Core Retention Metrics Leading

NPS (Net Promoter Score)

NPS (Net Promoter Score): A measure of customer loyalty based on likelihood to recommend.

Also known as: NPS

Formula

NPS = % Promoters - % Detractors

Computable definition

The canonical formula in the GASP formula grammar. The MCP server compiles it to SQL over the datasets listed below.

(count(field.nps_score where field.survey_type = 'customer' and field.nps_score >= 9) - count(field.nps_score where field.survey_type = 'customer' and field.nps_score <= 6)) / count(field.nps_score where field.survey_type = 'customer') * 100

Unit: score

Notes: Reported as a score from -100 to 100, so the x 100 is part of the definition.

Data requirements

Grain: Per survey response · Frequency: Per response · Aggregation: event · Staged: no · Main source: Survey Tool.

Field	Dataset	Type
`nps_score`	survey_response	integer
`survey_date`	survey_response	date
`survey_type`	survey_response	string

Example SQL (Postgres)

SELECT ((CAST(((SELECT COUNT(nps_score) FROM survey_response WHERE survey_date >= :period_start AND survey_date <= :period_end AND (survey_type = 'customer' AND nps_score >= 9)) - (SELECT COUNT(nps_score) FROM survey_response WHERE survey_date >= :period_start AND survey_date <= :period_end AND (survey_type = 'customer' AND nps_score <= 6))) AS DOUBLE PRECISION) / NULLIF((SELECT COUNT(nps_score) FROM survey_response WHERE survey_date >= :period_start AND survey_date <= :period_end AND survey_type = 'customer'), 0)) * 100) AS nps

Access: recommended sensitivity tier internal, access scope unscoped. Guidance your policy engine enforces.

From an agent: with the GASP MCP server, call lookup_metric({ "name": "NPS (Net Promoter Score)" }) or generate_query({ "metric": "NPS (Net Promoter Score)", "adapter": "<your adapter JSON>" }).

Benchmarks

Below 0: More detractors than promoters
0-30: Average (B2B SaaS average: 36-41)
30-50: Good
50-70: Excellent (top performers)
70+: World class

What It Tells You

A leading indicator of retention and growth. A falling NPS often shows churn risk before it reaches the revenue metrics, early enough to act. Bain & Company research links NPS to organic growth through referrals and reduced churn.

Common Mistakes

Low response rates (<20% makes data unreliable)
Survey fatigue from over-asking
Not closing the loop with detractors
Comparing across industries (benchmarks vary significantly)

Connected in the GASP relationship graph.

Upstream: what drives this

CSAT drives NPS (Support)
Uptime drives NPS (Engineering)

Downstream: what this drives

NPS predicts Logo Churn Rate (Core)
NPS predicts Churned MRR (Core)

View canonical definition →

Sources

← Back to Core metrics