Using Regression Analysis To Understand Core Drivers In Your Customer Loyalty Survey
First things first: Whatâs it?
Regression analysis is a method that helps you determine which factors have the most significant impact on an outcome.
In the context of a customer loyalty survey youâve built using HubSpot, the outcome we are focused on is the Net Promoter Score (NPS) â the metric used to gauge how likely customers are to recommend your store. While NPS gives you an overall view of customer loyalty, regression analysis allows you to go deeper and uncover what influences that score.
For instance, your customer surveys might ask about product variety, staff responsiveness, or value for money. But simply looking at the responses to these questions doesnât tell you which factors truly drive NPS. Regression helps you break this down and identify the most important contributors.
***Weâll use the terms customer loyalty and NPS interchangeably in the article; therefore, this also applies to the âNPS surveyâ and âcustomer loyalty survey.â
Imagine youâve collected data from a customer loyalty survey where you asked a set of questions about different aspects of the customer experience, such as:
How do you perceive the variety of products we offer? (Rating 1â10)
How frequently do you shop at our store? (Frequency per month)
How responsive and knowledgeable did you find our staff? (Rating 1â10)
How would you rate the overall value for money? (Rating 1â10)
On a scale of 0 to 10, how likely are you to recommend our store to friends and family? (This is the NPS question)
Letâs assume youâve collected over 100 responses. For each response, you have ratings on these different aspects and an NPS score.
If you were to look at the raw survey data, you might be able to see patterns in how customers rate their experience. For example, you might notice that customers who rated staff responsiveness highly also tended to have higher NPS scores. But is staff responsiveness really the most important factor affecting NPS? What about value for money or product variety?
This is the challenge of analyzing surveys â simply looking at averages or individual responses doesnât give you the whole picture. Itâs hard to know which factors are statistically significant drivers of customer loyalty and how much each factor influences NPS.
But isnât customer loyalty analysis straightforward?
At first glance, it seems that way, of course. You collect feedback from your customers, calculate their Net Promoter Score (NPS), and categorize them as Promoters, Passives, or Detractors based on their likelihood to recommend your store. If your NPS is high, everythingâs going well; if itâs low, you need to improve. It sounds simple enough, right?
The reality, however, is that many factors influence customer loyalty, and focusing solely on the NPS score doesnât give you the whole picture. While calculating the NPS score is easy, figuring out why customers rate their experience the way they do and which aspects of that experience drive their loyalty (or lack thereof) is much more complex.
Most marketers stop at basic NPS analysis:
They take the average NPS and compare it to industry benchmarks.
They calculate the percentage of Promoters, Passives, and Detractors, assuming this gives them a clear snapshot of customer satisfaction.
Some might take broad, generalized actions based on the score, such as improving customer service if the NPS is low or enhancing product offerings if they believe thatâs the issue.
While this type of analysis might seem sufficient, it often isnât enough because it doesnât explain why customers give these scores.
So, what questions should you be asking?
Well, youâll need to be asking questions that help you go beyond basic reporting:
Are Promoters happy because of the great product variety, or is the responsive staff that matters most to them?
Itâs possible that customers who gave you a high NPS score did so because they loved your wide range of products. But, what if staff responsiveness played a more significant role than product variety in their overall satisfaction? If you donât ask the right questions and analyze the data carefully, you might assume product variety is the key driver of customer happiness when, in fact, itâs the interactions with your staff that truly stand out for them.
Are Detractors unsatisfied with pricing issues, or is the store layout confusing?
Customers who give low NPS scores might cite pricing as a reason for dissatisfaction, but is that the primary issue? Are they frustrated because your storeâs layout makes it hard to find what theyâre looking for? Maybe pricing isnât the main pain point, and improving the shopping experience would lead to more impactful changes in their satisfaction.
To illustrate how regression analysis can answer some of these questions, letâs walk through an elaborate example using customer survey data.
How is it relevant for HubSpot data?
For instance, letâs examine Customer Feedback and how regression analysis can enhance your understanding of customer loyalty survey (or similar) data.
