Amazon Reviews Scraper & AI Review Analysis: From Product URL to Action Plan

Reading hundreds of Amazon reviews to find out why your rating is slipping takes hours, and a sentiment score alone does not tell you what to fix.

Paste an Amazon product URL. The Actor collects the reviews for you and turns them into a seller-ready action plan: sentiment, rating distribution, recurring themes (quality, sizing, shipping, packaging, value, defects), the top complaints and praises, prioritized fixes with benefit and cost, quick wins, and the single most important thing to do this week.

• One input: a product URL or ASIN link. Or paste your own reviews.
• Collection included: reviews are gathered through proven Amazon review scrapers from Apify Store, with automatic fallback if one fails.
• Decisions, not just scores: every finding is tied to an action with priority, risk, benefit and cost.
• Five output languages: English, Turkish, German, Spanish, French.
• Safe to try: if no reviews are found, you are not charged for analysis.
• Weekly monitoring: turn on onlyNewReviews and schedule it; each run analyzes and charges only reviews posted since the last run. No new reviews means only the start fee.

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At a glance

Input	Amazon product URL (startUrl) or your own review list (reviews)
Output	Prioritized action table (dataset) + full manager report (key-value store MANAGER_REPORT)
Analysis	Sentiment, star distribution, recurring themes, top complaints and praises, action plan, quick wins, critical step, competitor opportunities, executive summary
Reviews per run	10-500 from a URL; any number when you paste your own
Collection	Ordered chain of Amazon review scrapers with a spending cap per source
Pricing	Pay per 100 reviews analyzed + per report; see Pricing
Runs on	Apify Console, API, schedules, n8n, Make, Zapier, AI agents via MCP

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Quick start

1. Paste an Amazon product URL into Amazon product URL, for example https://www.amazon.com/dp/B0XXXXXXXX.
2. Set Max reviews to collect (100 is a good default) and the output language.
3. Click Start. Open the Dataset tab for the action table, and the key-value store for MANAGER_REPORT.

What the action table looks like

Priority	Issue / Area	Risk of Inaction	Risk Level	Benefit	Cost	Score	Recommended Action
1	Sizing runs small	Returns and 1-star reviews keep rising	High	High	Low	9.1	Update the size chart and add "order one size up" to the first bullet
2	Zipper breaks after weeks	Durability complaints reach the top reviews	High	High	Medium	7.8	Switch supplier for zippers; reply to affected reviewers with a replacement offer
3	Packaging damage in transit	Unboxing complaints and damaged returns	Medium	Medium	Low	6.4	Add an inner carton; ask the 3PL for fragile handling

Illustrative example; your run reports its own findings, based only on the reviews it collected.

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Why sellers use it

Review tools usually stop at "73% positive". That number does not tell a brand manager whether to change the supplier, rewrite the listing, or fix the packaging. This Actor behaves like an analyst who makes recommendations:

1. It reads every review in chunks small enough that nothing is skipped or cut off. If a model response is ever truncated, the chunk is split and retried instead of being silently dropped.
2. It groups problems into themes and counts how often each appears, so you see scale, not anecdotes.
3. It turns each theme into an action with the risk of doing nothing, a benefit and cost estimate, and a score to sort by.
4. It separates quick wins (cheap, fast, visible) from structural fixes.
5. It names one critical step for this week, so the report ends in a decision.

Sentiment tool vs. this Actor

	Typical review sentiment tool	This Actor
Collects the reviews	Often no; you bring a file	Yes, from a product URL
Output	Positive/negative percentages	Themes, complaints, praises, prioritized actions
Tells you what to do	No	Yes: action, risk, benefit, cost, score
Handles long review sets safely	Varies	Chunked analysis with split-and-retry
Ready for a manager	Needs interpretation	Executive summary and one critical step

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What you get

Dataset: the action table

One row per prioritized action, sorted by score: Priority, Issue / Area, Risk of Inaction, Risk Level, Benefit, Cost, Benefit/Cost, Score, Recommended Action. The number of rows shown is set by Actions shown in the table (default 5); the full list is in the report.

Key-value store: MANAGER_REPORT

Machine-readable report with English keys:

Key	Content
overall_sentiment	Overall tone of the reviews
rating_distribution	Count of 1-5 star reviews
review_count	Number of reviews analyzed
themes	Recurring themes with frequency and positive/negative direction
top_complaints / top_praises	The most frequent negatives and positives
competitor_opportunities	Gaps you can use in positioning against similar products
quick_wins	Low-effort, high-impact moves
critical_step	The single most important action this week
executive_summary	A short paragraph for a manager or client
all_actions	The full prioritized action list

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Who it is for

• Private-label and brand sellers who need to know what to fix before the next production run.
• Amazon agencies and consultants who audit listings for clients.
• Product managers and sourcing teams comparing suppliers and competitor products.
• Customer experience teams looking for the root causes behind negative reviews.
• Market researchers mapping unmet needs in a category.

