MARKET RESEARCH
Detect review topic shifts between competitor app versions in BigQuery
On a schedule, scrapes the latest competitor app-store reviews, loads them into BigQuery.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionScrape latest competitor reviews with version tagsApify
- ActionClassify reviews into topics + sentimentOpenAI
- ActionAppend classified rows to BigQuery version tableBigQuery
- ActionQuery topic-share delta vs prior versionBigQuery
- LogicKeep topics that moved past threshold
- OutputPost ranked topic shifts to SlackSlack
What it does
Tracks how the conversation around a competitor's app changes release-to-release. It pulls fresh reviews, classifies each into topics (performance, pricing, bugs, UX, features), stores them in BigQuery keyed by app version, then diffs the topic distribution of the latest version against the previous one. Any topic that moved more than a set threshold gets surfaced as a shift.
When to use it
Use it to catch a competitor's regression or wins in near-real time — e.g. a new version suddenly drawing crash complaints, or a pricing change generating backlash you can exploit in positioning. Good for PMMs and competitive-intel teams monitoring 1-5 rival apps.
How it works
- 1A weekly schedule fires the run.
- 2Apify scrapes the most recent reviews for each tracked app, including the version each review targets.
- 3OpenAI classifies every review into a fixed topic taxonomy with a sentiment score.
- 4The classified rows are appended to a BigQuery table partitioned by app and version.
- 5A BigQuery query computes the topic-share delta between the newest version and the prior one.
- 6A logic step keeps only topics whose share moved past the threshold.
- 7The ranked shifts are posted to a Slack channel with example quotes.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect BigQueryDatasets, queries, schemas.
- 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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