What is Regression Analysis & How Is It Used?
Regression analysis helps organizations make sense of priority areas and what factors have the most impact and influence on their customer relationships. It allows researchers and brands to read between the lines of the survey data. This article will help you understand the definition of regression analysis, how it is commonly used, and the benefits of using regression research.
Regression Analysis: Definition
Regression analysis is a common statistical method that helps organizations understand the relationship between independent variables and dependent variables.
- Dependent variable: The main factor you want to measure or understand.
- Independent variables: The secondary factors you believe to have an influence on your dependent variable.
More specifically regression analysis tells you what factors are most important, which to disregard, and how each factor affects one another.
Importance of Regression Analysis
There are several benefits of regression analysis, most of which center around using it to achieve data-driven decision-making.
The advantages of using regression analysis in research include:
- Great tool for forecasting: While there is no such thing as a magic crystal ball, regression research is a great approach to measuring predictive analytics and forecasting.
- Focus attention on priority areas of improvement: Regression statistical analysis helps businesses and organizations prioritize efforts to improve customer satisfaction metrics such as net promoter score, customer effort score, and customer loyalty. Using regression analysis in quantitative research provides the opportunity to take corrective actions on the items that will most positively improve overall satisfaction.
When to Use Regression Analysis
A common use of regression analysis is understanding how the likelihood to recommend a product or service (dependent variable) is impacted by changes in wait time, price, and quantity purchased (presumably independent variables). A popular way to measure this is with net promoter score (NPS) as it is one of the most commonly used metrics in market research.
Net promoter score formula
The score is very telling to help your business understand how many raving fans your brand has in comparison to your key competitors and industry benchmarks. While our online survey company always recommends using an open-ended question after NPS to gather context to help understand the driving forces behind the score, sometimes it does not tell the whole story.
Regression Analysis Example in Business
Keeping with the bank survey from above, let’s say in the same survey you ask a series of customer satisfaction questions related to respondents’ experience with the bank. You believe the interest rates and customer service are good at your bank but you think there might be some underlying drivers really pushing your high NPS. In this example, likelihood to recommend, or NPS is your dependent variable A. Your more specific follow-up satisfaction questions are dependent variables B, C, D, E, F, G.
Through your regression analysis, you find out that INDEPENDENT VARIABLE C (friendliness of the staff) has the most significant effect on NPS. This means how the customer rates the friendliness of the staff members will have the largest overall impact on how likely they would be to recommend your bank. This is much different than what customers said in the open-ended comment about interest rates and customer service. However, as regression analysis proves, staff friendliness is essential.
Regression analysis is another tool market research firms used on a daily basis with their clients to help brands understand survey data from customers. The benefit of using a third-party market research firm is that you can leverage their expertise to tell you the “so what” of your customer survey data.
At The MSR Group, we use regression analysis to help our clients understand the relationship between independent variables and dependent variables. We have worked with banks to understand the impact that key index scores from the markets had on sales projections. We also help our clients prioritize efforts to improve customer satisfaction metrics such as net promoter score, customer effort score, and customer loyalty.
If you are interested in using regression analysis to help your business make data-driven decisions, contact The MSR Group by filling out an online contact form or emailing info@msrgroup.com. Regression analysis is a powerful tool that can help executives and management make data-driven decisions. It can help them understand the relationship between independent variables and dependent variables, and how each factor affects one another. It can also help them focus their attention on priority areas of improvement, and use predictive analytics and forecasting to understand how their revenue might be impacted in future quarters.
At The MSR Group, we use regression analysis to help our clients understand the relationship between independent variables and dependent variables, and prioritize efforts to improve customer satisfaction metrics. We have worked with banks to understand the impact that key index scores from the markets had on sales projections, and how increasing prices will have any impact on repeat customer purchases. Using regression analysis in quantitative research provides the opportunity to take corrective actions on the items that will most positively improve overall satisfaction.