Table 1: A glimpse of the customer loyalty survey dataset
Product Variety:Â How do customers perceive the range of products you offer?
Shopping Frequency:Â How often do they visit your store?
Store Atmosphere:Â What do they think about the overall ambiance?
Staff Responsiveness:Â How helpful and knowledgeable did they find your team?
Value for Money:Â Do they feel theyâre getting a good deal?
Store Navigation:Â How easily can they find what they need?
Overall Satisfaction:Â Their general feeling about the shopping experience
Age Range:Â Which age group does the customer belong to?
Notification Preferences:Â Are they interested in receiving updates about special offers?
Net Promoter Score (NPS):Â How likely are they to recommend your store to friends and family?
As youâre already familiar, the golden nugget in this dataset is the Net Promoter Score (NPS). This score tells you how likely a customer is to recommend your store, a powerful indicator of customer loyalty and potential business growth.
But hereâs the exciting part: we want to uncover which factors strongly influence this score. Is it the friendly staff? The great deals? Or perhaps the wide range of products? By analyzing this data, youâll gain valuable insights into what really matters to your customers.
As a marketer, understanding these drivers can change how you go forward with your marketing approach/efforts. As weâve already laid down before, hereâs what it enables you to do:
Focus your efforts on what truly impacts customer loyalty
Customize your marketing messages
Identify areas for improvement that will have the most significant impact on customer satisfaction/loyalty!
Essentially, you wouldnât have to shoot arrows in the dark.
Okay, so whatâs regression analysis, and how does it tie?
Regression analysis is a statistical method thatâll help you understand how changes in one or more factors (called independent variables) affect another factor (called the dependent variable).
The independent variables are things like product variety, store atmosphere, and staff responsiveness.
Our dependent variable is the Net Promoter Score (NPS).
Letâs break it down step by step:
Regression analysis looks at how each factor (like store atmosphere) relates to the NPS. Itâs like asking, âWhen store atmosphere scores go up, does NPS tend to go up too?â
The analysis doesnât just tell us if thereâs a relationship; it tells us how strong that relationship is. Itâs like saying, âFor every point increase in store atmosphere, NPS tends to increase by X points.â
All of this information is combined into a mathematical formula, which can predict NPS based on other factors.
We use this formula on a portion of our data to see how well it predicts NPS. This tells us how reliable our formula is.
Letâs say we come up with this formula:
NPS = 2 Ă (Staff Responsiveness) + 1.5 Ă (Value for Money) + âŚ
Improving staff responsiveness by 1 point could increase NPS by 2 points.
Improving perceived value for money by 1 point could increase NPS by 1.5 points.
But we donât know that yet. Thatâs what weâre about to find using regression analysis.
Perfect, so how does this work?
Weâre using statistical modeling, specifically machine learning (ML) regression, as a substitute for the âformulaâ we mentioned earlier. Instead of manually calculating how each factor affects NPS, we let the model do the work. (Please note that the code for the regression analysis is beyond the blogâs scope for now!)
The model analyzes customer survey data (Table 1, 1000 rows) to understand how factors like Overall Satisfaction and Value for Money influence NPS (remember the independent and dependent variables we discussed?). The model looks for patterns and assigns weights to each factor, showing how much each impacts NPS.
Table 2: Results of the regression analysis
Take a look at Table 2. What do you observe?
Overall Satisfaction (0.608): For every 1-point increase in overall satisfaction, NPS tends to increase by 0.608 points. This makes Overall Satisfaction the most powerful predictor in the model.
Value for Money (0.401):Â A 1-point improvement in perceived value for money could increase NPS by 0.401 points. This is the second most important factor in determining NPS.
What about other factors?
Store Atmosphere and Store Navigation have minimal positive effects on NPS, with increases of just 0.003 points and 0.001 points, respectively, for each point increase in their scores.
