How to Get More from Your NPS Score with Predictive Analytics

How likely is it that you would recommend [us] to a friend or colleague? It’s the question heard round the world - so to speak - thanks to the Net Promoter® Score [NPS]. Loved by management in companies large and small for its big-picture gauge, it also earns accolades for being so intuitive and easy to implement. But is it enough? After all, NPS comes from just one single question on a survey. But, when combined with predictive analytics, NPS can give you a more robust understanding of your customers and what is driving their loyalty.

Defining Net Promoter® Score

NPS measures customer advocacy and is calculated based on the answers to the question: “How likely are you to recommend?” Customers answer on a scale from 0 to 10 and their answers are grouped as follows:

  • Detractors (score 0 to 6) — Unhappy customers who could spread negative word-of-mouth
  • Passives (score 7-8) — Satisfied but indifferent customers who may be vulnerable to competitors
  • Promoters (score 9-10) — Loyal customers who will keep buying and will refer you to others

You then subtract the percentage of Detractors from the percentage of Promoters and - voila - you have your NPS score. The higher the score, the more customer advocates you have, the lower the score… you get the idea. You can use NPS to benchmark against the competition and to help drive business growth.

Your Score is Changing… But Why?

In short, any experience your customer has with your product or service can affect your NPS. It could be anything from your food taking too long to your plane being too hot to your customer simply being more irritable that day. To know for sure, you need to dig deeper. By relying solely on the Net Promoter Score, you miss any understanding of ‘the why.’

In order to get the answer, you need to understand the attributes or touchpoints that are driving your customer’s willingness to recommend or not. Let’s focus on the ‘or not’ for a moment as that’s something many overlook in the continuing quest to drive NPS higher.

Take, for example, a health insurance plan that’s trying to increase their score. One of the things they are looking at is whether doctors in their plan are spending enough time with their patients. If the answer is already ‘yes’ according to most of their respondents, this attribute would seemingly not have a large influence on their NPS in terms of driving it higher. BUT, what if the doctors stopped spending enough time, how would that influence the score? It might have a huge impact causing the score to decrease dramatically.

This really underscores the importance of not only looking at the ‘why,’ but also the ‘why not.’ Predictive analytics can help.

How Predictive Analytics Can Add to the Story

Predictive analytics encompasses a range of analytical and statistical techniques that help in developing models that are used to predict a future outcome, or explain why something happened

In order to gain this insight, additional touchpoint questions, beyond the NPS question, must be asked. For example, your NPS question might be: “How likely are you to recommend our airline based on your recent flight?” To get data for predictive analysis you might also ask some questions around the experience such as:

  • How easy was check-in?
  • Did the flight leave/arrive on time?
  • How nice was the flight attendant?

From there, predictive analytics can help you segment the pool of respondents into groups or personas to understand who they are and then look for the advocacy drivers that are most important to these groups.

To illustrate how predictive analytics helps to predict future advocacy and how likely customers are to repeat or renew their purchase, we’ll continue with our airline example. Let’s say you are now able to segment your customers in to frequent-flyer and infrequent flyer groups. For the frequent-flyers, you learn that ‘ease of check-in’ is a key driver in whether they will recommend you, yet your experience is cumbersome for them. Now you know that streamlining the check-in process could increase your NPS among this group.

You might also learn that advocacy for infrequent flyers is driven by whether their flight leaves and arrives on time. Thus, looking at improvements to your on-time track record could increase your NPS among this group.

Again, without this additional insight you might simply be guessing as to how to improve your score causing you to waste time and resources tweaking the wrong drivers.

Net Promoter Score is a proven tool in understanding customer satisfaction, but without the context or the ‘why’ that’s driving your customers’ satisfaction, you’re not getting the whole story. We can help you fill in the gaps with predictive analytics.

To learn more, contact Ironwood Insights Group today.


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