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Quantitative Research Examples

Market research 04 12
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In Quantitative research, marketers collect and analyze numerical data to make a data-driven decision. The research process uses Statistical Techniques to analyze, and interpret the numerical data collected from the audience.

Quantitative Research is used to identify patterns, test causal relationships, make predictions, and generalize data to the entire population. The aim of the research is to quantify the issue in order to make logical and statistical sense of it. The numerical data helps understand how common the problem is.

Quantitative Research is mostly used in the field of natural and social science

  • Biology
  • Chemistry
  • Psychology
  • Economics
  • Marketing

4 Types of Quantitative Research Methods

Quantitative Research2

You can use any of the four methods to conduct Quantitative Research to test the purpose of the research. 

  • Descriptive research. 
  • Experimental research. 
  • Correlational research. 
  • Surveys. 

Let’s explore these four types of quantitative research methods with examples. 

Descriptive Research:

It is used to examine more than one variable and aims to accurately describe a situation or population. In Descriptive research, you learn the answers for ‘what’, ‘where’, ‘when’, and ‘how’. 

When your purpose is to identify frequency or trends, descriptive research is a good option. It is mostly useful when you need to understand ‘how’, ‘when’, and ‘where’ something happened before you can dive into the ‘why’. Marketers can only observe and examine the variables and not manipulate them. 

Methods used to collect data for Descriptive Research are – Surveys, Observation, and Case Studies. 

Quantitative research example in descriptive research: 

A market research firm can use descriptive research to understand the demographics and purchase patterns of customers in a particular city. The firm can use online surveys to gather data on age, gender, income, purchase preferences, and brand loyalty. 

The survey software can help the market research firm analyze the target market’s characteristics and find correlations between variables. The firm can thus enable businesses to align their marketing strategies and product offerings.

Read how Voxco helped Modus Research increase research efficiency with Voxco Online, CATI, IVR, and panel system.

Experimental Research: 

Mostly used in Natural Sciences, Experimental Research aims to prove a hypothesis. One or more than one theory is undertaken in the research, and the entire process consists of proving or disproving the concerning theories. 

Experimental Research involves two sets of variables and tries to establish a cause-effect relationship among the variables. As a researcher, you can manipulate all variables except one called the dependent variable. The independent variable is manipulated in order to gauge the effect it has on the dependent variable. 

Quantitative research example in experimental approach: 

A cosmetic company wanting to test the effectiveness of their new anti-aging cream compared to their existing product can use experimental research. Their research team can randomly select participants into two groups – one group uses the new cream, and the other uses the existing product. 

After a defined period, their team can use surveys to gather the customers’ perceptions and satisfaction with the products. By comparing the results from both groups, the company can determine if the new cream outperforms the existing product. 

Correlational Research:

Correlation Research includes finding a relationship between two or more variables. It observes the impact one variable has on the other and the changes that occur. The purpose of Correlation research is to find trends and patterns between the variables. In this research, the researcher does not control any of the factors. 

  • Positive Correlation: The variables change in a similar direction. 
  • Negative Correlation: The variables change in opposite directions.
  • Zero Correlation: No relationship is found between the variables.

To test the hypothesis, researchers use Correlational and Experimental Research using statistics. 

Quantitative research example in correlation research: 

A fashion retail brand can use correlation research to determine the association between customer satisfaction and sales volume at their various retail location. 

They can use offline and online surveys to measure customer satisfaction. They can use mobile-offline surveys to gather data about the in-store shopping experience and staff friendliness. And send post-purchase surveys via email or SMS to gauge product quality and other relevant factors. 

After a period of six months, the team can perform a correlational analysis to identify the relationship between customer satisfaction and sales performance. They can leverage survey software to integrate both offline and online survey data and streamline analysis. 

Survey Research: 

The aim of conducting survey research is to understand the desire, needs, preferences, and behavior of the target market. Researchers ask questions that help them identify their target audience or to receive feedback from the customers. 

With Voxco survey software, you can conduct quantitative surveys online, offline, and CATI surveys. 

Quantitative research example in the survey: 

An online e-com website uses surveys to gather feedback from customers about their shopping experience on their website. They design the survey to assess the website usability, customer service, product selection, and overall satisfaction. 

The e-com company can leverage online survey tools to distribute surveys via email and embed on their website. The retailer gathers quantitative data and analyzes it to identify areas of improvement, enabling them to enhance customer satisfaction. 

Check out Voxco Online features to see how it can help you unlock the power of data with its robust functionalities.

