapy. Machine learning algorithms such as random forests can score a database to predict which customers are most likely to be receptive to an offer. As another example, a financial institution might use predictive analytics to assess the risk of loan default based on a customer's credit history, transaction data, and market conditions.

Prescriptive Analytics

An advanced form of analytics, prescriptive analytics, goes a step further by recommending actions you can take to affect desired outcomes. It not only predicts what will happen but also suggests various courses of action and the potential implications of each. This type of analytics is particularly valuable in complex decision-making environments. For example, a conjoint analysis market simulator leveraging optimization search routines can determine the right mix of product features and price to reach a particularly valuable market segment .

Each of these types of data analysis plays a critical role in an organization's data-driven decision-making process, enabling businesses to understand their past performance, diagnose issues, create successful products and services, predict future trends, and make informed choices that align with their strategic objectives.

Data Analytics Real-World Example

Consider the case of a data analyst working for an e-commerce platform. By analyzing customer purchase history, the analyst identifies a trend of increased sales in eco-friendly products ( descriptive analytics , the “what”). A survey is designed and conducted to dig deeper into which customers are preferring eco-friendly products, why they prefer them, and for which usage occasions ( diagnostic analytics , the “why”). Within another market research survey, a conjoint analysis or MaxDiff study is included for determining the right product claims, product features, and pricing, targeted to which market segments to develop new products for sales growth ( predictive and prescriptive analytics ).

The role of a data analyst is dynamic and impactful, bridging the gap between data and strategic decision-making. It's a role that requires not only technical skills but also curiosity, creativity, and a keen understanding of the business landscape.

The Data Analysis Process

Breaking down a data analytics process into systematic steps can demystify the journey, making it more approachable and manageable. The Data Analysis Process is a structured approach that guides data analysts from the initial phase of understanding the business problem to the final stage of delivering actionable insights.

Step 1: Defining the Question

The first and perhaps most critical step in the data analysis process is defining the question . This involves understanding the business objectives, the decisions that need to be supported by the data, and the specific questions that the analysis aims to answer. A well-defined question not only provides direction for the analysis but also ensures that the outcomes are relevant and actionable.

Step 2: Collecting Clean Data

Data collection is the next step, where data analysts gather the necessary data from various sources. This could include internal databases, secondary sources of data, customer surveys, and more. Ensuring the cleanliness of the data is paramount at this stage; hence, data cleaning and preprocessing become essential tasks. This involves removing inaccuracies, inconsistencies, handling missing values, and trimming outliers to ensure the data is reliable and accurate for analysis. For market research surveys, this also involves identifying unreliable respondents, fraudulent respondents, and records completed by survey bots.

Step 3: Data Analysis and Interpretation

With clean data in hand, analysts proceed to the heart of the process: data analysis and interpretation . This involves applying statistical methods and analytical models to the data to identify patterns, trends, and correlations. The choice of techniques varies depending on the data and the questions at hand, ranging from simple descriptive statistics to complex predictive models.

Step 4: Data Visualization and Sharing Findings

Data visualization plays a crucial role in this phase, as it transforms complex data sets into visual representations that are easier to understand and interpret. Tools like charts, graphs, and dashboards are used to illustrate the findings compellingly and intuitively.

Finally, sharing the findings with stakeholders is an integral part of the data analysis process. This involves not just presenting the data, but also providing insights, recommendations, and potential implications in a clear and persuasive manner. Effective communication is key here, as the ultimate goal is to inform decision-making and drive action based on the data insights.

For product optimization and pricing research, market simulators from conjoint analysis can be even more useful to a decision-maker than charts and graphs. They allow the manager to test thousands of potential product formulations and prices, to find the right products to best reach target market segments.

Example Scenario

Imagine a data analyst working for a healthcare provider, tasked with reducing patient wait times. By following the data analysis process, the analyst:

• Defines the question: What factors contribute to increased wait times?
• Collects and cleans data from patient records, appointment systems, and feedback surveys.
• Analyzes the data to identify patterns, such as peak times for appointments and common delays in the patient check-in process.
• Visualizes the findings using graphs that highlight peak congestion times and the factors causing delays.
• Shares the insights with the healthcare management team, recommending adjustments to appointment scheduling and check-in processes to reduce wait times.

This systematic approach not only provides actionable insights but also showcases the power of data analytics in solving real-world problems.

Understanding the data analysis process is foundational for anyone looking to delve into data analytics, providing a roadmap for transforming data into insights that can drive informed decision-making.

Tools and Techniques

The field of Data Analytics is supported by a variety of tools and techniques designed to extract, analyze, and interpret data. Market research surveys are often a key source of data. The choice of the right analytics tools and the application of specific analytical techniques can significantly impact the quality of the insights generated. In this section, we will explore some of the key data analytics techniques and highlight commonly used tools, especially for primary survey research, providing tips on how to choose the right ones for specific projects.

Key Data Analytics Techniques

Statistical Testing: When summarizing data using means (for continuous data) or percent of observations falling into different categories (for categorical or nominal data), we often want to know whether the differences we’re observing between groups of respondents, branches of a company, or time periods are statistically meaningful (that they were unlikely to occur by chance).

Correlation Analysis : A statistical approach that examines whether there is a positive, negative, or no correlation between two continuous variables. The square of the correlation coefficient indicates the percent of variance in one variable that is explained by the other.

Regression Analysis : A statistical method used to examine the relationship between dependent (outcome) and independent (predictor) variables. There are regression techniques for predicting continuous variables (ordinary least squares) as well as for categorical outcomes (logistic regression). Regression analysis is particularly useful for identifying relationships between variables, making predictions, and forecasting.

Tree-Based Analysis : These techniques are used for finding which variables tend to predict or explain some outcome, such as purchase of a product, or diagnosis with a disease. Common examples are Classification and Regression Trees (CART) and Random Forests, a combination of multiple trees that can be ensembled for a more accurate consensus prediction.

Time-Series Analysis : Focused on analyzing data points collected or recorded at specific time intervals. This technique is crucial for trend analysis, seasonal pattern identification, and forecasting.

Cluster Analysis : A family of methods used to group a set of objects (such as respondents) in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It’s extensively used in market segmentation and targeting strategies. Common approaches include k-means clustering, latent class clustering, and ensemble approaches that leverage multiple techniques to achieve a more robust consensus solution.

Conjoint Analysis and MaxDiff: Discrete choice methods often used in market research and economics for assessing the importance of features, measuring price sensitivity , and predicting demand for products or services.

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Commonly Used Data Analytics Tools

Excel : A versatile tool for basic data analysis, familiar to most professionals, capable of handling various data analysis functions including pivot tables, basic statistical functions, and data visualization.

SQL : Essential for data extraction, especially from relational databases. SQL allows analysts to query specific data from large databases efficiently.

Python/R : Both are powerful programming languages favored in data analytics for their libraries and packages that support data manipulation, statistical analysis, and machine learning.

Tableau/Power BI : These tools are leaders in data visualization, providing robust platforms for creating dynamic and interactive dashboards and reports.

Sawtooth Software : Provides tools, support services, and consulting services for designing and fielding market research surveys, as well as conducting conjoint analysis, MaxDiff, and cluster analysis.

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Choosing the Right Tools and Techniques

Selecting the appropriate tools and techniques depends on several factors:

Project Requirements : The nature of the data and the specific questions you are trying to answer will guide your choice. For instance, Python might be preferred for its machine learning capabilities, while Tableau is chosen for sophisticated visualizations.

Data Size and Complexity : Large datasets and complex analyses might require more advanced tools like Python or R, whereas Excel (limited to around 1 million rows and 16 thousand columns) could suffice for smaller, simpler datasets.

Skill Set : The proficiency of the data analyst in using these tools also plays a significant role. It’s essential to balance the choice of tool with the analyst's comfort level and expertise.

Budget and Resources : Some tools require significant investment, both in terms of licenses and training. Open-source options like Python and R offer powerful functionalities at no cost.

Example Application

Consider a retail company looking to optimize its inventory levels based on historical sales data. The data analyst might use:

• SQL to extract sales data from the company's database.
• Python for conducting time-series analysis to identify sales trends and predict future demand.
• Tableau to create visualizations that illustrate these trends and forecasts, facilitating strategic discussions on inventory management.

Through the strategic application of these tools and techniques, data analysts can uncover valuable insights that drive informed decision-making and strategic planning within organizations.

The exploration of tools and techniques underscores the versatility and power of data analytics. Whether through statistical analysis, predictive modeling, or insightful visualizations, these tools empower analysts to turn data into strategic assets.

Importance and Uses of Data Analytics

Data analytics has become a pivotal element of business strategy, influencing decisions across all levels of an organization. Its importance cannot be overstated, as it provides the insights needed for businesses to innovate, stay competitive, and improve operational efficiency. This section explores the significance of data analytics across various domains, including healthcare, product optimization and pricing, and its relevance for small enterprises and startups.

Embracing data analytics allows organizations to move from intuition-based decisions to informed strategies. As we advance, the integration of data analytics into every aspect of business operations and strategy will become more pronounced, highlighting its critical role in shaping the future of industries worldwide.

Transforming Business Success

Data analytics empowers businesses to make informed decisions by providing a deep understanding of customer behavior, market trends, and operational performance. It enables companies to:

• Optimize Operations : By analyzing data, businesses can identify inefficiencies in their operations and find ways to reduce costs and improve productivity.
• Enhance Customer Experience : Data analytics allows businesses to understand their customers' preferences and behaviors, leading to improved revenues, customer satisfaction and loyalty.
• Product Innovation/Optimization and Pricing : Survey research methods such as conjoint analysis and MaxDiff are especially useful for optimizing features for and pricing products/services, keeping companies at the forefront of innovation and competitiveness.

In healthcare, data analytics plays a critical role in improving patient outcomes and operational efficiency. By analyzing patient data, healthcare providers can:

• Predict Outbreaks : Data analytics can help in predicting disease outbreaks, enabling healthcare systems to prepare and respond effectively.
• Personalize Treatment : Analytics (including MaxDiff and conjoint analysis) can elicit real-time preferences from patients that can lead to better personalized treatment plans, improving patient care and outcomes. Several groups of physicians and academic researchers have presented research at Sawtooth Software conferences on using these tools for facilitating better communication between patients and doctors and selecting treatment plans for diseases such as cancer to result in improved outcomes.
• Improve Operational Efficiency : Data analytics can optimize hospital operations, reducing wait times and improving patient flow.

