App Review Analyzer

App review analysis clusters public reviews (App Store, Play Store, G2, Capterra) into sentiment, themes, and feature requests.

Public feedback is unsolicited VoC. Mining it cheaply complements interviews and NPS.

Competitive benchmarking, post-launch monitoring, or when you lack primary research budget.

1. Ingest reviews (paste or export).
2. Sentiment mix.
3. Theme clusters with counts.
4. Segment by rating/version when available.
5. Prioritized opportunities.

• Representative sample size stated.
• Quotes anonymized.

• Treating star average as insight.
• Ignoring version-specific regressions.

Northvale competitor SRM app (n=240 G2 reviews, 90 days). Sentiment: 58% positive. Themes: integration pain (31%), slow support (22%), strong audit trail (18% praise). Version 4.2 spike in 1-star "sync broken" mentions (14 reviews). Opportunity: position ERP-native sync in sales battlecards. Risk: do not over-index on angry power users — sample skews enterprise IT.

PulseWell category (wellbeing apps, n=180 App Store reviews). Themes: "too generic tips" (28%), privacy concerns (19%), helpful manager view (15% praise). Competitor AcmeWellbeing: GDPR mention in 12 recent 1-stars. Opportunity: emphasize anonymization in store listing. Regression: none in PulseWell beta (too few public reviews yet).

Harbor vs LeanCo (Capterra, n=95 combined). Harbor praised for manufacturing specificity; LeanCo praised for templates but "consultant churn" in 8 reviews. Opportunity: diagnostic SKU addresses "not sure we're ready" objection seen in 11 Harbor 3-star reviews.

CommCare field app reviews (n=110, filtered WASH NGOs). Themes: offline sync (26% issues), training burden (17%), donor reporting (12% praise). Implication for Clearwater: budget volunteer training hours; test offline queue before national rollout.

Run /appreviews for APPREV-001; broader VoC uses /feedback.