  • 100+ question types
  • Advanced logic 
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  • Auto-translations in 100+ languages

Quantitative Research Example in Customer Experience

Quantitative Research can provide descriptive data to determine the variance between particular segments of customers. The data collected is numerical and it is difficult to argue against statistical data. The numerical element of Quantitative research validates the prediction,

So, quantitative research also helps to measure and predict customer experience. For commercial business, customers, their interaction, feedback, and experience, play a vital role. This makes measuring and developing services to improve customer experience the most important task. 

Quantitative research makes it easy to analyze and evaluate the data collected. Visualizing the data in charts or graphs makes it easy to get an overall view of how successful a company is in offering a positive customer experience. You can thus use the object and statistically accurate data to develop strategies. 

Moreover, with a suitable sample size, the quantitative research data can be generalized to the entire population. The results obtained are valid and trustworthy. The researchers can thus make informed decisions regarding the business.

How to analyze data from Quantitative Research?

With the data all collected after the survey, you need to analyze it in order to use it. You can use statistical analysis such as SWOT, Conjoint, Turf, or Cross-tabulation. 


This type of statistical analysis is used to evaluate the internal and external performance of the organization. It helps identify the Strength, Weaknesses, Opportunities, and Threats of an organization to develop effective strategies. 

TURF Analysis: 

Totally Unduplicated Reach and Frequency Analysis is used to determine the reach of a preferred channel of communication. Also, the frequency of the communication is analyzed to understand its potential in the market.

Cross Tabulation: 

It helps establish relationships, trends, and patterns between various attributes. 

Descriptive Statistics provides an overview of data along with averages and variability. You can use graphs, frequency tables, and other visual charts to check for trends. 

Modern survey software provides the benefit of quantitative data analysis to analyze large volumes of data efficiently and seamlessly. Voxco empowers you with cross-tabulations, linear regression, ordered logistic regression, factor analysis, and more to enhance insight generation.

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Examples of Quantitative Research Applications

Let’s explore the versatility of quantitative research across different industries by looking into the following examples.  

Quantitative research example 1: Market research → 

A cosmetic company organizes an event to showcase its new line of products. They decide to conduct Quantitative research and collect feedback from the audience about their experience and their perception of the event. The company can gather knowledge about customer satisfaction during pre and post-event as well as sales. 

They can ask about the likelihood of recommendation, frequency of purchase, etc. In the end, with all data collected, it can be analyzed, and actionable insights can be derived. The company can take measures to improve its performance to increase customer satisfaction. 

Quantitative research example 2: Social research → 

 Quantitative Research may also be used to study the impact of social media on customer purchases. The sample size for this survey requires a suitable number of male and female respondents. It is necessary that the sample size is large enough to reflect the entire target market. 

The research can make use of a numerical scale like the Likert Scale to ask questions regarding the research topic. The question could be: “How likely are you to purchase a product after finding it recommended by a social media influencer on their Instagram?”

The options could range from 1 (least likely) to 10 (most likely). 

The numerical scale is easy to quantify and is a powerful Quantitative Research tool. 

Quantitative research example 3: Healthcare research →

A healthcare organization can use this research method to assess the effect of physical activity on heart health. They can utilize quantitative surveys to gather data from different age groups, ethnic backgrounds, and fitness levels. 

The survey can include questions about the participant’s physical activity levels, lifestyle, and cardiovascular health markers like heart rate, blood pressure, and cholesterol levels. 

Once the data is collected, the research team can leverage data analysis tools to perform correlation and regression analyses. It can help them determine the relationship between physical activity and heart health.

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When to use Quantitative Research?

As we have already mentioned, quantitative research collects data that can be quantified and thus produce a statistical report. 

It thus answers questions like – how much, how often, how likely. It gives you insight regarding the ‘what’, ‘when’, and ‘who’ before you can further dig into the ‘why’. 

Some examples of when you can use Quantitative Research are:

  • When you have a large sample size and the need to prove a hypothesis
  • When measuring satisfaction or usability metrics
  • When you need to gather feedback from different segments of customers not from the same location.


Data Collection is the process when observations are gathered for research. It is not only restricted for academic purposes but is also used by governments, businesses, etc.

To collect data for Quantitative research tools like Questionnaires, Focus Groups, Interviews, and Observation are most commonly used.

The aim of Quantitative Research is to gain insight into the audience. In marketing, it is used to understand what influences the Customer’s purchase decision.