Product Optimization and Pricing

Repositioning existing products, developing new products, and setting effective pricing strategies are vital to most any business. By using gold standard tools for survey research such as conjoint analysis and MaxDiff, businesses can:

• Find Optimal Sets of Features : Conjoint analysis can within a single survey research project evaluate 1000s of potential feature configurations, determining which feature sets will compete best relative to specific competitors.
• Identify Profitable Target Segments : Conjoint analysis or MaxDiff are excellent techniques for identifying and sizing market segments that have specific needs and are associated with different levels of price sensitivity.
• Measure Price Elasticity: Choice-Based Conjoint (CBC) analysis is particularly valuable for estimating price elasticity of demand for the firm’s brand(s), as well as assessing how changes to competitor’s prices affect quantity demanded for the firm’s brand(s) ( cross-elasticity ).

Relevance for Small Enterprises and Startups

For small enterprises and startups, data analytics offers a competitive edge, enabling them to:

• Make Informed Decisions : Even with limited resources, small businesses can use data analytics to make strategic decisions based on market trends and customer feedback.
• Identify Opportunities : Analytics can reveal market gaps and customer needs, providing startups with insights to innovate and capture new markets.

The Role of a Data Analyst

In the heart of data-driven organizations lies the Data Analyst , a professional whose responsibilities are as varied as they are critical. Understanding the role of a data analyst not only highlights the importance of data analytics in modern business but also sheds light on the skills and perspectives needed to excel in this field.

Responsibilities and Tasks

A data analyst's journey often begins with problem formulation and developing hypotheses and strategies for solving a business or organizational problem. Next often follows data collection, ensuring the quality and accuracy of the data sourced from various channels, including survey research. This foundational step is critical, as the integrity of the data directly impacts the insights derived from it. The analyst then proceeds to clean and preprocess the data, preparing it for analysis. This involves handling missing values, removing duplicates, trimming outliers, and ensuring the data is in a format suitable for analysis.

The core of a data analyst's role involves statistical analysis and data modeling to interpret the data. They employ a range of techniques, from simple descriptive statistics to more complex predictive models, to unearth trends, patterns, and correlations within the data.

However, the role extends beyond just analyzing data. Data visualization and reporting are equally important, as these allow the analyst to communicate their findings in a clear, compelling manner. Whether through dashboards, reports, or presentations, the ability to present data in an accessible way is crucial for informing decision-making processes within an organization.

Professional Insights

From the perspective of a seasoned data analyst, the job is not just about numbers and algorithms; it's about solving challenging business and organizational problems and storytelling with data. It involves translating complex datasets into actionable insights that can drive strategy and impact. An effective data analyst combines analytical skills with business acumen, understanding the broader context in which the data exists.

Career Opportunities in Data Analytics

The field of data analytics offers a dynamic career landscape, characterized by a high demand for skilled professionals capable of turning data into actionable insights. As businesses across industries continue to recognize the value of data-driven decision-making, the demand for data analysts has surged, creating a wealth of opportunities for those equipped with the right skills and knowledge. This section will explore career prospects, including job growth, and discuss the relevance of degrees and certifications in data analytics.

Job Growth and Demand

The demand for data analysts is projected to grow significantly in the coming years. According to industry reports and labor statistics, the job market for data analysts is expected to grow much faster than the average for all occupations. This growth is driven by the increasing volume of data generated by businesses and the need to analyze this data to make informed decisions.

• Projected Job Growth : Data analytics roles are expected to see one of the highest rates of job growth across all sectors.
• Industries Hiring : While technology and finance traditionally lead in hiring data analysts, healthcare, marketing, and retail are rapidly catching up, reflecting the broad applicability of data analytics skills.

Salary Ranges

Salaries for data analysts can vary widely based on experience, location, and industry. However, data analysts typically command competitive salaries, reflecting the high demand and specialized skill set required for the role.

• Entry-Level Positions : Even at entry levels, data analysts can expect salaries that are competitive, with potential for rapid growth as experience and skills develop.
• Senior Roles : Experienced data analysts, especially those with specialized skills or leadership roles, can command significantly higher salaries.

Degrees and Certifications

While a degree in data science, statistics, computer science, or a related field can provide a strong foundation, the field of data analytics also values practical experience and specialized skills.

• Relevant Degrees : Bachelors and masters degrees in relevant fields are highly valued, but not always required.
• Certifications : Certifications can supplement academic degrees and provide evidence of specialized skills in data analytics tools and methodologies. Popular certifications include Certified Analytics Professional (CAP), Google Data Analytics Professional Certificate, and various platform-specific certifications (e.g., Tableau, SAS).

Making It in Data Analytics

Success in a data analytics career is not solely determined by technical skills. Employers also value problem-solving abilities, business acumen, and the capacity to communicate complex findings in a clear and actionable manner. Continuous learning and adaptation to new tools, technologies, and methodologies are essential in this rapidly evolving field.

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Data analytics is not just a tool but a strategic asset that can drive significant business value, enhance operational efficiency, and foster innovation across various sectors. From improving healthcare outcomes to enabling small businesses to compete more effectively, the applications of data analytics are vast and varied.

As we embrace the future, the importance of data analytics in driving business success and societal improvement will only continue to grow. For those considering a career in data analytics or looking to implement data-driven strategies in their operations, the potential is limitless. The benefits of data-driven decision-making underscore the transformative power of data analytics, making it an indispensable part of modern business and governance.

Whether you are a budding data analyst, a business leader, or simply curious about the potential of data analytics, the journey into this field is not only rewarding but essential for those looking to make an impact in the digital age. 

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How To Do Market Research: Definition, Types, Methods

Jul 25, 2024

11 min. read

Market research isn’t just collecting data. It’s a strategic tool that allows businesses to gain a competitive advantage while making the best use of their resources. Research reveals valuable insights into your target audience about their preferences, buying habits, and emerging demands — all of which help you unlock new opportunities to grow your business.

When done correctly, market research can minimize risks and losses, spur growth, and position you as a leader in your industry. 

Let’s explore the basic building blocks of market research and how to collect and use data to move your company forward:

Table of Contents

What Is Market Research?

Why is market research important, market analysis example, 5 types of market research, what are common market research questions, what are the limitations of market research, how to do market research, improving your market research with radarly.

Market Research Definition: The process of gathering, analyzing, and interpreting information about a market or audience.

Market research studies consumer behavior to better understand how they perceive products or services. These insights help businesses identify ways to grow their current offering, create new products or services, and improve brand trust and brand recognition .

You might also hear market research referred to as market analysis or consumer research .

Traditionally, market research has taken the form of focus groups, surveys, interviews, and even competitor analysis . But with modern analytics and research tools, businesses can now capture deeper insights from a wider variety of sources, including social media, online reviews, and customer interactions. These extra layers of intel can help companies gain a more comprehensive understanding of their audience.

With consumer preferences and markets evolving at breakneck speeds, businesses need a way to stay in touch with what people need and want. That’s why the importance of market research cannot be overstated.

Market research offers a proactive way to identify these trends and make adjustments to product development, marketing strategies , and overall operations. This proactive approach can help businesses stay ahead of the curve and remain agile as markets shift.

Market research examples abound — given the number of ways companies can get inside the minds of their customers, simply skimming through your business’s social media comments can be a form of market research.

A restaurant chain might use market research methods to learn more about consumers’ evolving dining habits. These insights might be used to offer new menu items, re-examine their pricing strategies, or even open new locations in different markets, for example.

A consumer electronics company might use market research for similar purposes. For instance, market research may reveal how consumers are using their smart devices so they can develop innovative features.

Market research can be applied to a wide range of use cases, including:

• Testing new product ideas
• Improve existing products
• Entering new markets
• Right-sizing their physical footprints
• Improving brand image and awareness
• Gaining insights into competitors via competitive intelligence

Ultimately, companies can lean on market research techniques to stay ahead of trends and competitors while improving the lives of their customers.

Market research methods take different forms, and you don’t have to limit yourself to just one. Let’s review the most common market research techniques and the insights they deliver.

1. Interviews

3. Focus Groups

4. Observations

5. AI-Driven Market Research

One-on-one interviews are one of the most common market research techniques. Beyond asking direct questions, skilled interviewers can uncover deeper motivations and emotions that drive purchasing decisions. Researchers can elicit more detailed and nuanced responses they might not receive via other methods, such as self-guided surveys.

Interviews also create the opportunity to build rapport with customers and prospects. Establishing a connection with interviewees can encourage them to open up and share their candid thoughts, which can enrich your findings. Researchers also have the opportunity to ask clarifying questions and dig deeper based on individual responses.

Market research surveys provide an easy entry into the consumer psyche. They’re cost-effective to produce and allow researchers to reach lots of people in a short time. They’re also user-friendly for consumers, which allows companies to capture more responses from more people.

Big data and data analytics are making traditional surveys more valuable. Researchers can apply these tools to elicit a deeper understanding from responses and uncover hidden patterns and correlations within survey data that were previously undetectable.

The ways in which surveys are conducted are also changing. With the rise of social media and other online channels, brands and consumers alike have more ways to engage with each other, lending to a continuous approach to market research surveys.

3. Focus groups

Focus groups are “group interviews” designed to gain collective insights. This interactive setting allows participants to express their thoughts and feelings openly, giving researchers richer insights beyond yes-or-no responses.

One of the key benefits of using focus groups is the opportunity for participants to interact with one another. They spark discussions while sharing diverse viewpoints. These sessions can uncover underlying motivations and attitudes that may not be easily expressed through other research methods.

Observing your customers “in the wild” might feel informal, but it can be one of the most revealing market research techniques of all. That’s because you might not always know the right questions to ask. By simply observing, you can surface insights you might not have known to look for otherwise.

This method also delivers raw, authentic, unfiltered data. There’s no room for bias and no potential for participants to accidentally skew the data. Researchers can also pick up on non-verbal cues and gestures that other research methods may fail to capture.

5. AI-driven market research

One of the newer methods of market research is the use of AI-driven market research tools to collect and analyze insights on your behalf. AI customer intelligence tools and consumer insights software like Meltwater Radarly take an always-on approach by going wherever your audience is and continuously predicting behaviors based on current behaviors.

By leveraging advanced algorithms, machine learning, and big data analysis , AI enables companies to uncover deep-seated patterns and correlations within large datasets that would be near impossible for human researchers to identify. This not only leads to more accurate and reliable findings but also allows businesses to make informed decisions with greater confidence.

Tip: Learn how to use Meltwater as a research tool , how Meltwater uses AI , and learn more about consumer insights and about consumer insights in the fashion industry .

No matter the market research methods you use, market research’s effectiveness lies in the questions you ask. These questions should be designed to elicit honest responses that will help you reach your goals.

