Does your company have just one Net Promoter Score? Or perhaps you have a few from different stages of the customer journey? And then this score is used to make significant business and/or customer experience decisions? This methodology may be beneficial for some of your customers – but it may be the opposite for others. The reason is that different customer segments have different NPS scores.
As a quick review, NPS’s best utilization is as a gauge of predicted organic growth. This metric correlates strongly with customer experience and customer satisfaction. Digging into the data more deeply may reveal different NPS scores with different customer segments such as:
Men versus women
New customers versus long-time customers
Younger customers versus older customers
Customers who received a promotion discount versus customers that did not
And the list goes on…
How can a company determine whether these differences exist and what to do about them? The answer is in the data structure and the analysis. The following best practices are needed to mine this valuable insight:
NPS scores must be tied to customer records in your CRM database. If your NPS survey is a third-party application such as Survey Monkey with no integration to your CRM, you are cutting yourself short. And of course your CRM data must be clean and current.
Once you make sure NPS and CRM are integrated, you now need to research various segmentation correlations. The first step in doing this is developing the most “common-sense” hypotheses about your customers. What segments act differently? Do you send different marketing messages to different customers? What core segments are you already grouping your customers in? These are the first places to start.
The next step is to run correlations with your NPS scores and CRM data to see if there are any statistically significant differences. The more customers in the analysis the better as your data will be more directional.
The final step is to take your learnings from this analysis and follow-up on them with additional research such as focus groups or follow-up surveys to a specific segment.
For a hypothetical example, let’s say a company runs this analysis and finds that their NPS with men is 10 points lower than with women. That is a big gap! A deep dive analysis into the qualitative data (the second NPS question about ‘what factor led to your score in the first question’) will result in some early trends that will be more pronounced in this segment than the overall customer base.
These trends can then be further validated with additional research – or more importantly resolutions to these trends can be identified. The outcome of this analysis could result in any number of great changes to customer experience across marketing, product development, customers support, etc.
These efforts will take time and energy but in the end, they will increase company profitability by increasing your NPS score. More specifically, this process allows you to target your lowest scoring segment in order to turn them from a detractor to an advocate!