Amazon Reviews Scraper Guide: Setups and Extraction

The Amazon Reviews Scraper extracts structured customer review data, star ratings, verified purchase flags, and product rating summaries across 19+ Amazon domains. It operates either in a public cookie-free posture that pulls top reviews directly from standard product detail pages, or in an authenticated cookie mode that retrieves up to 2,000 paginated reviews per product listing.

Collecting customer feedback from Amazon enables catalog audits, product research, and competitive intelligence. Because Amazon actively monitors scraping traffic and uses regional interface differences, selecting the appropriate input parameters, proxy escalations, and fallback options is essential for continuous data extraction.

Practical use cases

Use case 1: Defect cataloging and product research

• Outcome: Identify recurring manufacturing defects, sizing errors, and usability complaints on competing product listings before manufacturing or sourcing inventory.
• Question to answer: What specific component failures and design flaws drive 1-star and 2-star customer ratings on target competitor products?
• Configure: Enter target product URLs in productUrls, choose your marketplace domain in country (such as amazon.com or amazon.de), set filterByStar to 1 (or run a separate batch for 2), set sortBy to helpful, set maxReviews to 15 for inline scans or provide cookieString with cookieModeMaxReviews set to 500, and set includeAggregates to true.
• Working method: Run extraction on the target ASINs with low-rating filters active. Parse the output reviewDescription, reviewTitle, variant, and reviewer image links in reviewImages to categorize product issues into packaging, mechanical failure, or description inaccuracies. Compare the frequency of these issues against the baseline rating distribution found in starsBreakdown on the aggregate record.
• Deliverable: A structured defect matrix linking each ASIN to specific failure modes, customer complaint frequency, and visual evidence from reviewer photos.
• Stop condition: Stop the run when all target listing URLs have been processed and at least 25 critical reviews per ASIN are cataloged, or when available filtered pages are exhausted.

Use case 2: Recurring competitor sentiment monitoring

• Outcome: Track score fluctuations, negative review spikes, and listing rating changes across competing brands on a weekly schedule.
• Question to answer: How do competitor satisfaction scores and review intake rates change week-over-week following catalog updates or pricing adjustments?
• Configure: Supply a fixed list of competitor product links in productUrls, set sortBy to recent, configure sourceMode as AUTO, keep countryFallback enabled, set includeImages to false to streamline payload size, and set includeAggregates to true.
• Working method: Execute scheduled scraping tasks weekly. Ingest both the review records and the dedicated amazon_review_aggregate record. Calculate weekly changes in averageRating and globalRatingsCount. Compare the newest reviewDate timestamps against prior run snapshots to measure incoming review velocity and flag sudden influxes of critical feedback.
• Deliverable: A weekly competitor tracking dashboard detailing rating score deltas, new negative review summaries, and total rating volume shifts.
• Stop condition: End the run automatically when the latest batch of recent reviews and rating aggregates is collected for all listed competitor URLs.

Use case 3: Voice-of-customer feature praise mapping

• Outcome: Extract exact customer phrasing, emotional benefits, and favored features from verified buyers to refine product detail copy and advertising messaging.
• Question to answer: Which specific product features and use cases drive 5-star satisfaction among verified buyers across different purchased variants?
• Configure: Provide target productUrls, supply a valid session cookie in cookieString, enable verifiedPurchasesOnly as true, set filterByStar to 5, set sortBy to helpful, and set cookieModeMaxReviews to 1000.
• Working method: Run authenticated extraction against top-selling products in your category. Verify that isVerified is true on all extracted reviews. Filter and group review records by variant (such as size, color, or style) and analyze reviewTitle and reviewDescription to isolate high-frequency positive keywords, primary buyer motivations, and standout feature praise.
• Deliverable: A voice-of-customer copy repository mapping customer phrases, high-value feature mentions, and emotional triggers to specific product variations.
• Stop condition: Terminate collection once the target ASINs reach the 1,000-review limit or when all available 5-star verified reviews for the selected ASINs have been collected.

Step-by-step extraction workflow

1. Define input parameters: Compile the Amazon URLs for your target items. If discovering listings dynamically, supply search queries in keywords or catalog categories in categoryUrls along with a discovery cap in maxProductsFromDiscovery.
2. Choose extraction mode: Use default cookie-free mode for rapid catalog spot-checks. If you require deep paginated review records or verified-purchase filtering, extract your session cookie header from your browser developer tools and enter it into cookieString.
3. Configure filters and sorting: Choose between helpful and recent sort orders. Apply filterByStar if your analysis focuses exclusively on specific star tiers.
4. Select proxy and network settings: Keep sourceMode set to AUTO and maintain default proxy configuration. The actor uses datacenter proxies initially and escalates to residential proxies only when access challenges occur.
5. Execute the run: Start the scraper on Apify and inspect log outputs for successful page retrievals or marketplace fallback events.
6. Export and analyze data: Retrieve the resulting dataset containing individual review objects and the standalone amazon_review_aggregate record for reporting.