Examples of common market research questions include:

Demographic market research questions

• What is your age range?
• What is your occupation?
• What is your household income level?
• What is your educational background?
• What is your gender?

Product or service usage market research questions

• How long have you been using [product/service]?
• How frequently do you use [product/service]?
• What do you like most about [product/service]?
• Have you experienced any problems using [product/service]?
• How could we improve [product/service]?
• Why did you choose [product/service] over a competitor’s [product/service]?

Brand perception market research questions

• How familiar are you with our brand?
• What words do you associate with our brand?
• How do you feel about our brand?
• What makes you trust our brand?
• What sets our brand apart from competitors?
• What would make you recommend our brand to others?

Buying behavior market research questions

• What do you look for in a [product/service]?
• What features in a [product/service] are important to you?
• How much time do you need to choose a [product/service]?
• How do you discover new products like [product/service]?
• Do you prefer to purchase [product/service] online or in-store?
• How do you research [product/service] before making a purchase?
• How often do you buy [product/service]?
• How important is pricing when buying [product/service]?
• What would make you switch to another brand of [product/service]?

Customer satisfaction market research questions

• How happy have you been with [product/service]?
• What would make you more satisfied with [product/service]?
• How likely are you to continue using [product/service]?

Bonus Tip: Compiling these questions into a market research template can streamline your efforts.

Market research can offer powerful insights, but it also has some limitations. One key limitation is the potential for bias. Researchers may unconsciously skew results based on their own preconceptions or desires, which can make your findings inaccurate.

• Depending on your market research methods, your findings may be outdated by the time you sit down to analyze and act on them. Some methods struggle to account for rapidly changing consumer preferences and behaviors.
• There’s also the risk of self-reported data (common in online surveys). Consumers might not always accurately convey their true feelings or intentions. They might provide answers they think researchers are looking for or misunderstand the question altogether.
• There’s also the potential to miss emerging or untapped markets . Researchers are digging deeper into what (or who) they already know. This means you might be leaving out a key part of the story without realizing it.

Still, the benefits of market research cannot be understated, especially when you supplement traditional market research methods with modern tools and technology.

Let’s put it all together and explore how to do market research step-by-step to help you leverage all its benefits.

Step 1: Define your objectives

You’ll get more from your market research when you hone in on a specific goal : What do you want to know, and how will this knowledge help your business?

This step will also help you define your target audience. You’ll need to ask the right people the right questions to collect the information you want. Understand the characteristics of the audience and what gives them authority to answer your questions.

Step 2: Select your market research methods

Choose one or more of the market research methods (interviews, surveys, focus groups, observations, and/or AI-driven tools) to fuel your research strategy.

Certain methods might work better than others for specific goals . For example, if you want basic feedback from customers about a product, a simple survey might suffice. If you want to hone in on serious pain points to develop a new product, a focus group or interview might work best.

You can also source secondary research ( complementary research ) via secondary research companies , such as industry reports or analyses from large market research firms. These can help you gather preliminary information and inform your approach.

Step 3: Develop your research tools

Prior to working with participants, you’ll need to craft your survey or interview questions, interview guides, and other tools. These tools will help you capture the right information , weed out non-qualifying participants, and keep your information organized.

You should also have a system for recording responses to ensure data accuracy and privacy. Test your processes before speaking with participants so you can spot and fix inefficiencies or errors.

Step 4: Conduct the market research

With a system in place, you can start looking for candidates to contribute to your market research. This might include distributing surveys to current customers or recruiting participants who fit a specific profile, for example.

Set a time frame for conducting your research. You might collect responses over the course of a few days, weeks, or even months. If you’re using AI tools to gather data, choose a data range for your data to focus on the most relevant information.

Step 5: Analyze and apply your findings

Review your findings while looking for trends and patterns. AI tools can come in handy in this phase by analyzing large amounts of data on your behalf.

Compile your findings into an easy-to-read report and highlight key takeaways and next steps. Reports aren’t useful unless the reader can understand and act on them.

Tip: Learn more about trend forecasting , trend detection , and trendspotting .

Meltwater’s Radarly consumer intelligence suite helps you reap the benefits of market research on an ongoing basis. Using a combination of AI, data science, and market research expertise, Radarly scans multiple global data sources to learn what people are talking about, the actions they’re taking, and how they’re feeling about specific brands.

Our tools are created by market research experts and designed to help researchers uncover what they want to know (and what they don’t know they want to know). Get data-driven insights at scale with information that’s always relevant, always accurate, and always tailored to your organization’s needs.

Learn more when you request a demo by filling out the form below:

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Get facts and insights on topics that matter, aug 18, 2024 | semiconductors, leading semiconductor companies worldwide 2024, by market cap.

As of August 18, 2024, Nvidia ranked as the leading semiconductor company in terms of market capitalization at 3.06 trillion U.S. dollars, followed by the likes of TSMC, Broadcom, Samsung, and ASML. This mix of companies reflects the broad and complex nature of the semiconductor industry, with firms featuring from across all parts of the chip ecosystem. 

The global semiconductor industry is dominated by companies from North America and the Asia-Pacific region. Countries such as China, Japan, South Korea, and Taiwan rank as some of the biggest markets for semiconductor equipment spending . One of the world’s leading chip-making machine manufacturers, and the only company in the world producing extreme ultraviolet lithography, or EUV, machines, is ASML based in Europe.

The European Union (EU) has set a goal for Europe to produce at least 20 percent of the world’s semiconductors by value by 2030. Semiconductor revenue in Europe is lower than in other regions, although Europe does have key strengths which could help it to achieve the ambitious targets. An example is in the production of chips for the automotive industry , with firms such as NXP Semiconductors (Netherlands) and Infineon (Germany) specializing in this field.

Aug 25, 2024 | Video Gaming & eSports

Black myth: wukong cumulative units sold worldwide 2024.

Released on August 19, 2024, and selling 10 million copies within three days, Black Myth: Wukong is one of the fastest-selling video games of all time. Heralded as the Chinese video game industry's first AAA video game production, the title was released on PC and PS5 with an Xbox version following shortly. Black Myth: Wukong was published by Game Science, which is backed by Tencent Gaming, one of the biggest gaming companies in China and worldwide.

After first teasing the game in 2020, the anticipation for Black Myth: Wukong was extremely high. The title made headlines when Game Science revealed that it would transition the game’s development to the newly released game engine Unreal 5, making it one of the first major titles to do so. This hype caused the game to top charts as soon as pre-orders became available in summer 2024. Apart from selling an unprecedented number of copies within just a few days, Black Myth: Wukong crushed the competition in terms of user engagement, setting records as the  second-most played video games on Steam based on peak concurrent players . A week post release, Wukong claimed over 2.4 million peak concurrent users on PC, ranking only behind PUBG, which is even more impressive considering that PUBG is a free-to-play online multiplayer title. 

Most Black Myth: Wukong’s sales are domestic, based on the fact that the majority of the over half a million reviews on Steam are in Chinese. Additionally, Chinese gamers accounted for about a third of Steam audiences , with nearly 32 percent of Steam users having Simplified Chinese and another 1.2 percent using Traditional Chinese as the language for the PC gaming platform. 

Despite being the biggest video gaming market worldwide based on revenue , the Chinese gaming industry has been hampered by a gaming console ban (which has now been lifted), as well as strict regulations regarding video game releases and gaming consumption among citizens. Some of these strict regulations have caused raised eyebrows during the release of Black Myth: Wukong, as gaming content creators and streamers revealed they had been sent a list of topics to avoid talking about while livestreaming the game.

Despite these growing pains, the Chinese gaming industry has been gaining international traction in recent years. Limited to mobile or domestic releases for long, China is now known for leading the way in free-to-play action role-playing titles like Genshin Impact , which gave rise to an entire genre of anime-style open-world online games.

There are several factors that play into the popularity of Black Myth: Wukong. One of the key factors of the action game’s success is the source material – the main character of Black Myth: Wukong is based on Sun Wukong, or the Monkey King, a key character in Journey to the West, a beloved piece of classic Chinese literature which has inspired hundreds of interpretations on TV shows, cartoons, movies, and other media adaptions. The cultural relevance of the topic can be regarded as a soft-power move to promote Chinese culture through media content, which is currently booming internationally as Chinese TV drama productions are also gaining more international popularity among Western audiences. 

Aug 12, 2024 | Diseases

Mpox cases and deaths worldwide 2022-2024.

As of June 2024, there had been almost 100 thousand cases of mpox (formerly known as monkeypox) confirmed. The virus has been recorded in over 100 countries and territories, and was re-named as a public health emergency of international concern by the WHO in August 2024.

Aug 15, 2024 | Key Figures of E-Commerce

Alibaba's quarterly consolidated revenue q2 2018-q2 2024.

In the second quarter of 2024, Alibaba's total revenue approached 243.24 billion yuan. The company's business segments include core commerce, cloud computing, digital media entertainment, innovation initiatives and others.

Aug 15, 2024 | Theme Parks & Water Parks

Disneyland theme park (california) attendance 2009-2023.

In 2023, the Disneyland theme park in Anaheim, California welcomed more than 17 million visitors in total. This shows an increase of 2.2 percent compared to the previous year. The number of visitors to the park peaked in 2019.

Aug 22, 2024 | Education Level & Skills

Proportion of gcse entries that achieved a pass grade in the uk 1988-2024.

In Summer 2024 GCSE students in the United Kingdom had a pass rate (achieving a grade of C/4 or higher) of 67.6 percent, the lowest since 2019 but still a noticeable increase when compared with years before 2020. The COVID-19pandemic, and closure of schools in the UK led to exams throughout the country being cancelled, with grades in 2020 and 2021 based on assessment by teachers and schools.

Aug 13, 2024 | Food & Beverage

Most popular american dishes in the u.s. q2 2024.

As of the second quarter of 2024, hamburgers, french fries, and grilled cheese sandwiches were the most popular American dishes in the United States. An average of around 84 percent of respondents had a positive opinion of each of the three dishes. 

Aug 14, 2024 | North Korea

Gdp comparison between south and north korea 2004-2023.

In 2023, South Korea's nominal gross domestic product (GDP) reached approximately 2,401 trillion South Korean won, while North Korea's amounted to about 40.2 trillion South Korean won. Consequently, South Korea's nominal GDP was approximately 60 times larger than that of North Korea during that year. Moreover, North Korea's GDP growth has been notably minimal when compared to that of South Korea. North Korea's economic development

North Korea's economy is centered around its capital city and military, with particular emphasis on the expansion of its nuclear capabilities in recent decades. Roughly 98 percent of foreign trade has been with China in the past decade, from whom North Korea imports large volume of mechanical and electronic goods. Food shortages , exacerbated by the Covid-19 pandemic, are a reoccurring issue for North Korea, as poor harvests, international sanctions, and a downturn in inter-Korean trade have created sourcing problems - the full extent of this issue remains unknown, but it is estimated that almost half the population is undernourished.

