App Review Analysis That Turns Thousands of Reviews Into a Fix List
Your reviews are a free, constant stream of customer feedback, but nobody can read 60,000 of them. I classify every review by theme, sentiment and intent, then show which problems are growing, which cost you customers and which fixes pay back.
What Review Analysis Finds That Ratings Miss
Findings from my three public app review case studies.
reviews analysed across Moniepoint, M-KOPA, Chowdeck and Glovo, with themes, leave signals and competitor mentions
leave-signal lift for Moniepoint reviews that mention fees, showing which complaints actually predict exits
leave signals per 1,000 reviews at Chowdeck and Glovo, a like-for-like comparison of two rivals
How Review Analysis Works
A repeatable pipeline from raw reviews to recommendations.
Collect and Clean Reviews
Reviews are gathered from Google Play (and other public sources where allowed), deduplicated, and tagged by date, app version and rating.
Classify Themes and Intent
Each review is labelled for the problems, praise, feature requests and questions it contains, including whether the customer is only venting or signalling they may leave.
Compare Across Time and Competitors
Trends by quarter show whether updates fixed anything, and reviews that name rivals show who customers compare you with and why.
Prioritise With a Break-Even View
Recommendations are ranked by customer impact and supported by a simple break-even calculator so you can judge whether a fix is worth the money.
What You Receive
Labelled Review Dataset
Every review with theme, sentiment and intent labels, delivered as a downloadable file.
Complaint and Leave-Signal Profile
What customers complain about, what makes them threaten to leave, and where they say they will go.
Praise and Feature Requests
What customers love and recommend, and the features they ask for most.
Reply and Reputation Audit
A review of how you respond publicly, with template recommendations that resolve issues instead of asking for ratings.
Break-Even Calculator
A simple model to test whether a proposed fix pays back.
Related Projects
Three full review analyses, each with methodology and results.
Common Questions
A few hundred reviews can already show clear themes. The larger case studies on this site use tens of thousands, which allows trends over time and reliable comparisons between versions or competitors.
Public reviews of competitors can be analysed in the same way, as in the Chowdeck vs Glovo case study. It shows what customers value elsewhere and why they switch.
Automated classification can misread sarcasm, so I check results against hand-labelled samples where it matters and document the method, as in the case studies. Star ratings are always read alongside the text, because the two often disagree.
No. The same approach applies to Google Business reviews, marketplace and e-commerce product reviews, survey open-ended answers and support tickets.
Read Every Review at Once
Find Out What Your App Reviews Are Really Telling You
Tell me which app or product you want analysed. First call is free, and I will outline what the analysis would cover.
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