TLDR: If you believe in Net Promoter Score® (NPS) as a key measure of customer loyalty and future purchase intent (as I do), then you’re naturally drawn to analyzing your survey results. But are we doing it all wrong?
First, let’s assume that the basics of survey response analysis remain true:
- Mining the comments for themes of feedback
- Looking at changes in your NPS® as a trend that can be correlated to changes in your business, such as product releases (provided you’re collecting surveys all the time, as we recommend)
Beyond that, I often hear people wanting go further with segmentation analysis. Their segmentation approach is to look at results by dimensions such as region, or customer tier, or product plan, or user role (in B2B companies), or consumer demographics.
I’m not sure this analysis is meaningful, because none of this reflects the experience of how customers engage your product or service. And it’s the customer experience that determines a customer’s Net Promoter Score far more that other factors.
Rather, I suggest you adopt an “experience-first” approach to segmentation analysis.
What is experience-based analysis?
Experienced-based analysis answers the question “what interactions led to the NPS response that we received?”
The most important interaction is how customers interact with your product itself. We’ll get to the details later, but sample questions to drive your analysis might be:
- Does frequency of use correlate to better survey scores?
- Do users of certain features provide better scores?
Other interactions happen across channels such as the contact center or social platforms. These experiences are about product support, billing questions and the like. They are secondary to the product experience, because they support the use of the product but aren’t the product itself. Questions to analyze might include:
- Which secondary interactions correlate to low or high scores?
- Which interactions best enable successful adoption?
Where to get the data
If your product or service has an online component, you must collect data on every online touchpoint. Some useful sources:
- Product usage metrics, at the user and feature level
- Customer support activity, across each channel through which it’s delivered (contact center, web, social)
- Post-purchase email campaigns, including open rates and click-through rates
How to interpret the data
A good question to start with is whether frequent usage corresponds to higher NPS scores. Consider bucketing your survey respondents into quartiles, quintiles or even deciles based on how often they use your product. Does the top decile by usage frequency produce higher NPS scores? And by what degree?
The basic understanding you’re looking for is the threshold of usage frequency where NPS scores are significantly better. You can get even fancier using statistical correlation instead of quintiles or deciles. Or you can even try to isolate the features in your product whose frequent use correlates to higher NPS.
This type of segmentation analysis can be interesting because any given interaction with your support function can be positive or negative. For example, somebody who has to call you three times to resolve a billing issue is probably not happy for having done so. In contrast, somebody who calls three times for three different product how-to questions might be very thankful for the help.
This is unlike usage analysis, where you’re starting with the initial hypothesis that the more-frequent users are happier.
So, the first step is to categorize your interactions by topic, then do the frequency analysis using the same approach as product usage. Some examples:
- For support requests about product how-to’s, what is the relationship between frequency of requests and NPS score? Are some requests better than none? Are too many requests harmful to NPS? At what threshold?
- For billing requests, what is the relationship between zero, one, or multiple billing issues and NPS score?
- For product defects, what is the relationship between zero, one, or multiple reported bugs and NPS score?
If you use post-purchase emails to educate your customers on how to adopt your product, to announce new features or to communicate available support resources, then presumably these interactions are having a positive effect on NPS.
A good question to start with is whether certain topics of email communication affect NPS. Do people who actively consume certain types of content also provide better NPS scores?
To do this analysis, you would need to instrument your email delivery process to produce metrics such as “email opened” and “click through.” Look for emails that have a positive relationship to NPS scores.
Summing it up
Customer experience – the sum of all interactions you have with a customer – is a fantastic lens through which you can analyze NPS scores. What you’re likely to find are surprising relationships between specific experiences and NPS. This can give clear purpose to your customer touch points; double down on the winning experiences and try to eliminate or reduce the negative ones.