How the score is built
Net Promoter Score comes from one question — how likely are you to recommend us, on a scale of 0 to 10 — and one piece of arithmetic:
| Answer | Group | Counts as |
|---|---|---|
| 9–10 | Promoters | +1 |
| 7–8 | Passives | 0 |
| 0–6 | Detractors | −1 |
NPS is the percentage of promoters minus the percentage of detractors, expressed as a number from −100 to +100. Passives count in the denominator but contribute nothing to the result, which is the first thing worth understanding about it.
The number nobody reports: the margin of error
This is what separates this calculator from most others. NPS is an estimate from a sample. Unless you surveyed every customer you have, the score you computed is not the score of your customer base — it is one draw from a distribution.
The width of that distribution is calculable. Because promoters and detractors come from the same sample and are mutually exclusive, the variance of the difference is:
Var(NPS) = [ p_prom + p_det − (p_prom − p_det)² ] ÷ n
The subtracted term is the part naive calculations miss. Treating the two proportions as independent samples would overstate the error, because a respondent who is a promoter cannot also be a detractor.
| Responses | Margin at 95% | What that means |
|---|---|---|
| 50 | ≈ ±20 points | Almost nothing is distinguishable |
| 100 | ≈ ±14 points | Only very large moves are real |
| 400 | ≈ ±7 points | Quarterly trends become readable |
| 800 | ≈ ±5 points | Month-to-month movement means something |
The consequence is uncomfortable and worth stating plainly: a company reporting "our NPS went from 31 to 42 this quarter" on 120 responses has measured a change that is statistically indistinguishable from zero. The presentation deck says the score improved. The statistics say nobody knows.
Why the margin shrinks so slowly
Error falls with the square root of sample size, not with sample size. Going from 100 to 400 responses — four times the survey effort — halves the margin. Halving it again takes 1,600.
That is why chasing ever-finer precision on NPS has poor returns past a point, and why the useful move is usually the opposite: accept a wider interval, survey less often, and compare against a longer baseline rather than the previous month.
What the number does not tell you
- Improvement can be invisible. Moving a customer from 4 to 7 is a real fix to a real problem, and NPS does not move at all — both are counted the same as any other non-promoter. A team optimising the headline number is incentivised to ignore that customer entirely.
- Two very different companies can score identically. 60% promoters and 20% detractors gives +40. So does 40% promoters and 0% detractors. The first has an active problem with a fifth of its customers; the second does not. The distribution matters more than the score.
- Who answers is not who you serve. Survey response rates run in the single digits, and the people who respond are systematically the ones with a strong opinion. Non-response bias is usually larger than the sampling error the interval measures — and, unlike sampling error, it does not shrink with more responses.
- It is not predictive on its own. The original claim tied NPS to growth. Later replication has been mixed, and the relationship varies by industry. Treat it as one signal among several, not as a target to manage toward.
Comparing scores, and when not to
Cross-company comparison is where NPS is abused most. Two systematic distortions make most published comparisons meaningless.
Culture changes how people use a scale. Respondents in some countries avoid the extremes, so a genuinely satisfied customer answers 8 rather than 10 — and lands in the passive bucket, contributing nothing. Scores from Japan and the Netherlands run structurally lower than scores from the United States for the same underlying satisfaction. Comparing a global average against a US benchmark is comparing two different measurements.
Method changes the sample. An in-app prompt shown right after someone completes a task successfully will produce a much higher score than an email survey sent to the full customer list, including people who churned. Neither is wrong; they are measuring different populations. When a vendor publishes an industry benchmark, the method behind it is rarely stated.
The comparison that survives both problems is your own score against your own score, collected the same way, with the interval shown.
Getting more out of the same survey
The score itself is the least informative thing the survey produces. Three things are worth more:
- The full 0–10 distribution. A bimodal shape — a cluster at 9–10 and another at 0–2 — is a completely different business situation from everyone bunched at 7–8, and both can produce the same NPS.
- The follow-up question. "What is the main reason for your score?" is where the actionable information lives. The number tells you something changed; the text tells you what to fix.
- Segments. NPS by plan, tenure or acquisition channel usually reveals that the aggregate score is an average of two very different populations. But segment carefully: splitting 200 responses into four segments leaves each with a margin near ±28 points.
Privacy
Everything runs in this tab as arithmetic. Nothing is uploaded, nothing is logged and there is no account — which matters because response counts and satisfaction data are usually internal figures.
Frequently asked questions
Why does this calculator show a confidence interval?
Because NPS is an estimate from a sample, not a measurement, and presenting it as a single exact number is how teams end up making decisions on noise. With 100 responses the 95% margin is roughly ±14 points. A team that reports moving from 31 to 42 has, with that sample size, measured nothing distinguishable from no change at all. The interval makes that visible instead of hiding it.
How many responses do I need?
It depends on your answer mix, which is why the tool computes it for you. As a rough guide, getting the margin down to ±5 points typically takes 700 to 900 responses; ±10 points takes around 200. Below 100 responses the margin is usually wider than any month-to-month movement you would want to act on.
Why are 7 and 8 excluded from the score?
By definition passives are counted in the denominator but contribute nothing to the numerator. The split is a convention Fred Reichheld introduced in 2003, not a statistical derivation. Its practical effect is that a company can improve — moving detractors up to 7 — and see no change in NPS at all. That is a known limitation, and a reason to look at the full distribution rather than the headline number alone.
Can I compare my NPS to another company's?
With care. Scores are not comparable across countries, because response styles differ systematically — respondents in some cultures avoid the extremes of a scale, which compresses NPS downward regardless of actual satisfaction. They are also not comparable across survey methods: an in-app prompt after a successful action produces a very different sample from an email to your whole list. The most reliable comparison is your own score against itself, measured the same way over time.
Is my data sent anywhere?
No. Every calculation runs in your browser as arithmetic. Nothing is uploaded, nothing is stored, and there is no account.