Monthly Archives: February 2017

How Full Is The Cup? – Looking at NPS® Improvement

NPS®, or Net Promoter Score® is frequently discussed and increasingly used by technology companies. Multiple blog posts and other resources discuss the various aspects of its implementation and use, but one particular point is rarely mentioned. That point is the way to measure improvement or decline in the score

Recently I had the opportunity to discuss NPS with someone who said “we’ve increased NPS by 50%”. When I asked him to explain, his response was that their score went up from +10 to +15. Given that NPS is supposed to represent customer loyalty, do we really think that customers’ loyalty to this company increased by 50%?

In order to develop a more insightful metric for NPS improvement we should remember that Net Promoter Score is a number that ranges from -100, where all customers are detractors, to +100 where all are promoters. Therefore, our ability to increase, or indeed, decrease this number is limited by the upper and lower boundary. The top boundary is our goal, and the gap to the bottom boundary is our risk. How do we, then, calculate the journey between the two boundaries?

Several lives ago I used a system based on the concept of measuring changes along the possible range of NPS scores rather than improvement. In essence, we look at our current position and calculate how much improvement we have left. So, for a score of 80, we have 20 points of potential improvement, while at -50 we’d have 150 points. Since that rating system worked on 1-5 performance scores (1 being best), we needed to calculate 4 boundaries between those regions, while the top and bottom ones were the boundaries of the the score. The table below contains the calculations I’ve used at the time:

RatingLower BoundaryUpper Boundary
5: Worst Rating-100(100+score)*0.96-100
1: Best Rating(100-score)*0.125+score+100

There are several features for this system:

  • It assumes a score of 3 is for fulfilling the basic requirements of the position, and allows for some improvement as well as slight decline to account for volatility in the ratings
  • It also assumes that goals will be increasingly difficult to attain and harder to sustain as the score increases and adjusts accordingly

To illustrate, let’s see how this system works for several sample scores:


In accordance with the assumptions made above, the window between the lowest score for “4” rating and highest score for “2” rating shifts with the score. With lower scores, the formula is faster to penalize and slower to reward, and as the score improves the formula will become increasingly forgiving.

Obviously this system is not carved in stone. I developed it to respond to certain conditions, you should experiment with these formulas as your environment requires.

“Net Promoter, Net Promoter System, Net Promoter Score, NPS and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.”