Kaesong Industrial Complex The Kaesong Industrial Complex project began in 2000 and was a crucial part of South Korea's efforts to improve relations with North Korea. It aimed to foster cooperation between the two Koreas and promote stability in the region. The industrial park, located in Kaesong, North Korea, was intended to provide a platform for small and medium-sized South Korean companies. South Korea would provide the necessary capital and infrastructure, while North Korean workers would be tasked with manufacturing products, aiming to stimulate economic growth on both sides of the border. Unfortunately, the complex was affected by tensions between the two Koreas and shut down in 2016. It has not been reopened since.

Aug 19, 2024 | Music

Market share of record labels in sweden august 2024, based on single/album charts.

From August 12 to 18 in 2024, Universal Music recorded the highest market share among Swedish record labels, at over 43 percent for album charts. Sony Music came second, with a market share of nearly 21 percent. Warner Music ranked third. The Swedish record label Playground Music Scand had the lowest market shares during this period. 

Based on the total sales of songs released in Sweden in 2019, Sony, Universal Music, and Warner Music also ranked highest. Here, Sony/ATV was the largest Swedish music publisher that year, reaching a market share of nearly 24 percent. Universal Music Publishing was the second biggest music company in the country.

The value of the recorded music industry in Sweden increased in recent years. As of 2019, companies generated revenues of over 1.5 billion Swedish kronor. 10 years earlier, it was about half as high. Music streaming services by far accounted for the largest share of music sales in 2019, growing by over one billion Swedish kronor in the period from 2009 to 2019.

Aug 16, 2024 | Demographics

Largest cities in nigeria 2024.

Nigeria is the African country with the largest population, counting over 230 million people. As of 2024, the largest city in Nigeria was Lagos, which is also the largest city in sub-Saharan Africa in terms of population size. The city counts more than nine million inhabitants, whereas Kano, the second most populous city, registers around 3.6 million inhabitants. Lagos is the main financial, cultural, and educational center in the country. 

The metropolitan area of Lagos is also among the largest urban agglomerations in the world. Besides Lagos, another most populated citiy in Africa is Cairo, in Egypt. However, Africa’s urban population is booming in other relatively smaller cities. For instance, the population of Bujumbura, in Burundi, could grow by 123 percent between 2020 and 2035, making it the fastest growing city in Africa and likely in the world. Similarly, Zinder, in Niger, could reach over one million inhabitants by 2035, the second fastest growing city.

More than half of the world’s population lives in urban areas . In the next decades, this will increase, especially in Africa and Asia. In 2020, over 80 percent of the population in Northern America was living in urban areas, the highest share in the world. In Africa, the degree of urbanization was about 40 percent, the lowest among all continents. Meeting the needs of a fast-growing population can be a challenge, especially in low-income countries. Therefore, there will be a growing necessity to implement policies to sustainably improve people’s lives in rural and urban areas.

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What Is Market Research? How To Do It Right Every Time

In this post

Importance of market research

Types of market research, market research process, how to conduct market research, benefits of market research, challenges of market research, market research vs. marketing research.

Do your users know your business’ product or service like the back of their hand?

You work with it every day and know it inside and out: all of its features, how it benefits users, and where it falls short. Yes, you know your product like nothing else, but what about your users? What do you know about the people buying your product? That’s where market research comes in. It helps businesses understand a target market’s ins and outs, present and potential customers’ needs and spending habits, competitors, and the industry. Market research services providers perform in-depth research to help businesses identify potential markets, check product viability, and ensure optimal placement.

What is market research?

Market research is a systematic process to collect, analyze, and interpret qualitative and quantitative data about potential customers, existing users, competitors, and the target market. Businesses use market research results to create products, experiences, and messages to attract and maintain a solid customer base. For example, companies use market research to gauge customer sentiment, gain competitor insights, fix brand perception, create marketing strategies, to name a few.

Every successful business has one thing in common; they get their target customers right. Market research helps them get a clear picture of their potential customers’ values, goals, aspirations, and needs. An effective market research strategy is crucial to understanding the business landscape, connecting with customers, planning marketing campaigns , and beating the competition. Organizations conduct market research in-house or hire a third-party company specializing in market research services. Companies can leverage market research findings in the following ways.

• Identify opportunities to improve average order value (AOV), form business partnerships, and design targeted campaigns.
• Investigate customers’ buying behaviors to test new products, gain actionable insights into customer churn rates , and find potential product glitches.
• Create promotional strategies to create brand awareness , boost brand perception, and buy ads.
• Gather competitive intelligence to address unfulfilled customer needs, find underserved market segments , and mitigate external business environment challenges.
• Make informed business decisions by addressing potential problems and strategizing operational alternatives.

Functions of market research

• Describes target customers’ preferences, competitors, and the overall market situation
• Evaluates product positioning and marketing in comparison to competitors
• Explains reasons behind existing business problems and potential fixes
• Predicts future market opportunities and consumer behavior for creating suitable plans and policies
• Aids in choosing the correct course of action based on the newly found merits and demerits of product, price, promotion, and place (4Ps)

Whether you’re researching to understand a market or draw conclusions on squirrel migration patterns, there are two kinds you can conduct: primary and secondary.

Primary research

Primary research is research you conduct yourself. It includes going directly to a source to gather original data. Researchers use primary research methods to discover meaningful first-hand data about a specific issue or problem. The result of primary research is either exploratory (when there isn’t a clear problem definition) or conclusive (solves a problem from the exploratory research) information. Different types of primary market research are as follows.

• Focus group discussions (FGDs) collect data from a small group of participants or subject matter experts representing a target market. Researchers usually select these participants based on demographics or characteristics. An interviewer facilitates the discussion and engages in conversation to find insights about a product, market, or service.
• Telephonic or face-to-face interviews are two-way conversations between participants and researchers. Interviewers rely on a series of open-ended questions to gain insights into respondents’ opinions and perceptions. The length of these in-depth interviews depends on the complexity of the subject.
• Surveys use open- and close-ended questionnaires to collect responses from a larger population. Online surveys have become a cost-efficient way to conduct primary research across different geographies. Researchers analyze survey research responses to draw inferences about respondents’ opinions and preferences.
• Observations don’t involve direct interaction between respondents and researchers. Trained observers record subjects’ reactions and make organized notes to study further and analyze user behavior.
• Field tests assess how the product or service will function in the real-life environment. These tests are risky but show how people react to your product when other viable options are available. Field test results enable you to adjust factors that might motivate users to take their business elsewhere, like price or packaging.

Secondary research

Secondary research gathers and analyzes information from previous research analyses and publications. In this case, a researcher uses research reports of another researcher as their source of data instead of doing the research themselves. Different types of secondary research are as follows.

• Internal research discovers data available within your organization. Internal data include website, database, user-generated content , and previous research findings and campaign results.
• External research fills the knowledge gap with data from public libraries, newspapers, government agencies, competitive analyses , and journals.

Primary and secondary research methods use qualitative and quantitative data to classify research findings and conclude.

Qualitative research

Qualitative research focuses on the ‘why’ rather than ‘what’ of any behavior, action, or event. It collects and analyzes non-numerical primary and secondary data to generate new research ideas or offer in-depth insights into a problem. Qualitative research relies on observations, interviews, surveys, and FGDs to collect and interpret data. The most common qualitative research approaches are as follows.

• Grounded theory collects rich data on a topic of interest to develop theoretical approaches from observations.
• Ethnography seeks to understand a group or community’s shared culture, beliefs, and conventions.
• Action research connects research and action to drive transformative changes.
• Phenomenological research interprets participants’ lived experiences to investigate an event.
• Narrative research relies on storytelling to understand participants’ perceptions of an event.

Quantitative research

Quantitative research analyzes numerical data to discover patterns, make predictions, and generalize results. Researchers use quantitative techniques to find historical benchmarks for their study. Quantitative methods include surveys, interviews, and experiments. Market researchers use quantitative and qualitative methods to derive insights from data and make decisions. Organizations leverage these techniques to execute different kinds of marketing research.

• Product/service use research discovers what and why of product or service usability for your target audience.
• Buyer persona research uncovers your target market size, challenges, characteristics, aspirations, and motivations.
• Market segmentation research categorizes your target audience groups into segments based on their unique pain points, goals, and needs.
• Pricing research studies competitors’ price points and helps you set a fair pricing point.
• Competitive analysis evaluates competitors’ strengths and weaknesses regarding their products, sales, and marketing.
• Brand awareness research helps a brand uncover its market position and improve brand preference.
• Campaign research dives deep into previous campaigns to discover what went well.
• Brand association research studies the desirability of a brand and boosts positioning accordingly.
• Demand estimation analysis measures customer demand for a product or service.
• Marketing effectiveness assesses the potency of a go-to-market strategy in maximizing revenues.
• Mystery shopping uses independent auditors as customers to assess the sales and service quality.
• Sales forecasting studies potential unit sales to predict future revenue.
• Trendspotting identifies local, global, or regional trends before they become mainstream.
• Feasibility studies predict the success or failure of business concepts, product development, service launches, or business expansion plans.

Systematic inquiry is at the heart of any market research exercise. The process may vary depending on deadlines, budgets, resources, methodologies yet generally follow the sequential stages below.

• Define the problem or opportunity. A problem half-defined remains half-solved. That’s why researchers start with problem diagnosis and definition. A clearly defined research problem aids researchers in establishing accurate research objectives and collecting relevant data.
• Create a research objective statement to develop a hypothesis or investigate a set of research questions. Market researchers create a hypothesis only when they have sufficient empirical evidence to support a claim.
• Choose a research design to specify your plan to collect and analyze data. Think of this research design as a framework to identify information sources, data collection methods, sampling methodology, and research costs.
• Develop a sampling plan to understand the characteristics of a population and make a conclusion. Researchers define the sample population, population size, and choose a sampling method at this stage. Two commonly used sampling methods are as follows.

Types of sampling

• Probabilistic sampling selects samples from a population using the theory of probability. This sampling method is ideal for quantitative research and finding sampling errors after data collection.
• Non-probability sampling relies on a researcher’s subjective judgment to select data. This method offers an equal chance for all respondents to participate in the research and is ideal for qualitative research.
• Collect primary or secondary data depending on the research design or plan. Researchers use various data collection methods to find information on variables. This data further helps them to study and analyze the research problem.
• Process and analyze collected data. Data editing and coding help researchers to identify and remove data classification inconsistencies. Coding involves setting rules for data categorization and transfer. The analysis part attempts to discover similar data patterns and their logic.
• Prepare and present a conclusion. The final stage of the market research process interprets data analysis and presents findings through a report. Such reports ease executive decision-making with concrete data-backed evidence.

