The AnswerShare™ NPS family — μNPS™, λNPS™, and ΔNPS™.
Three metrics, one methodology stack. μNPS is the human community baseline reconstructed from the public corpus. λNPS is what AI actually says about the brand, measured live across five frontier LLMs. ΔNPS is the signed gap between them — the diagnostic signal that surfaces AI amplification or suppression. ΔNPS = λNPS − μNPS. All three live on the canonical NPS scale of −100 to +100. Published 2026-05-26. Record as of .
Three NPS-family metrics at a glance
All three live on the canonical NPS scale of −100 to +100 so they can be compared directly.
- μNPS — Human Community Opinion
- Measures the human community's opinion of the brand online — the corpus baseline AI systems are trained against. Reconstructed from review sites, retailer reviews, community forums, and BBB complaint records.
- λNPS — AI Opinion
- Measures the AI's opinion of the brand in live retrieval. Direct measurement against five frontier LLMs (Perplexity, OpenAI, Gemini, Claude, Grok), sentiment-classified to the canonical NPS scale.
- ΔNPS — The Difference
- ΔNPS = λNPS − μNPS. Positive = AI rates the brand better than the human community does (amplification). Negative = AI rates the brand worse (suppression). The diagnostic signal GEO optimization moves.
μNPS — modeled corpus reputation
μNPS — mu-NPS — is the modeled reputation score reconstructed from the observable public corpus about a brand. The Greek letter μ (mu) denotes modeled: the score is computed from sentiment-classified signals found in public sources, not from a direct customer survey.
The output is an NPS-style scalar in the range -100 to +100, computed as the weighted share of promoter signals minus the weighted share of detractor signals. A score of +40 means the public corpus is meaningfully net-positive; -20 means detractor signals outweigh promoters even after recency weighting.
μNPS is reconstructed, not surveyed. It approximates what an NPS survey would show if every public mention of the brand were treated as a survey response weighted by source credibility and recency. It exists to anchor the ΔNPS diagnostic.
μNPS data sources — four ingestion adapters
μNPS pulls from four corpus sources today. Each adapter normalizes its upstream payload into a row on the mnps_signals table with a sentiment classification (promoter / passive / detractor), a base weight from mnps_weight_config, and the original published_at timestamp.
Source coverage will expand. Roadmap candidates: G2, Capterra, Glassdoor (employer), app store reviews, YouTube comment sentiment.
- Trustpilot (mnps-ingest-trustpilot, review)
- Consumer review platform. High-volume, sentiment-rich, structured rating (1-5 stars) maps cleanly to promoter / passive / detractor classification.
- Better Business Bureau (mnps-ingest-bbb, complaint)
- Complaint registry plus BBB letter-grade. Lower volume than reviews but high signal-credibility, particularly for service-sector and consumer-finance brands.
- Amazon Product Reviews (mnps-ingest-amazon, review)
- Retailer reviews for brands that sell on Amazon. Star rating maps to classification; verified-purchase flag is preserved in raw metadata for downstream filtering.
- Reddit Mentions (mnps-ingest-reddit, mention)
- Brand mentions on Reddit threads. Treated as community sentiment; classification derived from sentiment analysis on the mention text rather than a star rating.
μNPS computation — weighted NPS with exponential recency decay
For each signal i: age_days_i = (now − (published_at_i or fetched_at_i)) / 86_400_000; decay_i = exp(−age_days_i × ln(2) / half_life_days); effective_w_i = base_weight_i × decay_i.
Weighted totals by classification c in {promoter, passive, detractor}: W_c = sum(effective_w_i where classification == c); W_total = W_promoter + W_passive + W_detractor.
Final score (range -100 to +100): μNPS = ((W_promoter / W_total) − (W_detractor / W_total)) × 100. Constants (methodology v1): half_life_days = 180 (configurable per version in mnps_weight_config); base_weight = mnps_weight_config.weight (defaults to 1.0).
Source-weighted signals let us treat a verified BBB complaint differently from a single-line Reddit comment. Exponential recency decay (default half-life 180 days) means a 4-year-old 1-star review counts roughly 0.06× as much as a review posted today. Decay uses published_at when available, falling back to fetched_at; rows with neither receive a neutral decay = 1.0.
μNPS scale and interpretation
μNPS uses the standard NPS scale of -100 to +100. Bands are directional, not authoritative. Pass threshold for AnswerShare client reporting: μNPS ≥ +10 with a sample size of at least 50 weighted signals; below threshold, the ΔNPS diagnostic is reported with a low-confidence flag.
| Band | μNPS range | Reading |
|---|---|---|
| Strong positive | +30 to +100 | Corpus is clearly net-positive. Brand has earned promoter signal. |
| Net positive | +10 to +29 | Corpus leans positive after recency weighting. |
| Mixed | -9 to +9 | Promoter and detractor signals are roughly balanced. |
| Net negative | -29 to -10 | Detractor signal outweighs promoter signal. |
| Strong negative | -100 to -30 | Corpus is materially negative. Reputation remediation indicated before GEO work. |
λNPS — live retrieval signal from 5 production LLMs
λNPS measures how positively (or negatively) AI assistants frame the brand when answering user queries. It is computed by querying the brand across AI systems — Perplexity, OpenAI, Gemini, Claude, and Grok — with a calibrated prompt set, then sentiment-scoring the resulting citations and framing on a net promoter scale.
