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Quantitative Data Analysis
Quantitative Data Analysis – A Guide with Tips

Quantitative Data Analysis – A Guide with Tips

Looking to learn about quantitative data and analysis? This guide has got you covered! Discover what quantitative data is, its significance, and how to use statistical techniques to analyze it. With practical examples and helpful tips, you’ll be able to make informed decisions based on numerical information.

Quantitative data and quantitative data analysis play a vital role in market research. Market research is the process of researching your target market. A target market is a market a business serves through its product/service. It comprises the target audience – the group of people a business will be selling its product/service to. Connecting the dots together, quantitative data analysis is done to study your target audience i.e., the end-users/customers/consumers of your product/service.

Let’s find out how it is done.

What is Quantitative Data?

Quantitative data is any data that can be valued in numbers. The data is to be interpreted through mathematical calculations and statistical concepts to derive meaningful information. Through quantitative data, the researcher is able to get answers to questions like ‘how many’, ‘how much’ and anything that throws light into the target market of a business.

What is Quantitative Data Analysis?

Quantitative data forms the basis of any quantitative data analysis. Quantitative data analysis is a research method that involves the use of statistical techniques to analyze numerical data / quantitative data. It is a systematic approach to investigating phenomena and relationships among variables by collecting and analyzing numerical data, such as measurements or survey responses.

In this approach, researchers collect data through various methods such as surveys, experiments, observations, or secondary sources. The collected data is then analyzed using statistical tools such as regression analysis, t-tests, ANOVA, and chi-square tests, among others. The results of the analysis are used to make conclusions and predictions about the population from which the data was collected.

Quantitative data analysis is widely used in various fields such as social sciences, business, engineering, and healthcare to answer research questions and test hypotheses. It allows researchers to objectively analyze data and identify patterns and trends that may not be apparent with qualitative data analysis.

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How is Quantitative Data Analysis in Market Research Done?

Once the researchers have gathered the required quantitative data through different methods, it is time to use mathematical concepts and statistical tools to analyze the data. These are some of the statistical analytical tools in quantitative data analysis to exact meaningful conclusions on market research:

tools and methods in quantitative data analysis
  • Conjoint analysis
    The conjoint analysis aids researchers in knowing about the buyer’s behavior. The conjoint analysis breaks down attributes into smaller data to determine customer preference. For instance, in conjoint analysis of cars, a product is first categorized into various attributes, say, brand, color, price, fuel type, etc. Thereafter, each attribute is further broken down into different levels. Conjoint analysis is best used in making pricing decisions, product repositioning, market segmentation, product launch, etc.
  • GAP analysis

    GAP analysis is another method used in bridging the gap between the current state of a particular product/service and the desired action plan. Businesses use several GAP analysis tools like SWOT analysis, fishbone diagram, and McKinsey 7 framework to identify the gap and fill it in with the desired actions.

    For instance, if, through quantitative data analysis, it is inferred that there is a gap between your predicted sales and actual sales, you need to work on finding the reasons behind the poor sales. If the reason behind poor sales is a lack of awareness, the business needs to ramp up its marketing team. If poor quality is the reason, the business needs to focus on quality control and so on.

  • TURF Analysis

    TURF analysis is used when the business is in a dilemma to choose the best option from the combination of options. For instance, as a smartphone brand, they need to add a few new features to the new model. However, they can only allow for one or two of them due to price constraints.

    TURF analysis or Total Unduplicated Reach and Frequency through its reach and frequency helps reach a decision. Reach, in this case, refers to the number of customers a brand will reach by adding a particular feature. Frequency would calculate how often that would happen.

  • MaxDiff analysis

    In a MaxDiff survey, respondents are asked to choose the ‘most’ preferred and ‘least preferred item from the list. Each option is given a score. The max diff is calculated with the help of a score to find the best-worst ranking of a given attribute. For instance, the question is to choose the most preferred vacation. The options are mountains, beaches, waterfalls, and snowfalls. The respondent has to pick the best and worst vacation type for them. The answers gathered will allow the researchers to know customer preferences accordingly. Such a survey will be helpful for travel/tour aggregator companies.

Why is Quantitative Data Analysis Done?

  • To understand your customer
    It is through the identification of needs and wants, likes and dislikes, preferences, and customer buying behavior that a business can survive. The main aim of quantitative data analysis is to figure the customer out with the help of various quantitative data analysis methods and techniques.
  • Know your target group

    Quantitative data analysis is also done to know your target audience. Is the product/service suitable for youngsters, kids, adults, or elderlies? Is it suitable for rural customers or urban? What persuades and encourages them to buy the product? Is your target audience based online? Do they use smartphones or the internet? If yes, what percentage of your customers use social media websites?

    For instance, if, through quantitative data analysis, it is inferred that there is a gap between your predicted sales and actual sales, you need to work on finding the reasons behind the poor sales. If the reason behind poor sales is a lack of awareness, the business needs to ramp up its marketing team. If poor quality is the reason, the business needs to focus on quality control and so on.

  • Changing trends of your target market

    Suppose your target market is the automobile industry. Changing trends would refer to the buyer’s opinion on shifting from a fuel-based vehicle to an electric vehicle. Similarly, for a smartphone company, a changing trend would be to know how much percentage of their users would like to shift to foldable smartphones.