When to use market research?

Startups and prominent businesses use market research to understand their customers, measure business profitability, analyze the target market, and beat the competition. Companies conduct market research activities to:

• Evaluate product demand
• Test new products or services
• Assess business expansion opportunity
• Understand customer satisfaction levels
• Develop customer persona and test messaging
• Estimate overall business growth opportunities
• Get an inside look into customer experience (CX)
• Gauge product packaging and promotional decisions

The purpose of market research is to understand your customers to develop a one-of-a-kind product or service that fulfills their wants and needs. Researching the target market might require some extra digging, but following the steps below is an excellent place to start.

1. Identify your target audience(s)

The first step is to identify your target audience. Who do you think will benefit most from your product? Segment your target market into small groups based on location, demographics, personality traits, and buying behaviors. Because you are segmenting your audience on more than one quality, you’ll end up with more than one group to target, which is perfectly fine. You can create a persona from these audiences or develop a made-up person that might show interest in your business. This customer persona helps you hone in on the pains you’ll be relieving and gains you’ll be offering with your business.

2. Scope the competition

Researching your competition is arguably the most effective way to understand your market. Observing others in the same line of work helps you create a better marketing mix and overpower competitors. A business’s marketing mix includes four parts:

• Product: What is your competition selling? What are the benefits of the product? How can you make it better?
• Price: At what price are they offering their product? Are people willing to spend that much? Can you charge more for a luxury item or less for a cheaper version?
• Place: How is your competition reaching its customers? Is this channel effective? Or can you find a better way to connect with them?
• Promotion: How is your competition promoting its product? Where are they spending money on marketing wisely, and where are they wasting it?

3. Engage a portion of your persona

Once you have an idea of competitors, you'll want to focus on your audience. During this stage, you will conduct primary research to determine what your audience looks for in a product, why they find it valuable, and how it will benefit them. What's working for your product? What should you change? Should you scrap your idea and start over? Directly engaging with customers in this step is your best bet. Compile focus groups, send surveys, or use another market research method. We will go into more detail on those later. Your first goal will be to gather some demographic information. Ask customers their age, gender, income range, profession, education, and city of residence. Once you have that, you can ask more about their hobbies and interests and the associated goals and challenges they face daily. Then it's time to get down to business. Ask your audience about buying preferences, where they make purchases, and how often they buy a specific product. Have them go into detail about why they buy a particular product and ignore others.

4. Summarize data and draw conclusions

At this point, you’ve gathered a lot of data that’ll help you better understand the market. The last step is to organize your data and conduct customer data analysis to conclude your target audience so you can provide them with the best product, service, and messaging.

No matter what you do market research for, you’ll discover insights into your business and customers at the end. The research findings help you make informed decisions and unlock business growth. Let’s look at how companies benefit from market research activities.

• Stay ahead of the competition with a better understanding and perspective of your target market and audience.
• Minimize investment risks by evaluating new products, features, or services before launching them.
• Identify threats and opportunities to modify existing business plans, models, investments, or strategies.
• Spot emerging trends by taking a pulse of what’s hot and not in the market.
• Find business growth benchmarks to compare business revenue, productivity, and growth.

Market research challenges stem from Complex business dynamics, automation, and consumer data protection. Let’s take a sneak peek at how these challenges restrict the way researchers collect data and contribute to decision-making.

• The need for speed. Opinions change every day, and so do products. That’s why businesses can’t any longer wait for a month or more to get their hands on market research results. This need for agility pushes market research agencies to deliver quick turnaround times.
• Tight budget and automation are complicating how researchers design research and offer insights. The presence of multiple tools doesn’t necessarily streamline analysis. They instead add to training, ongoing support, and maintenance costs. That’s why you need a comprehensive platform so that you can do more with less.
• The human dimension is becoming more important in research. Today, organizations have more data available than ever and can make wrong interpretations and incorrect decisions. Market researchers’ expertise and skill are essential for companies to find real-time insights from unstructured data.
• Lack of focus on data curation is another challenge that organizations face. Analytics and data visualization software can produce many insights that may not offer concise answers to the problems at hand. That’s where data curation comes in to help businesses find the most relevant solutions for making better decisions.
• Consumer data protection is of utmost importance to any organization. This increasing need to maintain data privacy means organizations should look for credible and verifiable market research agencies that have robust data privacy protection policies in place.

Market research is the process of collecting, analyzing, and interpreting data about the viability of a product or service in a target market. Market researchers aim to understand the buying habits, behaviors, motivations of consumers to predict a potential target segment for a product or service. Marketing research goes behind finding a suitable market to sell your products. It investigates an organization’s marketing strategy. Organizations conduct marketing research to identify issues and gaps in existing marketing activities. Want to identify a market opportunity? Learn how to assess market opportunities to find their desirability.

Mary Clare Novak

Mary Clare Novak is a Content Marketing Specialist at G2 based in Burlington, Vermont, where she is currently exploring topics related to sales and customer relationship management. In her free time, you can find her doing a crossword puzzle, listening to cover bands, or eating fish tacos. (she/her/hers)

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Market Research 101: Data Analysis

An Overview of Market Research in Data Analysis

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Data analysis in a market research project is the stage when qualitative data, quantitative data, or a mixture of both, is brought together and scrutinized in order to draw conclusions based on the data.

6 Market Research Steps

Step 1 - Articulate the research problem and objectives: Market research begins with a definition of the problem to be solved or the question to be answered. Typically, there are several alternative approaches that can be used to conduct the market research.

Step 2 - Develop the overall research plan : The task of this stage is to determine the most efficacious way to collect the necessary information.  

Step 3 – Collect the data or information:  At this point, you have to consider how you're going to obtain the information (meaning, how participants are going to be contacted whether it's surveys, phone calls, one-on-one interviews, etc.).

Step 4 – Analyze the data or information: Collecting volumes of information can be overwhelming. At this stage, you need to organize the data and weed out what is not crucial.

Step 5 – Present or disseminate the findings : From knowing your audience to knowing what findings are actionable, before releasing your findings, you need to understand which findings you want to disseminate.

Step 6 – Use the findings to make the decision: Because external consumers of market research may not use the findings accurately, appropriately, or completely, you need to consider the attributes of good market research.

Quantitative Market Research Decision Support Tool

The following statistical methods will help you get from A to Z in the research process.

Multiple Regression - This statistical procedure is used to estimate the equation with the best fit for explaining how the value of dependent variable changes as the values of a number of independent variables shifts. A simple market research example is the estimation of the best fit for advertising by looking at how sales revenue (the dependent variable) changes in relation to expenditures on advertising, placement of ads, and timing of ads.

Discriminant Analysis - This statistical technique is used for the classification of people, products, or other tangibles into two or more categories. Market research can make use of discriminant analyses in a number of ways. One simple example is to distinguish what advertising channels are most effective for different types of products.

Factor Analysis - This statistical method is used to determine which are the strongest underlying dimensions of a larger set of variables that are inter-correlated. In a situation where many variables are correlated, factor analysis identifies which relations are strongest. A market researcher who wants to know what combination of variables (or factors) are most appealing to a particular type of consumer, can use factor analysis to reduce the data down to just a few variables.

Cluster Analysis - This statistical procedure is used to separate objects into specific groups that are mutually exclusive but also relatively homogeneous in a constitution. This process is similar to what occurs in market segmentation when the market researcher is interested in the similarities that facilitate grouping consumers into segments and also interested in the attributes that make the market segments distinct.

Conjoint Analysis - This statistical method is used to unpack the preferences of consumers with regard to different marketing offers. Two dimensions are of interest to the market researcher in conjoint analysis, the inferred utility functions of each attribute, and the relative importance of the preferred attributes to the consumers.

Multidimensional Scaling - This category represents a constellation of techniques used to produce perceptual maps of competing brands or products. For instance, in multidimensional scaling, brands are shown in a space of attributes in which the distance between the brands represents dissimilarity. An example of multidimensional scaling in market research would show the manufacturers of single-serving coffee in the form of K-cups. The different K-cup brands would be arrayed in the multidimensional space by attributes such as the strength of roast, number of flavored and specialty versions, distribution channels, and packaging options.

• Introduction to Data Collection in Market Research
• What Is Market Research?
• Preparing Data for Analysis and Triangulation
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• What Is Sentiment Analysis?
• How to Use a Codebook for Qualitative Research Analysis
• How to Analyze Small Business Qualitative Market Research
• How to Conduct Competitive Intelligence
• How to Analyze Interview Data and Survey Responses
• Advantages and Disadvantages of Quantitative Research
• Qualitative or Quantitative? How to Choose a Method for Your Survey
• How Market Researchers Choose Which Movies You See
• How to Share Market Research Findings With Research Presentations
• Learn About Syndicated Market Research

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How to Conduct Market Research for a Startup

• 17 Mar 2022

With every innovative product idea comes the pressing question: “Will people want to buy it?”

As an entrepreneur with a big idea, what’s the best way to determine how potential customers will react to your product? Conducting market research can provide the data needed to decide whether your product fits your target market.

Before launching a new venture, you should understand market research. Here’s how to conduct market research for a startup and why it’s important.

Access your free e-book today.

What Is Market Research?

Market research is the process of gathering information about customers and the market as a whole to determine a product or service’s viability. Market research includes interviews, surveys, focus groups, and industry data analyses.

The goal of market research is to better understand potential customers, how well your product or service fits their needs, and how it compares to competitors’ offerings.

There are two types of research you can conduct: primary and secondary.

• Primary research requires collecting data to learn about your specific customers or target market segment. It’s useful for creating buyer personas, segmenting your market, and improving your product to cater to customers’ needs .
• Secondary research is conducted using data you didn’t collect yourself. Industry reports, public databases, and other companies’ proprietary data can be used to gain insights into your target market segment and industry.

Why Is Market Research Important for Entrepreneurs?

Before launching your venture, it’s wise to conduct market research to ensure your product or service will be well received. Feedback from people who fall into your target demographics can be invaluable as you iterate on and improve your product.

Performing market research can also help you determine a pricing strategy by gauging customers’ willingness to pay for your product. Additionally, it can improve the user experience by revealing what features matter most to potential customers.

When assessing which startups to fund, investors place heavy importance on thorough market research that indicates promising potential. Providing tangible proof that your product fulfills a market need and demonstrating you’ve taken the time to iterate on and improve it signal that your startup could be a worthwhile investment.

