MARKET RESEARCH

Quarterly competitive landscape report from review mining

An agent-run workflow that mines reviews across your app and its competitors, synthesizes strengths, gaps, and shifting themes over the quarter.

CategoryMarket Research
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerQuarterly schedule fires
  • ActionScrape deep review history for full setApify
  • LogicCluster and compare themes across apps
  • ActionAdd release-context via web researchExa
  • OutputPublish narrative report to NotionNotionNotion
  • OutputPost headline findings to SlackSlack

What it does

Once a quarter, this researches the full competitive set — your app plus rivals — by mining a large body of reviews, then produces a written landscape report: who is praised for what, where everyone is failing users, which complaint themes are rising or fading, and where the whitespace sits. It lands as a Notion page with a Slack headline.

When to use it

For quarterly strategy reviews, board prep, or positioning work where you need a defensible, evidence-backed read on the market rather than a raw data dump.

How it works

  1. 1A quarterly schedule starts the run.
  2. 2Apify scrapes a deep review history for your app and each competitor.
  3. 3The agent classifies and clusters reviews per app into themes, then compares across apps to find shared gaps and unique strengths.
  4. 4It uses web research to add release-timeline context that explains theme shifts over the quarter.
  5. 5It writes a structured narrative report and publishes it as a Notion page.
  6. 6A Slack message posts the headline findings and a link to the full report.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ApifyActors, scrapers, datasets.
  2. 2
    Connect ExaNeural search across the web.
  3. 3
    Connect NotionPages, databases, comments.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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