  • Web usage report analysis

    If a brand owns a website, quantitative market research is crucial to calculate the online market presence. Quantitative data analysis through its various methods will be able to find answers to questions like:

    1. How long does the visitor stay on the website?
    2. What is the rate at which website visitors contribute to online sales (conversion rates)?
    3. What are the bounce rates of the website? What makes a user leave their website or abandon the cart?
    4. What is the total amount of sales contributed from the website?
    5. What percentage of your target audience is redirected to your website from social media platforms?

Methods of Quantitative Data Analysis

There are many modern and traditional techniques used to perform quantitative data analysis.

Traditional quantitative data analysis methods

  • Questionnaires are one of the oldest ways to collect user feedback. It is a research tool that involves a series of multi-choice questions to be filled in by the respondent. Some businesses still use questionnaires to gather meaningful customer data, especially in restaurants and hotels. Questionnaires are also helpful in gathering respondents’ opinions about the product/service and their contact information.
  • Surveys traditionally used to be paper-based or oral. Online surveys have covered the major portion of the frame now. Surveys mainly involve a series of yes/no questions so that the answers collected are precise. Surveys are also useful for collecting data from large audience sizes. The only problem with survey data is there is a risk of generalizing the data. The data analysis should be done such that the final inference justifies the opinion of the population as a whole and not just the sample population.

    There are two types of surveys: longitudinal and cross-sectional. Longitudinal surveys are spread over a span of time – say, years. Market research is conducted on a particular target market or years; after that, statistical methods are used to analyze the data. On the other hand, cross-sectional surveys collect data at a given point in time. The sample population is pre-decided. Basically, the market research is done in a short period.
  • Telephonic surveys have a better turnover than online surveys. Usually, conducted after a service is granted, the feedback through telephonic surveys has less chance of denial. Telephonic surveys can also convey the research’s importance and seriousness through the researcher’s voice and tone. Similarly, the feedback provided by respondents is more reliable.

Modern quantitative data analysis methods

  • Online surveys are executed with the help of online survey tools. These tools are built with features like attractive visuals that help respondents engage and interact with the survey. Such tools incorporate drag and drop, slider, and card gamification-like features to make answering surveys fun and engaging.
  • Omnibusing is a new way to gather responses when your business needs answers to just a few questions. When investing in a full-fledged survey is not a good idea, omnibusing helps you get answers by incorporating your questions in group questions. Such a group includes questions from multiple businesses. This reduces the overall cost.
  • Email surveys are a way to get the responses of respondents through emails. Through email surveys, the answers can be received within hours without having to interact with the respondent. However, these surveys are effective for questions with yes/no or A/B answers and do not work with descriptive answers.
  • Social media-based listening – Quantitative market research can also be conducted with the help of social media. Researchers can simply use stories and direct messages to get answers from the responders. Brands can also take a look at their comment section to know if users are talking good or bad about their brand.

Advantages of Quantitative Data Analysis

  1. Easy to calculate and understand – The results produced from quantitative data analysis in market research are easy to understand. The answers are exact. This helps to excrete meaningful results from the data using the data analysis methods. For instance, it’s easy to gauge customer preferences through MaxDiff analysis. TURF analysis is also a straightforward method to choose the best among different attributes.
  2. Numerical results are rational – Quantitative market research data analysis produces numerical and measurable results. It helps businesses to make straightforward decisions. Decisions about a product/service are made as easier as yes or no.
  3. High response rates – Respondents involved in quantitative data analysis are more responsive as compared to qualitative data analysis. The respondent does not have to be deeply involved with the business to be fit to give a response. The surveys include close-end questions that are easier for a target audience to answer.
  4. Cost-effective – Quantitative data analysis is also cost-effective in terms of brand awareness. Since there are a lot of methods through which quantitative data analysis can be done through online mediums, brand awareness is not needed as your online followers are your direct respondents. The ones that are actually interested in your product/service and not forced to complete a survey for the sake of it.

Limitations of Quantitative Market Research

  1. Quantitative data is incomplete at times – Sometimes, data collected through surveys (closed-ended questions) isn’t capable of finding out the ‘why’ of the answer. As the data collected is just numbers, they aren’t always feasible to counteract a given problem at hand.
  2. Limited scope – Quantitative data works with a smaller range of the population. This limits the results to the sample population that may not represent the larger audience. Thus, the results may not be true for a large part of your target audience.


Quantitative data analysis in market research is done to understand the brand’s target audience. It helps shape business decisions based on data collected through quantitative data analysis methods. The most common quantitative market research methods are surveys (online and offline) and questionnaires. Market researchers and marketers do quantitative market research to help other businesses scale.

Learn about Survey Data Analysis and Reporting

FAQ on Quantitative Data and Quantitative Data Analysis

What is quantitative data in market research?

Quantitative data in market research refers to numerical information that is collected through surveys, questionnaires, and other structured data collection methods. It is used to measure and quantify consumer attitudes, behaviors, and preferences.

What are some advantages of using quantitative data in market research?

The advantages of using quantitative data in market research include its ability to provide precise and measurable insights, its statistical significance, and its ability to identify patterns and trends.

What are some common methods of collecting quantitative data in market research?

Common methods of collecting quantitative data in market research include online surveys, telephone surveys, face-to-face interviews, and mail surveys.

How is quantitative data analyzed in market research?

Quantitative data is typically analyzed using statistical analysis tools, such as regression analysis, correlation analysis, and factor analysis.

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