Related: How to Talk to Potential Investors: 5 Tips

How to Do Market Research for a Startup

1. form hypotheses.

What questions do you aim to answer through market research? Using those questions, you can make predictions called hypotheses . Defining your hypotheses upfront can help guide your approach to selecting subjects, researching questions, and testing designs.

An example question you may ask is: “How much are people in my target demographic willing to pay for the current version of my product?” Your hypothesis could be: “If my product contains all its current features, customers will be willing to pay $500 for it.”

Another example question you may ask is: “What’s the user’s biggest pain point, and is my product meeting their needs?” Your hypothesis could be: “I believe the user’s biggest pain point is needing an easy, unintimidating way to learn basic car maintenance, and I predict that my product meets that need.”

You can and should test multiple hypotheses, but try to select no more than a few per test, so the research stays focused.

Related: A Beginner’s Guide to Hypothesis Testing in Business

2. Select the Type of Research Needed to Test Hypotheses

Once you’ve formed your hypotheses, determine which type of research to conduct.

If your hypotheses focus on determining your startup’s place in the broader market, start with secondary research. This can include using existing data to determine market size, how much of that market your startup could reasonably own, who your biggest competitors are, and how your brand and product compare to theirs.

If your hypotheses require primary research, decide which data collection method best fits your needs. These can include one-on-one interviews, surveys, focus groups, and polls. Primary research allows you to gather insights into customer satisfaction and loyalty, brand awareness and perception, and real-time product usability.

3. Identify Target Demographics and Recruit Subjects

To gather meaningful insights, you need to understand your target demographic. Do you aim to cater to working parents, young athletes, or pet owners? Determine the type of person who can benefit from your product.

If you conduct primary research, you need to recruit subjects. This can be done in several ways, including:

• Word of mouth: The simplest but least reliable way to recruit participants is by word of mouth. Ask people you know to refer others to be research subjects, then screen them to confirm they fit your target demographic.
• Promoting the study on social media: Many social media platforms enable you to show an ad to people who fall into specific demographic categories or have certain interests. This allows you to get the word out to a large number of people who qualify.
• Hiring a third-party market research company: Some companies provide full market research services and recruit participants and conduct research on your behalf.

However you recruit subjects, ensure they take a screener survey beforehand, which allows you to determine whether they fit the specific demographic you want to study or have a trait that eliminates them from the research pool. It also provides demographic data—such as age and race—that enables you to select a diverse subset of your target demographic.

In addition, you can offer compensation to boost participation, such as money, meal vouchers, gift cards, or early access to your product. Make it clear that compensation is in appreciation for subjects’ time and honest feedback.

4. Conduct the Research

Once you’ve determined the type of research and target demographic necessary to test your hypotheses, conduct your research. To reduce bias, enlist someone unfamiliar with your hypotheses to perform interviews or lead focus groups.

Ask questions based on your audience and hypotheses. For instance, if you’re aiming to test existing customers’ purchase motivations, you may ask: “What challenge were you trying to solve when you first bought the product?”

If examining brand perception, your audience should consist of potential customers who don’t yet know your brand. Present them with a list of competitor logos—with yours in the mix—and ask them to rank the brands by perceived reliability.

While the questions you ask are vehicles to prove or disprove hypotheses, ensure they don’t lead subjects in one direction. To craft unbiased research questions , use neutral language and vary the order of options in multiple-choice questions. This can keep subjects from selecting the same option each time if they sense the third option is always mapped to a certain outcome. It also helps account for primacy bias (the tendency to select the first option in a list) and recency bias (the tendency to select the final option in a list).

Once you’ve collected data, ensure it’s organized efficiently and securely so you can protect subjects’ identities .

Related: 3 Examples of Bad Survey Questions and How to Fix Them

5. Gather Insights and Determine Action Items

After you’ve organized your data, analyze it to extract actionable insights. While some of the data will be qualitative rather than quantitative, you can detect patterns in responses to make it quantifiable. For instance, noting that 15 of 20 subjects mentioned feeling overwhelmed when attempting to assemble your product.

Once you’ve analyzed the data and communicated emerging trends using data visualizations , outline action items.

If the majority of users in your target demographic reported feeling overwhelmed while assembling your product, action items might include:

• Creating different versions of assembly instructions to test with other groups, varying diagrams and instructional language
• Researching instruction manual best practices

Each round of market research can offer more information about how your product is perceived and experienced by potential users.

Market Research as an Ongoing Endeavor

While it’s useful to conduct market research before launching your product, you should revisit your hypotheses and form new ones over the course of building your venture.

By conducting market research with each version of your product, you can gradually improve it and ensure it continues to fit target customers’ needs.

Are you interested in bolstering your entrepreneurship skills? Explore our four-week online course Entrepreneurship Essentials and our other entrepreneurship and innovation courses to learn to speak the language of the startup world.

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Home Market Research

Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Wilson College Online Blog

What does a market research analyst do.

Market research is a crucial aspect of modern business that helps companies understand their customers and potential customers. Professionals such as market research analysts use data gathered through surveys, focus groups, and customer purchasing histories to gain insights into a company’s target audience and how to cater to that audience’s needs. 

Market research analysts use their understanding of the customer mindset and data analysis to help companies reach their target audience. Their work is vital to the success of any product or service.

Anyone interested in a career that blends psychology and marketing should consider learning more about what a market research analyst does and how earning a bachelor’s degree can help aspiring analysts reach their career goals.

Market Research Analyst Job Description

Market research analysts help companies uncover trends in the market so that their marketing strategies may be more effective. They do research to learn what customers in different demographic groups are looking for in a particular product or service and what would make a marketing campaign successful with a specific audience. They then use software models to analyze the collected data and report their findings to marketing and sales departments to help them create data-driven strategies.

Market research analysts also gather data on their company’s competitors to learn where and how they’re succeeding or failing, incorporating these findings into their marketing recommendations. 

Based on their findings and their in-depth understanding of the customer base, market research analysts’ recommendations can range from how a product should be advertised to what its price should be. Their work requires an informed interpretation of data alongside an understanding of customer psychology to predict what type of campaign will work best to achieve the company’s goals.

Market Research Analyst Job Environment

Market research is used in nearly every industry to identify what customers want from a product or service. Typical employers of market research analysts include businesses, educational institutions, and government agencies. They usually work in office settings, as most of what a market research analyst does involves using computers to analyze data and performing research. However, they may need to travel to conduct interviews or meet with focus groups.

Market Research Analyst Skills

Market research analysts need to have a firm grasp of marketing principles and customer behavior, as these skills directly relate to developing marketing strategies. Additionally, as they need to communicate their findings to other departments, they should be skilled at relaying essential information to project stakeholders. 

Here are the key skills and competencies that aspiring market research analysts need to develop:

• Research skills
• Data analysis proficiency
• Marketing knowledge
• Customer psychology knowledge
• Communication skills
• Attention to detail
• Organizational skills
• Computer and software knowledge

How to Become a Market Research Analyst

Companies typically look for market research analysts with an established background in the field. The right foundational education and experience can help applicants stand out.

Most companies require candidates for marketing research positions to have a minimum of a bachelor’s degree, preferably in business, communications, or one of the social sciences, such as psychology . Psychology courses can help students learn about customer behavior and help them understand how to put purchasing habits and decisions in context. Courses that focus on interpreting research are also useful for aspiring analysts.

A bachelor’s degree is also a key requirement for graduate degree programs, which may be required for leadership positions.

Companies hiring market research analysts look for professionals with a proven track record in delivering successful strategies and big data analysis, and applicants may need to build up their resumes before becoming eligible for a position. Internships and entry-level roles such as market research assistant or data analyst can help candidates gain professional experience.

Market Research Analyst Salary and Job Outlook

According to the U.S. Bureau of Labor Statistics (BLS), the median annual market research analyst salary was $74,680 in 2023. Individuals’ salaries may be affected by factors such as their education, experience level, industry, and location.

The BLS projects 13% job growth for market research analysts between 2022 and 2032, significantly higher than the 3% average projected for all occupations. As companies increasingly rely on the power of customer data to provide insight into market trends, the job market for analysts will remain strong.

Take Your Next Career Step With Wilson College Online

The right education is essential to establishing the knowledge and skills core to what a market research analyst does. For those looking for a career that combines customer psychology and marketing, consider Wilson College Online’s Bachelor of Arts in Psychology degree program . 

The program is designed to help students gain a firm understanding of human behavior and decision-making, which are key to success as a market research analyst or in another career. Additionally, the program teaches students how to interpret research and apply their findings in a range of different industries.

Take control of your future with Wilson College Online.

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What is market share analysis?

Last updated

22 August 2024

Reviewed by

Understanding your company’s position within its market is important if you want to be successful in the long term. That’s where market share analysis comes in.

This figure represents the total percentage of sales in the industry earned by a particular company within a defined time period. It provides insight into how well your company is doing compared to others in the industry.

Ultimately, by conducting a market share analysis, you can better understand how your business stacks up against others. You can also use it to reveal strengths and weaknesses in your business.

Here’s everything you need to know about this valuable metric.

• Calculating market share

The process of calculating market share is straightforward.

First, you need to figure out which time frame you’d like to analyze. Business owners often want to assess their quarterly market share to examine seasonal trends or short-term performance. However, you might also want to gather annual data to examine performance over a full business cycle. The time frame you choose will depend on the goals you’ve set.

Next is the data collection phase. You’ll need to calculate the total market sales figure by adding together the sales from all companies in your market for the defined period. This information typically comes from industry reports, research firms, and government statistics. You also need the total sales generated by your company in that same period.

Ultimately, your company’s market share percentage is equal to the company’s sales divided by the total market sales. Here’s the formula to follow:

Market share (%) = (your business’s revenue/total category revenue) x 100

The following tips are worth considering to ensure the most accurate analysis possible:

Data accuracy: ensure the data you collect is accurate and up-to-date for a reliable market share calculation.

Market definition: clearly define your market so that you don’t include irrelevant sales data. The market can vary based on geography, product lines, or industry segments.

Comparative analysis: regularly calculate market share to track changes over time and understand your company’s performance relative to competitors.

• The benefits of market share analysis

Market share analysis as a metric can offer insight into your business’s growth trajectory and current and future sustainability. The strategic decisions you make with this data can give you a competitive edge.

Competitive comparison

Market share analysis helps you compare your performance with that of your competitors, allowing you to spot strengths and areas requiring improvement.

You’ll determine which company or brand has the highest market share. Whether that’s your business or another business, you’ll be able to position yourself accordingly.

