Track Amazon Best Sellers rank with Apify and qualify low-competition products

Reads Amazon Best Sellers, Movers & Shakers and New Releases for your categories through Apify in monitor mode, so only new entries and rank changes come back. Each one is scored on rank gain, new-entry status, top-10 position, price band and how few reviews it has (a strong rank with few reviews suggests a market you could enter). Built for sellers doing product research and for brand teams watching a category. The rank is part of the dedupe key, so a product that moves again is reported again.

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.categories, listTypes and countryCode, then lowCompetitionReviews, minStars, minPrice and maxPrice. Use a distinct monitorStoreName per tracker.
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 rank movers (new entry 25, rank gain 25, top-10 15, low reviews 15, % change 15, price band 10).
• Add depth for subcategories or scrapeProductDetails for brand and seller data.
• 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.