Nps Analyzer
Customer-feedback and experience-analytics pack for survey score data: compute Net Promoter Score (NPS) from 0-10 recommend ratings with promoter/passive/detractor breakdown, CSAT (customer...
nps-analyzer
Customer-feedback & experience-analytics adapter pack. Turns raw survey score arrays into the metrics a CX / Customer Success team actually reports — NPS, CSAT, CES, distributions, cohort comparison, industry benchmarking, and verbatim theme mining.
Intelligent microservice: every tool has a deterministic core that runs fully offline with no model. themes additionally tries an optional LLM to cluster comments into named themes and silently falls back to the keyword heuristic when no model is reachable (mode: 'heuristic' | 'llm').
Tools
| Tool | In → Out |
|---|---|
nps | { scores } (0-10) → Net Promoter Score, promoter/passive/detractor counts + %, average, rating band |
csat | { scores } (1-5 or 1-10) → CSAT % (top-box), average, normalized 0-100 |
ces | { scores } (1-5 or 1-7) → Customer Effort Score, ease %, high-effort %, interpretation |
distribution | { scores } → histogram buckets + min/max/mean/median/stdDev + most common range |
segment | { segments } (map or array of cohorts) → NPS per cohort, ranked, spread, weighted overall |
benchmark | { score, metric, industry } → industry-band rating + gap-to-good + percentile hint |
themes | { verbatims } (comments) → keyword themes, mentions, tone, sentiment tally (+ optional LLM namedThemes) |
Usage
import pack from './index.js';
pack.adapters.nps({ scores: [10, 9, 8, 7, 6, 10, 9, 3] });
// → { nps: 25, responses: 8, promoters: 3, passives: 2, detractors: 3, ..., rating: 'good' }
pack.adapters.csat({ scores: [5, 4, 5, 3, 4], options: { scale: 5 } });
// → { csat: 80, scale: 5, averageScore: 4.2, normalizedScore: 84, ... }
pack.adapters.segment({ segments: { enterprise: [10, 9, 9], smb: [7, 6, 5] } });
// → ranked NPS table + spread + weightedOverallNps
await pack.adapters.themes({ verbatims: ['Support was slow and confusing', 'Love the fast UI'] });
// → { mode: 'heuristic', themes: [...], sentiment: {...} } (mode: 'llm' if a model is reachable)Input coercion
scores accepts an array, a JSON string, or a comma / newline / semicolon separated string. Non-numeric entries are dropped. verbatims accepts an array or a newline-joined block. All tools validate and throw TypeError with a clear message on empty / non-numeric input.
DRY boundaries
- Survey-score analytics only (NPS/CSAT/CES formulae + CX benchmarking). Generic descriptive
statistics (mean, percentile, z-score, anomaly) belong to statistics / numbers — not duplicated here.
- The theme miner is a small self-contained CX keyword tally, not a general NLP engine; full
tokenizing / sentiment models live in nlp.
- No cross-pack imports except
../_shared/llm.js. Pure ESM, Node built-ins, zero npm deps.