You’ll also be able to set realistic goals for your business and map out desired improvements. If you’re the leading brand, you’ll outline a defensive strategy. If you want to take on the market leader, you’ll create an active strategy.

Strategic planning

The insights you derive from a market share analysis can help you develop a strategic plan that informs important decisions about your approaches to market entry, product development, business expansion, and marketing.

Performance tracking

You can track your business’s performance by assessing your market share. For instance, you can evaluate your sales performance and the effectiveness of your marketing campaigns, which will reveal the resultant increase (or decrease!) in market share. You’ll be able to adjust your strategies and set measurable objectives based on this data.

Resource allocation

With accurate market share analysis tools, you can optimize how you distribute your budget. The analysis can also help you invest in areas that yield higher returns and focus resources on market segments with larger growth potential.

• Factors impacting market share analysis

Several factors can impact the accuracy and relevancy of a market share analysis. If you understand these factors, you’ll have an easier time interpreting data correctly.

Market size

A market’s size will certainly influence your calculation.

If the market is growing, it may lead to increased sales for everyone in the market (yourself and your competitors). Conversely, if the market contracts, sales will drop for everyone. This can make it challenging to determine if your declining sales are due to:

Increased competition, where competitors are taking a larger share of the overall market

An overall industry downturn, where the size of the market is shrinking for everyone

Competitor number and strength

The number of competitors plays a role in determining your market share, but so does the strength of each competitor. For example, your sales might remain consistent, but a new competitor entering the market may fragment it.

You might also see changes based on competitors’ changes to marketing and pricing strategies, for instance.

Consumer preferences

Consumers also impact your company’s market share. For instance, consumers may switch to another company if you no longer offer the products they used to buy or if you change a major component of your product.

Trends can also cause market share fluctuations. Consider the growing trend toward eco-friendly and sustainable products. If a competitor starts offering more environmentally friendly options but your company doesn’t, you might lose customers who prioritize sustainability.

Economic conditions

The conditions of the economy also impact your market share. Economic downturns generally reduce consumer spending, increasing the market share of companies offering low-cost budget products or services as consumers seek to save money. An economic boom, on the other hand, will do the opposite, driving the share of premium, high-end brands and businesses.

Rising inflation and interest rates will also change market dynamics, generally shrinking the size of the market as a whole—or, again, driving consumers toward budget, low-cost options.

• Types of market share analysis

Market share analysis provides insight into different aspects of performance. When you understand the types of market share analysis reports, you can better evaluate your competitive position.

Revenue market share

Revenue market share is calculated based on your company’s revenue as a percentage of the total market revenue. This metric provides insight into the company’s financial strength compared to that of competitors.

If you have recently introduced new products or services, this metric helps assess how well these offerings are generating income.

Volume market share

Volume market share is based on the number of units your company has sold as a percentage of the total units sold within the market. This metric focuses on the quantity of products sold rather than the revenue those products have generated.

This figure helps you understand the popularity of your products and services and how well your new offerings have penetrated the market. However, it doesn’t account for the profitability of those units sold. A high-volume market share may not translate to high revenue if the products are sold at low prices or with thin margins. Additionally, it doesn’t provide insights into your company’s overall financial health.

Value market share

The value market share considers the volume of products your company has sold and the prices of those products.

This figure offers a comprehensive view of your business’s market position by combining both sales volume and pricing strategy. It can help you understand the value of your products and services and how much revenue your sales are generating. This can be particularly useful in assessing the effectiveness of premium pricing strategies and identifying high-margin products.

Customer market share

The customer market share is a value based on how many customers your company has compared to the total market. It helps you determine how well your customer acquisition and retention strategies are working.

This metric differs from the others because it focuses more on customers than product volume or revenue.

• Market share growth strategies

You can incorporate several types of strategies into your company’s plans to increase your market share. Consider the following:

Product innovation

Product innovation introduces new or improved products to the market. This strategy allows you to differentiate yourself from competitors and attract new customers.

You can incorporate product innovation into your business model by creating new products that fulfill your clients’ needs, making improvements to existing products, and personalizing products based on customer preferences.

Market penetration

You can use market share analysis to strategize market penetration. For instance, this metric may help you develop promotional campaigns, find new distribution channels, and optimize your sales team.

Customer retention

Market share analysis can help you focus on keeping the customers you currently have—not acquiring new ones. This strategy involves a focus on customer service, loyalty programs, and improvements based on feedback.

Competitive pricing

The data you gather may also help you adjust your prices so they are more competitive. For example, you may focus on reducing prices without compromising profitability.

You’ll also work in value-based pricing and bundling to attract customers looking for better value. Value-based pricing involves setting prices based on the perceived value of your product or service to the customer rather than solely on cost. This strategy can help you charge higher prices for products that offer superior benefits or solve significant problems for customers.

You can also consider bundling, which attracts customers looking for better value by offering a combination of products or services at a reduced price compared to buying them separately.

• Challenges in conducting market share analysis

Market share analysis isn’t always a simple process. It presents several challenges that can reduce the accuracy and reliability of the results.

You might also note changes in market dynamics that impact your data. For instance, technological advancements and changes in consumer behavior may skew the information you receive. Regularly update your market share analysis and integrate external factors like economic indicators, technological advances, regulatory changes, and social and cultural trends into your research.

• Tools for market share analysis

Several tools are available to help you find accurate, reliable data. Market research reports provide valuable insights into industry trends and market dynamics. These reports come from reputable market research firms.

Data analytics software helps businesses analyze large volumes of data and even create visual depictions of market share data. These tools can offer advanced analysis based on raw data.

Surveys and polls are also effective tools for collecting data from customers and other market participants. While surveys and polls are not the best methods of gathering insights for every project, you can gather some information about market trends and consumer preferences that will help contextualize your market share calculation.

• How to win market share

To win market share, start by understanding your target market thoroughly. Research and analyze your target audience to gain insights into their needs, preferences, and pain points. Use this insight to tailor your products or services to better meet their demands.

Improving the quality of your products or services should be a priority. Investing in quality enhancements can lead to increased customer satisfaction and loyalty, which in turn helps you gain a competitive edge and win market share.

Employing competitive pricing strategies is also key. Analyze competitors’ pricing and adjust yours to offer better value, considering approaches like value-based pricing, discounts, or bundling to attract price-sensitive customers.

By focusing on the areas outlined above, you can more effectively position your company to capture a larger share of the market.

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2024 Housing Market Forecast and Predictions: Housing Affordability Finally Begins to Turnaround

As we look ahead to 2024 , we see a mix of continuity and change in both the housing market and economy. Against a backdrop of modest economic growth, slightly higher unemployment, and easing inflation longer term interest rates including mortgage rates begin a slow retreat. The shift from climbing to falling mortgage rates improves housing affordability, but saps some of the urgency home shoppers had previously sensed. Less frenzied housing demand and plenty of rental home options keep home sales relatively stable at low levels in 2024, helping home prices to adjust slightly lower even as the number of for-sale homes continues to dwindle. 

Realtor.com ® 2024 Forecast for Key Housing Indicators

Home Prices Dip, Improving Affordability

Home prices grew at a double-digit annual clip for the better part of two years spanning the second half of 2020 through 2022, a notable burst following a growing streak that spanned back to 2012. As mortgage rates climbed, home price growth flatlined, actually declining on an annual basis in early 2023 before an early-year dip in mortgage rates spurred enough buyer demand to reignite competition for still-limited inventory. Home prices began to climb again, and while they did not reach a new monthly peak, on average for the year we expect that the 2023 median home price will slightly exceed the 2022 annual median.

Nevertheless, even during the brief period when prices eased, using a mortgage to buy a home remained expensive. Since May 2022, purchasing the typical for-sale home listing at the prevailing rate for a 30-year fixed-rate mortgage with a 20% down payment meant forking over a quarter or more of the typical household paycheck. In fact, in October 2023, it required 39% of the typical household income and this share is expected to average 36.7% for the full calendar year in 2023. This figure has typically ranged around 21%, so it is well above historical average. We expect that the return to pricing in line with financing costs will begin in 2024, and home prices, mortgage rates, and income growth will each contribute to the improvement. Home prices are expected to ease slightly, dropping less than 2% for the year on average. Combined with lower mortgage rates and income growth this will improve the home purchase mortgage payment share relative to median income to an average 34.9% in 2024, with the share slipping under 30% by the end of the year.

Home Sales Barely Budge Above 2023’s Likely Record Low

After soaring during the pandemic, existing home sales were weighed down in the latter half of 2022 as mortgage rates took off, climbing from just over 3% at the start of the year to a peak of more than 7% in the fourth quarter. The reprieve in mortgage rates in early 2023, when they dipped to around 6%, brought some life to home sales, but the renewed climb of mortgage rates has again exerted significant pressure on home sales that is exacerbated by the fact that a greater than usual number of households bought homes over the past few years, and despite stories of pandemic purchase regret , for the most part, these homeowners continue to be happy in their homes. 

This is consistent with what visitors to Realtor.com report when asked why they are not planning to sell their homes. The number one reason homeowners aren’t trying to sell is that they just don’t need to; concern about losing an existing low-rate mortgage is the top financial concern cited. Our current projection is for 2023 home sales to tally just over 4 million, a dip of 19% over the 2022 5 million total. 

With many of the same forces at play heading into 2024, the housing chill will continue, with sales expected to remain essentially unchanged at just over 4 million. Although mortgage rates are expected to ease throughout the course of the year, the continuation of high costs will mean that existing homeowners will have a very high threshold for deciding to move, with many likely choosing to stay in place.  Moves of necessity–for job changes, family situation changes, and downsizing to a more affordable market–are likely to drive home sales in 2024. 

Shoppers Find Even Fewer Existing Homes For Sale

Even before the pandemic, housing inventory was on a long, slow downward trajectory. Insufficient building meant that the supply of houses did not keep up with household formation and left little slack in the housing market. Both homeowner and rental vacancy remain below historic averages . In contrast with the existing home market, which remains sluggish, builders have been catching up, with construction remaining near pre-pandemic highs for single-family and hitting record levels for multi-family . 

Despite this, the lack of excess capacity in housing has been painfully obvious in the for-sale home market. The number of existing homes on the market has dwindled. With home sales activity to continue at a relatively low pace, the number of unsold homes on the market is also expected to remain low.  Although mortgage rates are expected to begin to ease, they are expected to exceed 6.5% for the calendar year. This means that the lock-in effect, in which the gap between market mortgage rates and the mortgage rates existing homeowners enjoy on their outstanding mortgage, will remain a factor. Roughly two-thirds of outstanding mortgages have a rate under 4% and more than 90% have a rate less than 6%.

