Transform in a few hours hundreds of scattered feedbacks (support, surveys, interviews, app reviews) into actionable and prioritized insights.
PMs receive on average several hundred user feedbacks per month, scattered across Intercom, surveys, app reviews, internal Slack, sales calls, support tickets. Synthesizing all this manually takes 1 to 2 days per cycle. AI gets you to 2-3 hours for exhaustive, structured, ranked synthesis. The rule: never delegate deep reading of important feedbacks (strategic interviews) — AI accelerates high-volume triage but doesn't replace careful listening.
Step-by-step Workflow
1
Centralize feedback sources
Before AI: export feedbacks from all sources over the desired period (Intercom, Zendesk, surveys, app reviews, NPS, Sales/CS calls, Slack threads). The more exhaustive, the better the synthesis.
2
Pseudonymize if necessary
If feedbacks contain identifying data (names, accounts, sensitive info): pseudonymize before sending to a public LLM. Or use ChatGPT Enterprise / Claude for Work for GDPR compliance.
3
Request structured thematic synthesis
Format: top 10 themes by frequency, overall sentiment by theme, representative quotes, prioritization by business impact. This is what makes synthesis actionable.
4
Identify new signals
AI can compare with a prior synthesis: what's emerging? What's declining? What keeps coming up despite our efforts? Hidden opportunities lie there.
5
Convert to product actions
For each major theme: what action? (feature, bugfix, communication, internal training, doc). Prioritize by RICE or ICE to integrate into roadmap. AI drafts the trame, PM decides.
Copyable Prompts
Thematic synthesis of feedback
You are a senior product manager. Here are [N] user feedbacks collected over [PERIOD]:nn[PASTE FEEDBACKS — pseudonymized if necessary]nnProduce a structured synthesis:nn1. **Top 10 themes** by frequency, with:n - Number of mentionsn - Overall sentiment (positive/neutral/negative)n - 2-3 representative quotes (verbatim, pseudonymized)n - Affected personas nn2. **Notable shifts** vs prior period (if comparable): emergence, decline, persistencenn3. **Weak signals**: themes mentioned less but potentially important (edge cases, specific NPS detractors)nn4. **Internal tensions**: contradictory feedback, segments wanting opposites, arbitrages to makenn5. **Recommended actions**: top 5 actions by expected impact × effortnnStay faithful to feedbacks (no invention), precise on numbers, actionable on recommendations.
NPS and verbatim analysis
Here are the latest NPS results:nn**NPS Score**: [SCORE]n**Sample**: [N RESPONSES]n**Promoter verbatims**: [LIST]n**Passive verbatims**: [LIST]n**Detractor verbatims**: [LIST]nnProduce:n1. **Promoter synthesis**: what makes them fans (top 5 reasons with quotes)n2. **Passive synthesis**: what prevents them from becoming promoters (top 5 reasons)n3. **Detractor synthesis**: what makes them unhappy (top 5 reasons, severity)n4. **Patterns by persona**: NPS by segment if visible n5. **Priority actions** for: (a) strengthen promoters, (b) move passives up, (c) address critical detractorsn6. **Churn risk**: indicators in detractor verbatims
Qualify a new feedback
Here's received feedback:nn[FEEDBACK]nn**Source**: [INTERCOM / SURVEY / APP REVIEW / etc.]n**User profile**: [IF KNOWN]nnProduce:n1. **Classification**: bug / feature request / UX / pricing / othern2. **Severity**: low / medium / high / criticalen3. **Likely frequency**: isolated / recurring / patternn4. **Affected persona**: who truly suffers from thisn5. **Recommended action**: handle immediately / backlog / investigate / closen6. **Routing**: which team/person to handle (product, support, eng, success)n7. **Suggested tags** for ongoing triage structure
Product prioritization matrix
From this feedback synthesis:nn[SYNTHESIS]nnProduce a prioritization matrix for the next roadmap:nnTable format with, for each major theme:n- **Short description**n- **Business impact**: revenue / retention / acquisition / NPS (note 1-10 + reason)n- **Volume affected**: % of base or number of usersn- **Estimated effort**: XS, S, M, L, XLn- **Estimation confidence**n- **Calculated RICE score**n- **Recommendation**: implement in sprint X, in V2, to explore, to abandon n- **Arbitrage reason** in 1 sentencennEnd with the top 5 to integrate absolutely in next quarter, and the 3 'we will not do this' to own.
Recommended tools
Claude AI
★ 4.9 (55) · Gratuit
Assistant conversationnel d’Anthropic axé sécurité et contexte long. Excellent pour rédaction, analyse, résumés, code et agents. Interface claire, bons résultats en français.
Why : Excellence sur la synthèse thématique multi-sources. Capacité à gérer des centaines de feedbacks en un prompt grâce à son contexte 1M+.
Assistant IA de réunion : enregistre, transcrit et résume automatiquement vos appels Zoom/Meet/Teams.
Why : Capture automatique des calls users (Zoom, Meet, Teams) avec synthèse et tagging. Élimine la tâche pénible de prendre des notes pendant l'interview.
Pseudonymized (no names, emails, account IDs): generally yes. With identifying data: only via Claude for Work / ChatGPT Enterprise (no-training contractual). For ultra-sensitive data (health, finance): self-hosted or dedicated solutions.
Can AI replace reading feedback?
For high-volume triage (200+ feedbacks/month): largely yes. For deep reading of strategic feedbacks (in-depth interviews, churn of major accounts): no, careful listening remains human. Best practice: AI for 80%, human reading for the 20% that matter.
How to avoid over-interpreting feedback?
Always cross with: (a) quantitative data (analytics, usage metrics), (b) actual feedback volume (1 mention vs 50), (c) author profile (engaged user vs casual). AI can amplify listening biases if you don't challenge its synthesis.
Should you share the raw AI synthesis with teams?
No. Always review, enrich with your context, and frame in actionable terms for each team (design, eng, marketing). The AI synthesis is a draft; the final deliverable is human.