Fig 1: Correlation matrix between all your variables
Take a look at Fig 1. The correlation matrix visually shows how different factors from the survey are related to each other and NPS. This is a visually friendly way to understand which factors are most strongly linked to customer loyalty. For example, our observation of Overall Satisfaction and NPS having a very high correlation (0.82) is reflected in the correlation matrix as well. Similarly, Value for Money (0.54) and Staff Responsiveness (0.77) also show positive correlations, indicating that focusing on these areas can help improve your customer loyalty.
Take a look at Table 1. Whatâs the R2 score at the top?
The R-squared (R²) score is a way to measure how well a regression model (or âformulaâ) explains the variability in the data. In simple terms, itâll tell you how much of the changes in NPS can be predicted based on the factors weâre analyzing, like Overall Satisfaction and Value for Money. An R² score of 1 means the model perfectly predicts NPS, while an R² of 0 means the model explains none of the variability.
For example, if our R² score is 0.98, it means the factors in the model can explain 98% of the changes in NPS. This is super important because the higher the R² score, the more confident you can be in the modelâs predictions.
You can also predict the NPS score from here on
Now that you know the formula from the model, you can also predict the NPS score for any customer. The model takes all the important factors â like Overall Satisfaction and Value for Money â and tells you exactly how much each one impacts NPS. As we saw, if Overall Satisfaction increases by 1 point, NPS goes up by 0.608 points. So, if you know a customerâs satisfaction score, how they feel about value for money, and other details (i.e., the impact of each variable), you can plug those into the formula and predict their NPS score.
Though itâs not super important or a replacement for your customer loyalty/NPS survey, itâs a good note!
What are the different methods for regression analysis?
Now that we understand what regression analysis is, letâs explore some common types.
Think of these as different tools in your data analysis toolkit. Just as you wouldnât use a hammer for every home repair job, different regression methods are suited for different data types and questions.
This is the simplest and most common type of regression. It looks for a straight-line relationship between variables.
You expect a straightforward, linear relationship between variables
The dependent variable (like NPS) is continuous.
When you want to predict a numerical outcome.
For example, youâll use linear regression when you want to understand how an increase in customer service rating directly relates to an increase in NPS.
Youâll use this technique when the outcome you expect is categorical, often binary (yes/no, true/false).
When youâre predicting a binary outcome.
When you want to classify results into categories.
Say, if we refer to the same table (Table 1), if we applied logistic regression to the same data, instead of predicting a specific NPS score, we would transform the NPS into categories, such as:
Promoters (NPS of 9â10)
Detractors (NPS of 0â6)
So, instead of predicting an exact NPS score (like 7 or 9), the model would predict which category a customer falls into â whether theyâre a promoter, detractor, or passive. So, letâs say the model has figured out this âformula,â and if you bring in more data (real-time), youâll be able to predict whether a customer will be a promoter or not (Promoter = 1, Not Promoter = 0). The logistic regression model would analyze factors like Overall Satisfaction and Value for Money and predict the probability of a customer becoming a promoter. This is especially good when youâre working with limited data and want to predict which category your customer might fall into.
Think of multiple regression as an extension of linear regression that includes two or more independent variables. Youâll want to use this technique when:
When you have multiple factors influencing your outcome.
When you want to understand the relative importance of different variables.
Fig 2: Illustration of linear regression vs. multiple linear regression taken from here
Youâll use polynomial regression when the relationship between variables is curvilinear (not a straight line). So, in this case, perhaps youâre trying to understand how customer satisfaction might increase with age up to a point and then decrease for older customers. The relationship isnât straightforward here anymore.
Letâs say youâre analyzing how age affects NPS. In some cases, the relationship between age and satisfaction might not be linear:
Younger customers (18â25) might be less satisfied, as they might be looking for trendier or more cost-effective options.
Middle-aged customers (35â45) might have higher satisfaction because the products and services match their needs.
Older customers (55+) might again have lower satisfaction due to different preferences or unmet needs.