Proxy routing, fallback mechanics, and session cookies

Amazon storefronts enforce different access controls depending on the regional domain and traffic profile. The scraper includes several features to balance run reliability and operational efficiency.

• Datacenter and residential proxy escalation: In AUTO source mode, the actor routes requests through standard datacenter proxies first. If a sign-in prompt or verification block is detected, it automatically escalates to a residential proxy. Choosing INLINE_ONLY disables residential escalation and keeps requests strictly on a single marketplace.
• Multi-marketplace fallback: Certain domains, such as amazon.com, may restrict public inline review cards behind sign-in prompts. When countryFallback is active in cookie-free mode, the scraper retries the target ASIN on open alternate domains, specifically amazon.ca and amazon.com.br, using country-matched proxies to obtain public reviews.
• Authenticated cookie mode: Supplying a cookieString switches the actor from inline page parsing to Amazon paginated review endpoints. This allows extraction of up to 2,000 reviews per ASIN. When requests exceed 500 reviews, the actor automatically fans out across star filters to bypass standard 50-page pagination caps.

Data quality, operational limits, and compliance

• Omission of empty values: Amazon review pages do not always contain every possible field. The scraper omits empty fields rather than returning them as null values. For instance, if a review contains no media, reviewImages and reviewVideos are omitted from that record.
• Standalone aggregate record: When includeAggregates is enabled, the actor pushes a record with type: "amazon_review_aggregate". This contains averageRating, globalRatingsCount, starsBreakdown, customersSay, and customersSayAspects. This record is generated even if individual review blocks on the listing are restricted.
• Concurrency recommendations: Keep maxConcurrency between 1 and 3 concurrent browser pages. Running higher concurrency levels increases the likelihood of access challenges.
• GDPR sensitive fields: Reviewer personal information, including reviewerName, reviewerProfile, and reviewerAvatar, is excluded by default. These fields are only populated when includeGdprSensitive is explicitly enabled.

Frequently asked questions

Why does cookie-free mode return only 8 to 13 reviews per listing?

In cookie-free mode, the scraper reads Amazon's public inline review block on standard product detail pages. Amazon renders approximately 8 to 13 top reviews in this block without pagination for unauthenticated users. To extract deeper paginated history up to 2,000 reviews, provide a session cookie in cookieString.

Should I enter my Amazon account password into the scraper?

No. Never enter your Amazon account password. The scraper only accepts session cookie strings copied from your browser developer tools. The actor automatically rejects inputs containing the word password.

What is the operational difference between AUTO and INLINE_ONLY modes?

AUTO is the standard operational mode. It attempts datacenter proxies first, enables multi-marketplace fallbacks if a storefront is walled, and escalates to residential proxies upon encountering blocks. INLINE_ONLY is a strict, single-marketplace mode that disables both multi-marketplace fallback and residential proxy escalation.

How does multi-marketplace fallback function?

When cookie-free mode encounters an account verification block on the primary requested domain, countryFallback automatically queries the same product ASIN on open alternate storefronts, namely amazon.ca and amazon.com.br, using country-matched proxies.

Can I filter for verified purchases without providing a session cookie?

No. Amazon restricts the verified purchase filter to paginated endpoints that require an authenticated session. In cookie-free mode, the scraper still populates the isVerified boolean field on returned records, allowing you to filter verified reviews downstream in your data pipeline.

Resources

Complement your review extraction workflows with other specialized scraping tools in the Amazon suite:

• Amazon Reviews Scraper: Scrape customer reviews, ratings, and aggregate metrics across 19+ Amazon domains.
• Amazon Category Scraper: Scrape department hierarchies, category trees, and browse-node listings.
• Amazon Product Scraper: Extract detailed product specifications, pricing, buy box data, and descriptions.
• Amazon Best Sellers & Rankings Scraper: Monitor category bestsellers, new releases, and sales rankings.
• Amazon Reviews Scraper Pro: Extended review extraction capabilities for advanced Amazon workflows.
• Amazon Offers Scraper (All Offers Display): Extract competitive merchant offers, seller pricing, and fulfillment channels.
• Amazon Seller & Shop Scraper: Collect storefront details, merchant ratings, and seller catalog data.
• Amazon Deals Scraper: Track daily promotions, lightning deals, and coupon discounts.
• Amazon Brand Store Scraper: Extract brand showcase layouts and curated brand product collections.
• Amazon Creator Shop Scraper: Monitor influencer storefronts and curated product recommendations.
• Amazon Wishlist & Registry Scraper: Scrape public customer wishlists, gift registries, and idea lists.
• Amazon Keyword Suggestions Scraper: Extract real-time search autocompletion suggestions for search optimization.