Per-LLM NPS = %Promoter − %Detractor (range -100 to +100). The headline λNPS is the median across the five per-LLM values, following the AnswerShare 5-model median convention with outlier-drop: if max(panel) − min(panel) > ~10 points, the outlier model is dropped before the median is taken. The classifier that scores Promoter / Passive / Detractor is a different model from the one that generated the response, so generator and rater are independent.
Refusals are counted as Passive, not dropped, to prevent a refusal from inflating a small-sample NPS. Grok was added to the panel 2026-05-26 to complete coverage of the top-five conversational AI engines by user reach.
- Perplexity (sonar-pro)
- Retrieval-grounded answer engine. Live web grounding gives a current-day view of the brand.
- OpenAI (gpt-4.1)
- Frontier general-purpose LLM. Broad training corpus, conservative framing tendencies.
- Google Gemini (2.5-pro)
- Search-grounded LLM. Heavy weighting on Google index signals during generation.
- Anthropic Claude (opus-4-7)
- Frontier general-purpose LLM. Tends toward strict-rubric scoring; deductions are common.
- xAI Grok (current)
- Added 2026-05-26 to complete top-five conversational AI panel coverage.
ΔNPS — the AI perception gap
ΔNPS (delta-NPS) is the signed gap between machine-expressed and modeled-corpus reputation: ΔNPS = λNPS − μNPS, on the canonical NPS scale. SEO measures whether you appear; GEO measures whether you're cited; ΔNPS measures whether AI thinks better or worse of you than humans do.
ΔNPS > 0 (amplification, λ-amplification): AI cites and frames the brand more favorably than the human community does — often the 'AI moat' outcome GEO infrastructure is designed to produce. ΔNPS ≈ 0 (aligned): AI and humans broadly agree. ΔNPS < 0 (suppression, λ-suppression): AI cites the brand less favorably than humans do — the highest-urgency state, remediable via GEO.
Both inputs are -100 to +100, so the theoretical range is -200 to +200; in practice ΔNPS clusters between -60 and +60. Values beyond ±60 typically indicate a measurement defect — re-run both inputs and check brand-disambiguation parity and temporal alignment before interpreting. ΔNPS is signed and interpretive with no pass/fail threshold.
The λ symbol denotes machine-transformed output behavior rather than latent model state. λNPS measures generated responses under controlled prompting conditions, not internal model beliefs or hidden parameters. μNPS, λNPS, ΔNPS, and AnswerShare are trademarks of AnswerShare.
| ΔNPS range | Orientation | What it suggests |
|---|---|---|
| +30 to +60 | Strong amplification | AI is markedly more positive than the corpus. The GEO moat is working: bot-facing surfaces are outpacing human-facing brand assets, or competitors are weaker. |
| +10 to +30 | Mild amplification | AI is meaningfully more positive than the corpus. GEO infrastructure is helping; corpus could be reinforced to widen the gap. |
| -10 to +10 | Aligned | AI and corpus broadly agree (within measurement noise). No action required from ΔNPS alone. |
| -30 to -10 | Mild suppression | AI is meaningfully more negative than the corpus. Investigate citation gaps, stale grounding, or weak structured-data signals. |
| -60 to -30 | Strong suppression | AI is markedly more negative than the corpus. High urgency — humans like you, AI does not, and AI is the increasingly dominant intermediary. |
| Beyond ±60 | Validate first | Measurement defect more likely than genuine signal. Re-run μNPS and λNPS, check disambiguation parity, confirm temporal alignment. |
Frequently asked questions
- What is the AnswerShare NPS family?
- Three metrics on the canonical -100 to +100 NPS scale: μNPS (modeled human-community reputation reconstructed from the public corpus), λNPS (machine-expressed reputation measured live across five frontier LLMs), and ΔNPS = λNPS − μNPS (the signed gap that surfaces AI amplification or suppression).
- How is λNPS measured?
- λNPS queries five production LLMs — Perplexity (sonar-pro), OpenAI (gpt-4.1), Google Gemini (2.5-pro), Anthropic Claude (opus-4-7), and xAI Grok — with a calibrated prompt set. Each response is classified Promoter/Passive/Detractor by an independent classifier model. Per-LLM NPS = %Promoter − %Detractor, and the headline λNPS is the median across the five with an outlier-drop rule (drop a model more than ~10 points from the cluster).
- What does a negative ΔNPS mean?
- A negative ΔNPS (λ-suppression) means AI systems frame the brand less favorably than the human community does. It is the highest-urgency ΔNPS state — humans like the brand, AI does not — and is remediable via GEO work on citation gaps, stale grounding, and structured-data signals.
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