What is Net Promoter Score, how is it calculated and what are its limitations?

Net Promoter Score, or NPS, is an indicator based on the answer to one question: how likely are you to recommend a company, product or service? The scale runs from 0 to 10. The score summarises stated propensity, but it does not directly measure satisfaction, loyalty or growth.

Its simplicity makes comparison and adoption easier. That same simplicity makes it easy to misuse. Without knowing the sample, timing and reasons behind the answers, a high or low number offers little practical direction.

How do you calculate Net Promoter Score?

Answers from 9 to 10 are classified as promoters, 7 to 8 as passives and 0 to 6 as detractors. Subtract the percentage of detractors from the percentage of promoters. The result ranges from -100 to 100.

If 50 percent answer 9 or 10, 30 percent answer 7 or 8 and 20 percent answer from 0 to 6, NPS is 30. Passives are part of the denominator but do not enter directly into the subtraction.

Do not call it a percentage: it is a score. Report the sample size, period and collection method. An NPS of 50 based on ten responses does not have the same stability as the same score from a large, representative sample.

When should you ask the NPS question?

You can run a relational survey at regular intervals or send a request linked to a specific moment. The first observes overall perception; the second can follow an experience, but it may mainly reflect that single event.

Choose a point when the customer has enough experience to answer. Avoid asking immediately after an interaction designed to create excitement or while a problem remains unresolved, unless that is the subject of the research.

Monitor survey pressure. Asking too often reduces response rates and quality. Keep the questionnaire short and add an open question about the reason for the score.

How should you interpret the score?

Segment carefully by customer, product, channel or stage. Look for consistent differences and read the comments. Do not compare departments that collect responses at different times. An external benchmark offers context only when its method, market and sample are comparable.

The absolute figure is less useful than its relationship with causes and behaviour. Do promoters actually recommend? Do detractors leave more often? Which themes recur? Connect surveys, retention, churn, purchases and tickets without assuming that correlation proves causation.

The article in which Fred Reichheld popularised the method argued for a link between recommendation and growth, but subsequent research has questioned how widely that relationship applies. For this reason, read the original Harvard Business Review proposal as the origin of the model, not a universal guarantee.

A note on comparability

If you change the wording, scale, channel or timing, record it in the historical series. Even an apparent improvement may result from different people responding. Keep the method stable when observing a trend and test changes within a separate group.

Alongside the score, share the sample size, response rate, distribution of scores and themes that emerged. This transparency prevents a single figure from being used to present certainty that the data does not possess.