Net Promoter Score (NPS) is a customer-feedback metric that summarizes how respondents rate their likelihood of recommending an organization, product, or service to a friend or colleague. Respondents answer on a 0–10 scale; NPS is the percentage of promoters minus the percentage of detractors, producing a score from −100 to +100. It summarizes the balance of response groups, but does not explain why people chose their ratings or prove that a business will grow.
How NPS is defined
The standard NPS question asks how likely someone is to recommend a company, product, or service to a friend or colleague. It is a statement of reported likelihood, not evidence that the respondent actually made a recommendation.
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Responses use an 11-point scale from 0 to 10 and fall into three groups:
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| 9–10 | Promoters |
| 7–8 | Passives |
| 0–6 | Detractors |
This grouping is part of the standard NPS method documented by Bain & Company and Qualtrics.
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How to calculate NPS
Subtract the percentage of detractors from the percentage of promoters:
NPS = percentage of promoters − percentage of detractors
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Use the same set of valid respondents as the denominator for both percentages. Passives count in that denominator, even though they add to neither the promoter nor detractor percentage in the subtraction.
Example
If 70% of respondents are promoters, 20% are passives, and 10% are detractors, the calculation is 70 − 10 = 60. Report this as an NPS of 60 points, not as “60% of customers recommend us.” The score can range from −100, if every respondent is a detractor, to +100, if every respondent is a promoter. Qualtrics provides guidance on the NPS question and scoring and a calculation example.
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What an NPS result tells you—and what it does not
A higher NPS means the surveyed group has a larger net share of promoters than detractors under the standard categories. Organizations can track results across defined groups, such as products, stores, service teams, customer segments, geographies, or business functions, and compare them over time when the surveys and samples are reasonably comparable.
A score on its own is not a universal measure of satisfaction, a complete measure of loyalty, or proof of business success. It also cannot reveal why a respondent selected a rating. Qualtrics recommends examining experience drivers and contextual data alongside the score; an open-ended “Why?” prompt or targeted follow-up questions can help add context. Operational data may provide further evidence, but an association between an experience and an NPS result does not by itself establish causation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is there a good NPS score?
There is no universal threshold that makes an NPS “good.” Interpretation depends on who answered, the industry and customer segment, survey wording and channel, timing, response patterns, and the comparison group. A positive score only indicates that promoters outnumber detractors in that respondent pool; it does not establish that the result is strong by a relevant industry standard.
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- Who was invited, how respondents were selected, and how many responded.
- Whether question wording and the 0–10 scale were consistent.
- When and through which channel the survey was conducted.
- Whether the industry, geography, and customer segment are comparable.
- Whether qualitative reasons or other experience measures accompany the score.
Use a benchmark only when its population and survey method are relevant to the result being judged. A bare score comparison can mislead when these conditions differ.
How survey design affects interpretation
Sampling and response bias can distort results, and small or mixed samples can make them volatile. Bain’s methodology guidance emphasizes identifying the right respondents and interpreting granular measurements carefully. In a business-to-business survey, one contact may not represent an entire account: decision makers, influencers, and users can have different experiences. Breaking results into smaller groups can expose operational differences, but those groups need enough responses for conclusions to be stable.
There is also scholarly debate about the metric’s limits. Brendan Rocks’s 2016 paper discusses challenges to claims that NPS is superior to other loyalty measures and the possibility that reducing an 11-point scale to three categories loses information. Nicholas I. Fisher and Raymond E. Kordupleski’s 2018 paper presents a critical perspective on NPS as a high-level market-research measure. These critiques are reasons to treat NPS as one useful summary measure rather than a complete or uncontested account of customer loyalty.
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