MARKET RESEARCH

Weekly cross-store version sentiment divergence digest

Each week compares feature sentiment between iOS and Android for the current release and emails product a digest highlighting features that regressed on one platform but not…

CategoryMarket Research
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionScrape current-version reviews from iOS and AndroidApify
  • ActionScore feature sentiment per platformOpenAI
  • LogicCompute per-feature cross-store divergence
  • ActionCompose divergence digestOpenAI
  • OutputEmail digest to product listGmailGmail

What it does

Once a week it gathers the current-release reviews from both the iOS App Store and Google Play, scores feature sentiment on each platform, and computes where the two diverge. It then composes an email digest that highlights features doing fine on one OS but suffering on the other, with the gap size and sample reviews, so platform-specific regressions get attention.

When to use it

Use it when your app ships to both stores and a release can break a feature on only one platform. Cross-store divergence is easy to miss because each store's overall rating looks acceptable in isolation; this digest puts them side by side.

How it works

  1. 1A weekly schedule starts the run.
  2. 2Apify scrapes current-version reviews from both stores.
  3. 3OpenAI scores feature sentiment separately per platform.
  4. 4The flow computes per-feature divergence between iOS and Android.
  5. 5OpenAI writes a digest emphasizing the widest single-platform regressions.
  6. 6Gmail sends the digest to the product distribution list.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect GmailRead, draft, send, label.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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