How Text Analytics can help analyse Net Promoter Score®

NumrSense AI
Text Analytics
Author Image
Divya Kandwal
December 9, 2019
mins read

Is your company bleeding money? US companies lose $75 billion dollars every year due to poor customer service. This is a pity because most of it is, in fact, preventable.  


NPS® is a metric developed by Fred Reichheld that quantifies Customer Loyalty. It is based on just 1 question, which is

“On a scale of 1 to 10, how likely are you to recommend us?”

Net Promoter Score® scale Numr Research Text Analytics customer feedback

NPS® is then calculated by subtracting the percentage of Promoters (people who rate 9 or 10) from Detractors (6 and below).

Read our comprehensive guide on how to measure NPS®, here.


Net Promoter Score® gives you an idea of WHAT is going on with your customers. It doesn’t tell you the WHY. This is where Text Analytics comes in.

Text Analytics explains WHY your customers are recommending you or WHY they are not.  

Text Analytics means analysing a continuous stream of unstructured data (feedback/comments) to derive insights.  

If you want to learn in plain English how Text Analytics is performed using Machine Learning, go here.


The days of long satisfaction surveys are almost over. Nobody has the time or will to answer a never–ending survey. In fact, survey fatigue is at an all-time high. According to a survey (yes, we can see the irony here) only 9% people answer long surveys thoughtfully. Imagine spending all those resources resulting in no actionable insights.

What’s more, these long customer satisfaction surveys with fixed questions can only cover a specific set of issues.  

C-Sat surveys fail miserably at covering emerging issues.  

NPS® is of course a logical solution to this conundrum.  

It’s clean, manageable and everyone in your organisation can understand it. The beauty of the NPS® lies in its brevity. It’s a one scale question with an open-ended feedback/comment section.  

And, open-ended text analysed using Text Analytics is crazy effective for understanding upcoming issues that need fixing as they come up.  

In fact, take it a step further and use Machine Learning to,


Basically, you can design algorithms to understand that  

When someone is talking about a specific issue (like delayed delivery), how does that affect recommendation.  

(Side Note- We have a short article on how we used Text Analytics to prioritise decision making for a client, here.)

This gives you an idea of the most important issues that might make your customers leave. Fix them first. What’s more, using Predictive Analytics and home historical Sales data, you can even predict Financials to be on top of things.

In short, Text and Predictive Analytics tells you everything that is going on with your customers early enough for you Close Loop and retain them.  

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