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The number of survey responses the current `customers.nps_score` is computed from — the confidence qualifier the board must read alongside any NPS value. Per the NPS methodology (Reichheld/Bain), a score from a small or unrepresentative sample is unreliable; surfacing the response count lets the board discount low-n scores. Common pitfall: celebrating (or alarming at) an NPS swing that is actually a sample-size artifact — always read the score and the response count together. — Customers KPI, I'mBoard-authored (editorial tier).

I'mBoard-authored (editorial tier)

No public third-party standard anchors this KPI yet, so I'mBoard authors and maintains the definition — transparently labeled as editorial tier. See the ontology methodology for the published vs editorial tier system and the back-attribution workstream.

Rogue ID: customers.nps_responses Type: Number Domain: Customers

Definition

The number of survey responses the current customers.nps_score is computed from — the confidence qualifier the board must read alongside any NPS value. Per the NPS methodology (Reichheld/Bain), a score from a small or unrepresentative sample is unreliable; surfacing the response count lets the board discount low-n scores. Common pitfall: celebrating (or alarming at) an NPS swing that is actually a sample-size artifact — always read the score and the response count together.

Formula

Count of completed survey responses behind the period's NPS score. Response rate = nps_responses ÷ customers surveyed.

Why it matters

The confidence denominator under NPS — an NPS based on <50 responses or <10% response rate should be flagged low-confidence rather than trended.

How to interpret

Read with customers.nps_score and customers.nps_trend. A trend across periods with materially different response counts may be measurement noise, not a real movement.

Calculation policy

How an AI agent should compute this KPI from messy company data. Free-text rules consumed at reasoning time — not a deterministic DSL. The most common ways to get this wrong are listed under Common miscomputations.

Inclusion rules

Count completed survey responses behind the period's customers.nps_score — the confidence denominator under any NPS value.
Count one per completed response (a usable 0–10 score) within the survey window that produced the reported score.

Exclusion rules

Partial / abandoned surveys that did not yield a usable 0–10 score.
Responses outside the survey window for the reported score.
Customers surveyed but who did not respond — those belong to the response-rate denominator, not the response count.

Required inputs

Count of completed responses for the period's survey.
The number of customers surveyed (for the response-rate calculation).

Data-source priority

Survey platform (Delighted, Pendo, in-app NPS) response export.
Manual survey tally as a fallback.

Edge cases

Multiple surveys in one period: define whether the count is the latest survey or the pooled set, and keep it consistent with how nps_score is computed.
A response count materially different from prior periods means a trend may be measurement noise, not a real movement — flag it.

Validation checks

nps_responses ≥ promoters + passives + detractors used in the score.
Non-negative integer.
Flag the score low-confidence when nps_responses < ~50 or the response rate < ~10%.

Common miscomputations

Reporting nps_score without the response count — a swing on a tiny sample reads as signal when it is noise.
Counting customers surveyed (sent) as responses (received) — inflates confidence.
Pooling responses across surveys for the count but computing the score from a single survey (or vice versa).

customers.nps_score
customers.nps_trend

Source

I'mBoard editorial — authored and maintained by I'mBoard, first published 2026-04-01. No third-party standard is cited for this KPI; when one emerges, the definition is back-attributed and promoted to the published tier (a minor version bump). Read the ontology methodology for the published vs editorial tier system, attribution rules, and dispute process.