Key Driver Analysis: Find out what’s powering preference

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Posted Oct 26, 2022
Eliza Jacobs

Do you know why your most loyal customers keep returning to your brand? Do you know what’s keeping other people away? When times are good, you may think you know what customers like about your products, but you may be in for a rude awakening when your target market changes their minds.

To stay ahead of the shifting opinions of consumers, you need a method of statistical analysis that reveals the key driving factors behind their preferences and actions. Fortunately, that’s exactly what a key driver analysis is designed to do. Here, we explore how this analysis works, its benefits, and how aytm can help.

What is a key driver analysis?

Key driver analysis is a statistical methodology for identifying the derived importance between various potential drivers and customer behavior and attitudes. Likely drivers could include the product’s features or characteristics. 

Key driver analysis works by measuring the relative importance of potential drivers as they relate to consumer behavior, which could include customer satisfaction, purchase intent, or customer loyalty.  Using statistical techniques, including multiple linear regression, a model is used to estimate the relationship between two or more independent variables (potential drivers) and dependent variables (in this case, customer opinion).  

This kind of analysis can provide background to customer satisfaction scores. You may know how your customers feel about your products, but if you can understand the why behind those feelings, you can build a more resilient brand. A key driver analysis will reveal the areas you can focus on to improve the customer experience. Furthermore, it can reveal new opportunities and trends for your company to take advantage of. 


Let’s consider an example of how a key driver analysis could be used for a pharmaceutical product. First, you would identify all the possible attributes or factors that could impact one or more key outcomes for your customers (for example, likelihood to recommend, brand satisfaction, likelihood to re-engage/repurchase, loyalty, or even selection of a brand among a competitive set). Then, you would turn these drivers into a scaled evaluation. Common scales on which to measure predictor variables include satisfaction and performance, but NOT importance (rather, importance is derived through the analysis). These questions could then be compiled into an online survey and sent to your target market. 

Key drivers analysis results will provide you with percentages demonstrating how each factor influences your customer’s behavior. These factors could include whether the product met the customer’s expectations for performance, safety statistics, low risk of side effects, and the aesthetic of the packaging. These percentages can then be mapped using the regression analysis we previously  discussed. This analysis will reveal the fundamental drivers. Sometimes respondents will list an attribute as highly important when they do not feel strongly about it. Other times, they may describe a driver as less important than it is to them. This linear regression analysis allows you to understand better what is actually driving our customers. 


The primary benefit of a key driver analysis is that you can get to know your customers better. The simple fact is that predicting consumer behavior is challenging. You may see a competitor doing well and notice that they offer lower prices. This could lead you to think that lowering your prices would make you more competitive - this is not necessarily the case. Then, your competitor still beats you. 

Perhaps, by conducting a key driver analysis, you would have found that prices were not nearly as important to your customers as having good customer service or easier-to-use products. Instead of relying on vague assumptions about what motivates them, you can have reliable quantitative data that can drive your decision-making. 

Another benefit of using a key driver analysis is that it allows you to narrow your focus to those areas that matter most to your customers. These factors are also the most likely to have a high ROI for the improvement effort you put into them. With the economy in such turmoil, many businesses are left with limited budgets. A key driver analysis can help guide you to where your investment will do the most good for your business. 

When to do it

There are several critical areas that a key driver analysis is perfect for measuring. These include: 

  1. Customer satisfaction
  2. Positioning
  3. Customer base expansion
  4. Customer loyalty and retention
  5. New product testing
  6. Purchase intent
  7. Likelihood to recommend

For example, if you’re designing a new product, you’ll want to know which aspects or features  of your new product are likely to make it successful. Furthermore, if there is anything you need to change about your product, a key driver analysis can reveal it. This methodology is also helpful in developing your brand’s position - how are you  perceived and what elements of your brand can you  bring to the forefront to keep customers returning to you? 

As we have already mentioned, a key driver analysis goes deeper than just the “how” to also reveal “why” a customer is satisfied or not with your products or services. 

How to conduct a key driver analysis

If you’d like to conduct your own key driver analysis, there are a few steps you’ll need to take. The first step, as we’ve already mentioned, is to field a study to your designated target market. In this survey, you will  identify all the possible variables impacting your customers’ experience. In order to set up a survey for key drivers analysis, respondents must provide:

  • An evaluation for a set of product or experience attributes that are of interest for predicting one or more key outcomes. Common scales on which to measure predictor variables include satisfaction and performance, but NOT importance (rather, importance is derived through the analysis).
  • An evaluation of any key outcomes for which we seek to identify key drivers (e.g., likelihood to recommend, brand satisfaction, likelihood to re-engage/repurchase, loyalty, or even selection of a brand among a competitive set).

This type of analysis usually begins with a performance calculation where you add up the total performance of each factor and calculate percentages, giving you a score for each category. This step aims to identify whether or not there is a correlation between any of the factors. The correlation data helps us to recognize how important each factor truly is. For example, if your excellent customer service contributes to a positive view of your brand and even your product’s aesthetics, you would recognize just how vital it is for your business to get service right.

Once you have completed this step, you are ready to begin the analysis. Here, the role of predictor variables on the outcome variable is assessed through multiple regression with relative importance analysis layered on top. The primary benefit of relative importance analysis is that it provides a decorrelated view of the importance of each predictor attribute on impacting the outcome variable. The decorrelated approach is useful in that it provides distinct and unique contribution values for each of your predictor variables, which helps to prioritize areas of product/brand experience to focus on in order to most efficiently impact your brand KPIs.

How aytm can help you run a key driver analysis

With aytm’s Key Drivers Analysis, you can complete this market research with greater depth, quality, and efficiency than ever before. aytm will deliver a custom report that provides critical insights through clear graphics and the written word. You can get rapid access to the most vital KPIs such as likelihood to purchase, customer satisfaction, likelihood to recommend, and customer loyalty. The report will highlight those variables which are genuinely the critical drivers of your customer’s behaviors and preferences.

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