MARKET RESEARCH
App Store Review Miner: Cluster Feature Requests into Linear Backlog
Scrapes fresh App Store and Google Play reviews on a schedule, uses an LLM to extract and cluster feature requests by theme.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily schedule fires
- ActionScrape App Store + Play reviewsApify
- LogicFilter to reviews with a feature request
- ActionExtract and cluster requests into themesOpenAI
- LogicDedup themes against existing issues
- ActionCreate or update Linear issuesLinear
- OutputPost top-themes digest to SlackSlack
What it does
This workflow turns the daily firehose of app-store reviews into a clean, themed product backlog. It pulls recent reviews, isolates the ones that actually contain a feature request, groups them into themes, and pushes each theme into Linear so product managers see demand signals instead of raw text.
When to use it
Run this when your app collects more reviews than anyone can read, and feature ideas are getting lost in the noise. It is ideal for product teams who want App Store and Play Store wishes to land directly in their planning tool, ranked by how often users ask.
How it works
- 1A daily schedule fires the run.
- 2Apify scrapes the latest App Store and Google Play reviews for your app IDs.
- 3A filter drops reviews with no actionable request (ratings-only, bug-only, spam).
- 4OpenAI extracts the underlying ask from each review and clusters them into named themes with a one-line summary, mention count, and average star rating.
- 5A dedup check matches each theme against existing Linear issues by title similarity.
- 6New themes become Linear issues; existing themes get a comment incrementing the request count.
- 7The run posts a digest of the top themes to the product channel.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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