Fig 3: Illustration of linear regression (or model) vs. polynomial regression taken from
5. Other techniques to read
If the relationship between variables is curved (like in polynomial regression in Point 4) or complex, linear regression techniques will struggle. For example, if customer satisfaction increases up to a point but then drops after a certain level, a straight-line approach wonât capture that. For example, improving staff responsiveness might help NPS up to a point, but once customers expect a certain level of service, further improvements wonât change their opinion much.
Here are three tree-based methods that are popular alternatives for analyzing complex relationships, especially when the linear methods we discussed above (like linear/multiple regression) donât fit â for example, decision trees, random forests, and gradient boost regressions:
Decision trees:Â Great for simplicity and visualization, but they can be too focused on the specific data used to build them, which may reduce their ability to perform well on new or unseen data.
Random forests:Â More reliable and stable for complex data but computationally heavier.
Gradient boosting:Â Best for accuracy in tricky data, but comes with high complexity.
***Weâre going to look at these techniques in our future blogs!
When it comes to regression analysis in marketing, you need to understand its true potential â and, just as importantly, what it canât do. While regression can provide powerful insights into customer behavior and campaign performance, some common misconceptions exist about how it should be applied. Regression analysis goes beyond simple correlations and requires a nuanced understanding of your data.
Hereâs what you need to be careful about:
Assuming that correlation always implies causation, without considering external factors or confounding variables
Applying the same regression model to all scenarios without taking the situation itself into context.
Using regression to justify your marketing decisions rather than to make your future strategies better.
Treating regression analysis as a one-time activity rather than an ongoing process
Overcomplicating your formula by including every possible marketing variable youâve captured!
Looks great! Give me a quick summary!
Regression analysis will help you pinpoint the exact factors that influence customer behavior, like which aspects of your service lead to higher NPS scores. Sure, theâ formulaâ will work if the number of variables is fewer still, as we saw above, with increasing complexity, the types of ML models we use to understand relationships between the independent and dependent variables.
Techniques like linear regression, multiple regression, and more advanced ones like polynomial regression allow you to understand and predict outcomes based on customer data â think satisfaction scores, spending habits, or even how often they engage with your brand. The more advanced methods, like random forests and gradient boosting, take it a step further by digging deep into complex, non-linear relationships.
But hereâs the thing. Continuously running these models manually and interpreting the results can be very cumbersome and challenging. Especially as your dataset grows or you start adding new variables. Your analytics team will be stuck crunching numbers, creating plots, and sending you reports filled with stats, so youâll spend hours translating data into actionable insights. All of this ultimately (and unfortunately) leads to delayed decision-making and a less responsive marketing strategy. So, whatâs the fix?
ConvertML makes your job easier and streamlined with automated regression analysis and interpretation
This is where ConvertML comes in to make your life easier. It automates the entire process of regression analysis, integrating survey data directly from HubSpot and various other sources youâd want to bring: surveys from different platforms, ticketing information, or external DBs in just a few clicks.
How is ConvertML making your job easier and more streamlined?
Streamline the entire regression analytics â automatically considering real-time data, so you donât have to run algorithms manually.
Instantly visualize your predictions with easy-to-read charts and graphs to make it easier for you to act quickly.
See how each factor impacts outcomes like NPS, with downloadable reports for a deeper dive into the details.
What is ConvertML doing that other companies arenât?
It interprets the output and provides GenAI recommendations
Once youâve used ConvertML to automate your regression analysis, the next big challenge is making sense of the output.
Sure, ConvertMLâs dashboards, charts, and reports are super intuitive and easy to understand. But what if youâre short on time? What if you need to make a decision right now?!
Well, hereâs where ConvertML goes the extra mile!
ConvertML doesnât just run regression models â it interprets the results for you using GenAI. Instead of digging through numbers and stats yourself, ConvertML instantly provides clear, easy-to-understand summaries.
But it doesnât stop there. ConvertML gives you actionable recommendations â like pointing out which factors to focus on to improve NPs (or customer loyalty). It helps you quickly pick the best options, use your insights, and get results faster!
Schedule a demo to cut through the noise and simplify customer loyalty survey analytics with ConvertMLâs automated regression analytics, GenAI summary, and recommendation.
