Guide: Extracting Amazon Reviews Scrapers

Understanding the Amazon Review Access Problem

Amazon has recently started to restrict access for popular review extraction tools like Helium 10 and Jungle Scout, making it difficult for sellers to gather customer feedback at scale. This creates challenges for product research, competitive analysis, and generating customer-driven FAQ content.

Apify provides a reliable alternative through its marketplace of specialized data extraction tools. Unlike traditional all-in-one software, Apify functions as a marketplace where developers create and maintain specialized scraping "actors" for different platforms and data types. You can find actors for TikTok data, Google Maps listings, Instagram content, and importantly for ecommerce sellers, multiple Amazon-specific scrapers including dedicated review extractors.

This guide walks through the complete process of using Apify's Amazon Reviews Scraper to extract customer feedback for analysis and content generation.

Step 1: Navigate the Apify Marketplace

Access apify.com and search for "Amazon Reviews Scraper" in the marketplace. You'll see multiple options from different developers.

The platform hosts specialized actors for different Amazon data needs: product details, reviews, pricing, seller information, and search results. This specialization often provides more reliable results than generic tools.

Step 2: Configure Your Scraping Job

Once you've selected an Amazon Reviews Scraper actor, click into the detailed view to access the input configuration interface.

The interface provides both Form and JSON input options. Form view offers guided fields, while JSON allows direct parameter entry.

Scraping Parameters Reference

Core Configuration

ASIN (Required): 10-character product identifier (e.g., "B08C1W5N87"). Find in product URL or "Product Information" section.

Domain Code (Required): Marketplace region - "com" (US), "co.uk" (UK), "de" (Germany), "ca" (Canada), etc.

Sort By (Required): "recent" for newest reviews, "helpful" for most useful. Use "recent" for trend analysis.

Filtering Options

Max Pages: Each page contains ~10 reviews. Start with 1 page for testing, increase based on needs. More pages = higher cost.

Filter By Star: Target specific ratings - "five_star", "one_star", or "all_reviews". Extract 1-star reviews for complaints, 5-star for testimonials.

Filter By Keyword: Search for specific terms (e.g., "durable", "broke"). Useful for finding reviews about particular features or issues.

Reviewer Type: "verified_purchase" for authentic feedback, "all_reviews" for complete dataset.

Output Configuration

Format Type: Use "current_format" for consistency with Amazon's latest structure.

Media Type: "all_contents" includes image/video URLs, "text_only" extracts faster with less storage.

Example Configuration

{
  "asin": "B08C1W5N87",
  "domainCode": "com",
  "sortBy": "recent",
  "maxPages": 1,
  "filterByStar": "five_star",
  "filterByKeyword": "good",
  "reviewerType": "all_reviews",
  "formatType": "current_format",
  "mediaType": "all_contents"
}
```

Step 3: Execute and Monitor the Scraping Run

Configure Run Options before starting:

Maximum Results: "Unlimited" for complete extraction, or specify a number to control costs.

Build: Select "latest" for most current scraper version.

Timeout: Default 43,200 seconds (12 hours) handles large review sets.

Memory: 256 MB standard allocation works for most jobs.

Click "Start" to begin extraction. Monitor real-time metrics including reviews extracted, pages processed, time elapsed, and cost accumulation. You can pause or abort runs anytime.

Step 4: Export Your Data

Once complete, access extracted data through the Results tab in multiple formats:

JSON: Structured data for API integration or AI tool processing

CSV: Spreadsheet-compatible for Excel or Google Sheets

Excel (XML): Direct import with preserved formatting

Each review includes title, full text, star rating, reviewer name, date, verified status, helpful votes, product variant info, and media attachment URLs.

Step 5: Generate Customer Insights with Claude

Use Claude to transform raw review data into actionable intelligence. Upload your exported data and use this prompt:

FAQ Generation from Reviews

You are a customer research analyst. Analyze the attached Amazon review data and identify the 6-10 most frequently asked or implied questions that buyers have.

For each question:

• Extract the actual concern or question from review text

• Phrase it as a clear, natural question a buyer would ask

• Note how frequently this concern appears

• Indicate the stage of buyer journey (awareness/consideration/decision)

Return as a prioritized list with: Question | Frequency | Journey Stage | Key Review Quotes

Focus on questions about: product fit, pricing/value, comparisons to alternatives, integration compatibility, ease of use, and support/service.

Cost Management and Best Practices

Start Small: Run test jobs with maxPages: 1 to verify configuration before large extractions.

Use Filters Strategically: Keyword and star filters reduce processing time and costs. Extract only what you need.

Schedule Regular Updates: Run smaller periodic jobs (daily/weekly) rather than one massive scrape.

Monitor Usage: Check Apify dashboard under "Usage and Billing" to track cost per run.

ASIN Accuracy: Double-check ASINs before running. Incorrect codes waste credits and return empty datasets.

Saved Tasks: Use "Save as new task" to store configurations for repeat scraping.

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