Mine public reviews, app stores, and forums for unmet needs, competitor weaknesses, and switching triggers — with quoted evidence. Use when you want customer voice without waiting on interviews.
Tasks
Mine competitor and product reviews from public sources such as G2, Capterra, TrustRadius, app stores, Reddit, and community boards to capture unmet customer needs and switching triggers
Frame the top 3 to 5 customer need themes using solution-free naming conventions, accompanied by real verbatim quotes with URLs and frequency classifications
Populate up to 5 competitor weak points and switching triggers utilizing only cited customer language, then generate 3 opportunity hypotheses, 2 battle-card-ready weaknesses, and 3 assumptions to validate
Inputs
The product(s) or competitor(s) to mine
The specific business decision the customer voice data should inform
An optional theme to focus on, such as onboarding, pricing, or reliability
Outputs
A Voice-of-Customer Snapshot containing scope, 3 to 5 need themes with verbatims, competitor weak points, switching triggers, and a 'So What?' section
3 opportunity hypotheses framed as problems
2 battle-card-ready competitor weaknesses
3 assumptions intended for validation in real customer interviews
Limitations and checks
Public voice skews toward negative and vocal reviewers, meaning the satisfied and silent majority is under-represented
Outputs represent qualitative theming and lack statistical confidence
Not suitable for early-stage or niche enterprise products with no meaningful public footprint
Cannot mine private user feedback, tickets, or research data
Verify every quoted verbatim is a real, short customer excerpt accompanied by a source URL and that no quotes, ratings, review counts, or reviewer roles are fabricated
Confirm each of the top 3 to 5 need themes is named solution-free in 4 to 8 words and does not use specific feature complaints
Ensure frequency honesty by explicitly categorizing themes as recurring across sources, concentrated in one thread, or isolated but vivid
Check that a bias note is included for every swept source, acknowledging the specific source skew rather than treating it as a census
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