Rental Supply Outpaces Demand to Drive Mild Further Decline in Rents

After almost a full year of double-digit rent growth between mid-2021 and mid-2022, the rental market has finally cooled down, as evidenced by the year-over-year decline that started in May 2023 . In 2024, we expect the rental market will closely resemble the dynamics witnessed in 2023, as the tug of war between supply and demand results in a mild annual decline of -0.2% in the median asking rent.

New multi-family supply will continue to be a key element shaping the 2024 rental market.  In the third quarter of 2023, the annual pace of newly completed multi-family homes stood at 385,000 units. Although absorption rates remained elevated in the second quarter, especially at lower price points, the rental vacancy rate ticked up to 6.6% in the third quarter. This uptick in rental vacancy suggests the recent supply has outpaced demand, but context is important. After recent gains, the rental vacancy rate is on par with its level right before the onset of the pandemic in early 2020, still below its 7.2% average from the 2013 to 2019 period.  Looking ahead, the strong construction pipeline– which hit a record high for units under construction this summer –is expected to continue fueling rental supply growth in 2024 pushing rental vacancy back toward its long-run average. 

While the surge in new multi-family supply gives renters options, the sheer number of renters will minimize the potential price impact. The median asking rent in 2024 is expected to drop only slightly below its 2023 level. Renting is expected to continue to be a more budget friendly option than buying in the vast majority of markets, even though home prices and mortgage rates are both expected to dip, helping pull the purchase market down slightly from record unaffordability. 

Young adult renters who lack the benefit of historically high home equity to tap into for a home purchase will continue to find the housing market challenging. Specifically, as many Millennials age past first-time home buying age and more Gen Z approach these years, the current housing landscape is likely to keep these households in the rental market for a longer period as they work to save up more money for the growing down payment needed to buy a first home. This trend is expected to sustain robust demand for rental properties. Consequently, we anticipate that rental markets favored by young adults , a list which includes a mix of affordable areas and tech-heavy job markets in the South, Midwest, and West, will be rental markets to watch in 2024.

Key Wildcards:

• Wildcard 1: Mortgage Rates With both mortgage rates and home prices expected to turn the corner in 2024, record high unaffordability will become a thing of the past, though as noted above, the return to normal won’t be accomplished within the year. This prediction hinges on the expectation that inflation will continue to subside, enabling the recent declines in longer-term interest rates to continue. If inflation were to instead see a surprise resurgence, this aspect of the forecast would change, and home sales could slip lower instead of steadying.
• Wildcard 2: Geopolitics In our forecast for 2023 , we cited the risk of geopolitical instability on trade and energy costs as something to watch. In addition to Russia’s ongoing war in Ukraine, instability in the Middle East has not only had a catastrophic human toll, both conflicts have the potential to impact the economic outlook in ways that cannot be fully anticipated. 
• Wildcard 3: Domestic Politics: 2024 Elections In 2020, amid the upheaval of pandemic-era adaptations, many Americans were on the move. We noted that Realtor.com traffic patterns indicated that home shoppers in very traditionally ‘blue’ or Democratic areas were tending to look for homes in markets where voters have more typically voted ‘red’ or Republican. While consumers also reported preferring to live in locations where their political views align with the majority , few actually reported wanting to move for this reason alone. 

Housing Perspectives:

What will the market be like for homebuyers, especially first-time homebuyers.

First-time homebuyers will continue to face a challenging housing market in 2024, but there are some green shoots. The record-high share of income required to purchase the median priced home is expected to begin to decline as mortgage rates ease, home prices soften, and incomes grow. In 2023 we expect that for the year as a whole, the monthly cost of financing the typical for-sale home will average more than $2,240, a nearly 20% increase over the mortgage payment in 2022, and roughly double the typical payment for buyers in 2020. This amounted to a whopping nearly 37% of the typical household income. In 2024 as modest price declines take hold and mortgage rates dip, the typical purchase cost is expected to slip just under $2,200 which would amount to nearly 35% of income. While far higher than historically average, this is a significant first step in a buyer-friendly direction.

How can homebuyers prepare? 

Homebuyers can prepare for this year’s housing market by getting financially ready. Buyers can use a home affordability calculator , like this one at Realtor.com to translate their income and savings into a home price range. And shoppers can pressure test the results by using a mortgage calculator to consider different down payment, price, and loan scenarios to see how their monthly costs would be impacted. Working with a lender can help potential buyers explore different loan products such as FHA or VA loans that may offer lower mortgage interest rates or more flexible credit criteria. 

Although prices are anticipated to fall in 2024, housing costs remain high, and a down payment can be a big obstacle for buyers. Recent research shows that the typical down payment on a home reached a record high of $30,000 .  To make it easier to cobble together a down payment, shoppers can access information about down payment assistance options at Realtor.com/fairhousing and in the monthly payment section of home listing pages. Furthermore, home shoppers can explore loan products geared toward helping families access homeownership by enabling down payments as low as 3.5% in the case of FHA loans and 0% in the case of VA loans .

What will the market be like for home sellers?

Home sellers are likely to face more competition from builders than from other sellers in 2024. Because builders are continuing to maintain supply and increasingly adapting to market conditions, they are increasingly focused on lower-priced homes and willing to make price adjustments when needed. As a result, potential sellers will want to consider the landscape for new construction housing in their markets and any implications for pricing and marketing before listing their home for sale.

What will the market be like for renters?

In 2024, renting is expected to continue to be a more cost-effective option than buying in the short term even though we anticipate the advantage for renting to diminish as home prices and mortgage rates decline. 

However, for those considering the pursuit of long-term equity through homeownership, it’s essential to not only stay alert about market trends but also to carefully consider the intended duration of residence in their next home. When home prices rise rapidly, like they did during the pandemic, the higher cost of purchasing a home may break even with the cost of renting in as little as 3 years. Generally, it takes longer to reach the breakeven point, typically within a 5 to 7-year timeframe. Importantly, when home prices are falling and rents are also declining, as is expected to be the case in 2024, it can take longer to recoup some of the higher costs of buying a home. Individuals using Realtor.com’s Rent vs. Buy Calculator can thoroughly evaluate the costs and benefits associated with renting versus buying over time and how many years current market trends suggest it will take before buying is the better financial decision. This comprehensive tool can provide insights tailored to a household’s specific rent versus buying decision and empowers consumers to consider not only the optimal choice for the current month but also how the trade-offs evolve over several years.

Local Market Predictions:

All real estate is local and while the national trends are instructive, what matters most is what’s expected in your local market. 

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One-Third of Property Managers are Offering Concessions as Rental Market Cools

The Numbers

JULY 2024 U.S. Typical Home Value (Zillow Home Value Index)

$362,482 (2.8% YoY)

JULY 2024 U.S. Typical Rent (Zillow Observed Rent Index)

$2,070 (3.4% YOY)

July 2024 Change in New Listings

MAY 2024 Typical Mortgage Payment

• June 2024 S&P Case-Shiller Price Index: Home Price Growth Continues to Stall
• July 2024 Existing Home Sales Exceeds Expectations By Small Margin
• New Home Sales Rebounded More Than Expected In July
• Mortgage Rates Eased Slightly This Week On Employment Data Revisions And Fed Minutes
• Zillow Counts: Mid-month Update in Zillow’s Existing Home SalesNowcast (July 2024)
• Zillow Home Value and Home Sales Forecast (July 2024)
• July 2024 Housing Starts: Housing Starts Fall, Single Family Starts At Lowest Level Since April 2023
• Support Growing for Middle Housing
• Sellers lose their advantage, but lower rates may revive market competition (July 2024 Market Report)
• Mortgage Rates Fell Again This Week On Positive Inflation News

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Asia Pacific (APAC) Data Center Blade Server Market - Forecasts from 2024 to 2029

• Table of Contents
• Description
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1. INTRODUCTION 1.1. Market Overview 1.2. Market Definition 1.3. Scope of the Study 1.4. Market Segmentation 1.5. Currency 1.6. Assumptions 1.7. Base and Forecast Years Timeline 1.8. Key Benefits for the Stakeholder 2. RESEARCH METHODOLOGY 2.1. Research Design 2.2. Research Processes 3. EXECUTIVE SUMMARY 3.1. Key Findings 4. MARKET DYNAMICS 4.1. Market Drivers 4.2. Market Restraints 4.3. Porter’s Five Forces Analysis 4.3.1. Bargaining Power of Suppliers 4.3.2. Bargaining Power of Buyers 4.3.3. Threat of New Entrants 4.3.4. Threat of Substitutes 4.3.5. Competitive Rivalry in the Industry 4.4. Industry Value Chain Analysis 4.5. Analyst View 5. ASIA PACIFIC (APAC) DATA CENTER BLADE SERVER MARKET BY DATA CENTER TYPE 5.1. Introduction 5.2. Tier 1 5.3. Tier 2 5.4. Tier 3 5.5. Tier 4 6. ASIA PACIFIC (APAC) DATA CENTER BLADE SERVER MARKET BY SERVICE 6.1. Introduction 6.2. Professional Service 6.3. Consulting Service 6.4. Installation and Support Service 7. ASIA PACIFIC (APAC) DATA CENTER BLADE SERVER MARKET BY ENTERPRISE SIZE 7.1. Introduction 7.2. Small 7.3. Medium 7.4. Large 8. ASIA PACIFIC (APAC) DATA CENTER BLADE SERVER MARKET BY INDUSTRY VERTICAL 8.1. Introduction 8.2. IT and Telecom 8.3. Manufacturing 8.4. Media and Entertainment 8.5. BFSI 8.6. Retail 8.7. Government 8.8. Healthcare 8.9. Others 9. ASIA PACIFIC (APAC) DATA CENTER BLADE SERVER MARKET BY COUNTRY 9.1. Introduction 9.2. India 9.3. Japan 9.4. China 9.5. Taiwan 9.6. Thailand 9.7. Indonesia 9.8. Others 10. COMPETITIVE ENVIRONMENT AND ANALYSIS 10.1. Major Players and Strategy Analysis 10.2. Market Share Analysis 10.3. Mergers, Acquisitions, Agreements, and Collaborations 10.4. Competitive Dashboard 11. COMPANY PROFILES 11.1. Dell Inc 11.2. Cisco 11.3. Huawei 11.4. IBM 11.5. Lenovo 11.6. Inspur Systems 11.7. Fujitsu 11.8. NEC Corporation 11.9. Unitas Global 11.10. Amazon Web Service (Amazon)

Data Center Blade Server Market - Forecasts from 2024 to 2029

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