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Monitor urgent App Store reviews with Apify, OpenAI, Google Sheets and Slack

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Created by: Matt Michels || mattmichels
Matt Michels

Last update

Last update 2 days ago

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Pulls the newest Apple App Store reviews for the app IDs you list, keeps only reviews it has not seen before, and scores each one for urgency from the star rating, complaint keywords (crash, refund, cancel...), helpful votes and recency. Built for product, support and community teams who want bad reviews in front of a person the same day, and for agencies tracking client apps.

How it works

  1. Daily schedule / manual run reads every setting from Config. A weekly schedule uses the same Config to send a report instead of running the actor.
  2. Starts the Apify actor asynchronously, then waits and polls the run; FAILED, ABORTED, TIMED-OUT or too many polls stop with an error.
  3. Downloads the dataset, validates rows and dedupes within the run and across runs (workflow static data, dedupeMemoryDays).
  4. A transparent rule score runs first, then an LLM chain with a structured output parser reads each record against your plain-English aiCriteria and returns a fit score, verdict, summary, outreach angle and red flags. Calls are batched with a delay for rate limits, retried, and fall back to the rule score if the model fails.
  5. Upserts every row to Google Sheets. Hot rows go to Slack as one approve / dismiss message (send-and-wait, 24 h limit) and the decision is written back to the sheet; warm rows go to an HTML email digest; cold rows stay in the sheet.
  6. Weekly report: reads the sheet, summarises the last 7 days (tiers, approvals, top 10) and emails it.
  7. A separate Error Trigger branch posts any failure to Slack.

Setup (about 15 minutes)

  1. Create an HTTP Header Auth credential: name Authorization, value Bearer <APIFY_API_TOKEN> (Apify Console > Settings > API & Integrations). Select it on the 3 Apify HTTP nodes.
  2. Create a Google Sheet with a tab named in sheetTab and a header row matching the sheet columns (see Upsert to Google Sheets, plus status and reviewedAt). Paste its ID into googleSheetId.
  3. Connect OpenAI (or swap the chat model sub-node for Anthropic, Gemini, Ollama...), Slack and Gmail; set slackChannelId and digestEmail in Config (also the channel on Slack: error alert).
  4. In Config set actorInput.appIds (the number in an App Store URL, e.g. id324684580), actorInput.countries, complaintKeywords, hotScore and warmScore. Use actorInput.sinceDate to limit how far back each run looks.
  5. Write your ideal-lead description in aiCriteria; set aiWeight (0 = rules only, 1 = AI only).
  6. Run once manually, check the sheet, then activate.

Customize

  • Edit weights in Score review urgency (1 star 45, keywords up to 30, recency 15, helpful votes 15, detail 5).
  • Raise maxReviewsPerApp for busy apps, or set deltaMode and a stateKey in actorInput to have the actor itself return only new reviews.
  • The AI node drafts a reply angle for each hot review; nothing is posted back to the App Store.
  • Swap Gmail/Slack for Teams, Discord, HubSpot, Airtable, etc. Each delivery node only reads $json.

Disclosure: I maintain these Apify actors (account transparent_meteorite). The actor is a paid pay-per-event Apify tool; check its Store page for current pricing before scheduling.