MARKET RESEARCH
Detect feature-level rating drops after an app release
After each app-store release, scrapes new reviews, classifies them by feature, and alerts product in Slack when a specific feature's sentiment drops sharply versus the prior…
How it runs
The automated pipeline, trigger to output.
- TriggerNew app version detected on store listingApify
- ActionScrape reviews posted since release dateApify
- ActionClassify reviews by feature and score sentimentOpenAI
- LogicCompare per-feature sentiment to prior version baseline
- LogicBranch when a feature's drop exceeds threshold
- OutputPost feature regression alert to product SlackSlack
What it does
Watches your app's store listing for a new version, then pulls the reviews that arrived after the release date and figures out which feature each one is complaining about. If sentiment for any single feature falls below your threshold compared to the previous release, it posts a focused alert to your product Slack channel naming the feature, the version, and representative quotes.
When to use it
Use it when a release can silently tank one feature (search, sync, notifications) while overall stars look fine. Aggregate ratings hide feature regressions; this surfaces them within hours of rollout so product can ship a hotfix before the bad reviews pile up.
How it works
- 1A new app version is detected on the store listing.
- 2Apify scrapes reviews posted since the release date.
- 3OpenAI tags each review with a feature label and a sentiment score.
- 4The flow compares per-feature sentiment against the prior version's baseline.
- 5If any feature's drop exceeds the threshold, a logic branch fires.
- 6Slack receives an alert with the feature, version, delta, and example quotes.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, 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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