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.
Interested in using regression analysis? Drive Research can help with that too. Reach our market research company by filling out an online contact form or emailing [email protected].
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.
In a simple example, say you want to find out how pricing, customer service, and product quality impacts (independent variables) impact customer retention (dependent variable).
A survey using regression analysis research is used to determine if increasing prices will have any impact on repeat customer purchases.
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:
1. 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.
For instance, our market research company worked with a manufacturing company to understand the impact that key index scores from the markets had on sales projections.
Regression analysis was used to understand how revenue would be impacted by independent variables such as:
- The ups and downs of oil prices
- The consumer price index (CPI)
- The gross domestic product (GDP)
We used reports and predictive analytic forecasts on these key independent variable statistics to understand how their revenue might be impacted in future quarters.
Though keep in mind, the further in the future you predict, the less reliable the data will be using a wider margin of error.
2. 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.
The calculation or score is based on a simple 'likelihood to recommend' question.
It is a 0 to 10 scale where “10” indicates very likely to recommend and “0” indicates not at all likely to recommend.
It groups your customers into 3 buckets:
- Promoters or those who rate your brand a 9 or 10
- Passives or those who rate your brand a 7 or 8
- Detractors or those who rate your brand a 0 to 6
The NPS is calculated as the difference between the percentage of promoters and the percentage of detractors (i.e., 75% promoters - 15% detractors = +60 NPS.)
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.
For instance, if you were to ask a customer why they rated your restaurant a “10” on the likelihood to recommend scale, they may say something like “good prices” or “good food” in an open-ended comment.
But is that what is really driving your high NPS rating?
Customers are often not experts at expressing their emotions and feelings in a survey.
This is where regression analysis can help.
Regression Analysis Example in Business
Keeping with the restaurant survey from above, let’s say in the same survey you ask a series of customer satisfaction questions related to respondents’ dining experience at your restaurant.
You believe the price and food are good at your restaurant 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.
You ask on a scale of 1 to 5 where “5” indicates very satisfied and “1” indicates not at all satisfied, how satisfied are you with the following?
- Cleanliness of the restaurant (INDEPENDENT VARIABLE B)
- Friendliness of the staff (INDEPENDENT VARIABLE C)
- Price of the food (INDEPENDENT VARIABLE D)
- Taste of the food (INDEPENDENT VARIABLE E)
- Speed of your order (INDEPENDENT VARIABLE F)
- Check-out process (INDEPENDENT VARIABLE 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 restaurant.
This is much different than what customers said in the open-ended comment about price and food.
However, as regression analysis proves, staff friendliness is essential.
This is likely driven by subconscious undertones of the customer experience and customers not understanding how they impact their overall experience.
If this facet of your business can be improved so all customers rate your staff a “5” on satisfaction, it is significantly more likely your NPS score will push higher than +60.
Contact Our Market Research Company
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.
Drive Research is a market research company in Syracuse, NY.
Interested in exploring regression analysis for your customer satisfaction survey? Need a quote or proposal for the work? Contact us below.
- Message us on our website
- Email us at [email protected]
- Call us at 888-725-DATA
- Text us at 315-303-2040
George Kuhn
George is the Owner & President of Drive Research. He has consulted for hundreds of regional, national, and global organizations over the past 15 years. He is a CX certified VoC professional with a focus on innovation and new product management.
Learn more about George, here.
