Review scraper workflow / 2026-05-20

Turn raw review scraper exports into a weekly action queue.

A first-run workflow for teams already collecting public reviews with Apify scrapers and needing theme tags, response priorities, proof quotes, and owner-ready follow-up rows.

Buyer job

Agencies, marketplace teams, product marketers, and reputation operators using review scraper exports

Review scrapers collect useful text, but buyers still have to turn rows into response work, listing fixes, proof quotes, and battlecard updates.

source-specific review rows become prioritized themes, response drafts, proof snippets, and workflow-owner queues.

1

Start with a scraper export

Paste a small set of public review rows from Google Maps, Amazon, Trustpilot, or another source.

2

Normalize the next step

Classify themes, urgency, proof quotes, and response needs before handing work to a person or queue.

3

Route by owner

Send response drafts, listing fixes, product issues, and proof snippets to the right weekly workflow.

Actors in the workflow

Start narrow, then connect the workflow.

Open a small sample first. Once the output fits the job, connect the matching scraper dataset or schedule.

ActorBuyerLink
Local Review Intelligence MonitorLocal SEO agencies, reputation managers, franchises, multi-location operatorsStore
Google Maps Review Intelligence MonitorLocal SEO agencies, reputation managers, franchises, multi-location operatorsStore
Amazon Review Intelligence MonitorEcommerce sellers, marketplace operators, agencies, product teamsStore
Trustpilot Review Intelligence MonitorBrand marketers, reputation managers, ecommerce operators, support leadersStore
Search intent

Built for specific buyer queries.

This page exists to answer focused workflow searches before routing the buyer to a sample-led Store path.

Query shape
Apify review scraper analysis workflow
turn review scraper output into action queue
review scraper sentiment action queue
Proof media

Preview the output before opening the Store page.

These public-safe GIFs show the first-run sample becoming the buyer-facing workflow artifact.

Turn two local reviews into a response queue.

The Store sample uses fictional Northstar Dental reviews, so a buyer can inspect complaint themes, urgency, recommended action, and proof quote flow before connecting a scraper dataset.

Animated first-run proof for local-review-intelligence-monitor

Turn two Google Maps reviews into response work.

The sample uses fictional local-business reviews, so a buyer can inspect themes, urgency, response direction, recommended action, and proof quote flow before connecting a scraper dataset.

Animated first-run proof for google-maps-review-intelligence-monitor

Turn two Amazon reviews into seller actions.

The sample shows how marketplace reviews become product-quality themes, listing actions, operations follow-ups, seller-response tasks, and proof quotes without starting with a broad production export.

Animated first-run proof for amazon-review-intelligence-monitor

Turn two Trustpilot reviews into reputation actions.

The sample keeps the first run small, so a buyer can see support themes, proof quotes, response direction, and weekly reputation actions before adding a larger review export.

Animated first-run proof for trustpilot-review-intelligence-monitor
Decision workflow

Choose the weekly handoff this Actor will produce before running another export.

Show the safe input, scored rows, and the exact team handoff for Monday.

Anchor each use case to one Actor, one sample, and one output artifact.

Sample request

Need this adapted before the first run?

Send a public-safe workflow request if the sample, setup, or output handoff needs a variant.

Keep requests public-safe: no credentials, private URLs, customer data, emails, or sensitive business data.