Mine 50 to 100 competitor reviews from G2, Capterra and Reddit into ranked switch-moment themes, with AI tagging that a person checks
A step-by-step method for turning a competitor's recent reviews into tagged themes, switch moments and clusters you can act on, with an optional AI tagging step.
Evidence: The source shows its work: steps, screenshots, code or data you can inspect.
The business problem
A founder entering a crowded software market does not know which complaints about the incumbents are common enough to build a pitch or a feature around.
What was tried
Pull 50 to 100 recent reviews per competitor from G2, Capterra and Reddit into a spreadsheet with the quote, rating, date, reviewer role and company size. Tag each row with a theme, sentiment, type, urgency and segment from a fixed list of about 30 to 50 tags, optionally pasting each quote into an AI model and checking every tag yourself. Have two people tag a sample of 20 rows to check consistency. Filter for negative, urgent reviews and phrases that signal looking for alternatives, favouring 3-star reviews. Treat a pattern as actionable only when at least 10 reviews share a theme and trigger, and cross-check against Reddit. Route onboarding problems to the product, support complaints to a comparison landing page, pricing complaints to sales scripts, and refresh quarterly.
What was reported (not reported)
The post reports no measured result for the method itself. Its examples are anonymised and qualitative, and the one revenue figure it quotes is credited to the agency's own paid campaigns, not to review mining.
Limitations
Written by the founder of a marketing agency that sells competitor-conquest campaigns, and the post repeatedly promotes them. The thresholds (50 to 100 reviews, clusters of 10, a 20 percent trend trigger, a 0.2-point rating trigger) are defaults with no validation. Review sites are self-selected samples that skew to extreme experiences, the post does not say how to weight Reddit against G2, and linking pipeline to review-driven campaigns is described but not shown. Statistics it cites from third parties are not verified.
What you need
Access to the public review pages, a spreadsheet, and either an AI model or a second person for tagging. Several hours for a first pass.
Sources
- SaaS Hero (agency; Aaron Rovner) ↗ Vendor case study, published February 18, 2026
Source published: February 18, 2026. Last reviewed here: October 13, 2026. Spot a mistake? Tell us.
Tools in this workflow
- ChatGPT: Optionally suggests tags for each pasted reviewTry ChatGPT
- Google Sheets: Holds the review dataset, tags and cluster countsTry Google Sheets
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