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Worked example: diagnosing a rating drop

A backpack listing fell from 4.5 to 4.1 stars in two months.

1. Run on your listing with 200 reviews. The action table puts "sizing runs small" and "zipper breaks" at the top, with high risk and low-to-medium cost.
2. Check themes in the report: sizing appears in a large share of negative reviews, zipper failures in fewer but almost all of them 1-star. That tells you sizing drives volume and zippers drive severity.
3. Look at quick_wins: update the size chart, add a sizing line to the first bullet and the main image, and reply to recent sizing complaints.
4. Read critical_step: contact the supplier about the zipper before the next purchase order.
5. Run on two competitors. If they share the sizing complaint but not the zipper one, your fix on sizing becomes a positioning point ("true to size").
6. Re-run in six weeks and compare. The sizing theme should shrink first; zipper complaints follow after the new batch sells through.

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What to do with each part of the report

Report part	Typical owner	Typical use
critical_step	Owner or brand manager	This week's decision
quick_wins	Listing manager	Bullet, image and A+ content changes, FAQ additions
all_actions	Operations and sourcing	Supplier changes, packaging, QA checks
top_complaints	Customer support	Reply templates, return reason tracking
top_praises	Marketing	Copy in customers' words, ad angles
competitor_opportunities	Product and marketing	Differentiation and positioning
executive_summary	Management or client	One paragraph for a report or deck

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Listing optimization checklist from reviews

• The top complaint is answered in the first bullet or main images
• Sizing, dimensions or compatibility are stated where buyers look first
• The top three praises appear in the title or bullets, in customers' own words
• Packaging issues are raised with your logistics partner
• Recurring defects are reported to the supplier with review evidence
• Negative reviews about fixed issues receive a public reply

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Use cases

Amazon sellers and brand owners

Run it on your own listing monthly. Track whether the top complaint changes after a fix. Use top_praises to rewrite bullets in your customers' own words.

Product development and sourcing

Before reordering, run it on your product and two competitors. Recurring defects in reviews are a cheaper quality report than a lab test, and competitor complaints show which improvements would win customers.

Agencies and Amazon consultants

Deliver a review audit to a new client on day one. The executive summary and action table drop straight into a client deck; the scores make the priorities defensible.

Market research before launching a product

Analyze the top 3-5 products in a category. Themes that repeat across all of them are unmet needs, your differentiation angle.

Customer support leads

Find out which issues generate the most negative reviews and prepare reply templates and FAQ updates for them.

Retail buyers and distributors

Before listing a third-party brand in your own store, check how its products are reviewed on Amazon. Recurring defects or delivery problems are a warning sign worth raising with the brand before you commit stock.

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How it works

1. Collect. For a product URL, the Actor calls an ordered chain of Amazon review scrapers from Apify Store (cheapest first). Each call has a spending cap. If a source fails or returns less than half of what you asked for, the next one is tried, and the best result is kept.
2. Normalize. Review text, title and star rating are extracted into one format, whatever the source.
3. Analyze in chunks. Reviews are analyzed ten at a time by a language model with a strict JSON format, so long review sets never overflow the model.
4. Merge. Chunk results are combined into themes, complaints, praises and actions with counts.
5. Prioritize. Actions are scored by risk, benefit and cost and sorted.
6. Report. The action table goes to the dataset; the full report goes to MANAGER_REPORT.

If no reviews can be collected, the run stops with a clear message and this Actor charges nothing.

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Input

Field	Description
startUrl	Amazon product page URL. The Actor collects its reviews for you.
maxReviews	Reviews to collect from the URL (10-500). Default 100.
onlyNewReviews	Monitoring mode for scheduled runs. First run = baseline; later runs for the same URL analyze and charge only new reviews. Default off.
reviews	Alternative: your own reviews as plain strings or {text, rating} objects. Used only when startUrl is empty.
businessName	Product name or ASIN, used as report context. Filled in automatically in URL mode.
domainContext	Advanced: a hint about what to focus on (for example "a children's product; focus on safety and durability").
language	English, Türkçe, Deutsch, Español, Français
model	Advanced: the default is fast and economical
maxAksiyon	Number of actions shown in the table (3-10). The full list is in the report.

Example: product URL

{
  "startUrl": "https://www.amazon.com/dp/B0XXXXXXXX",
  "maxReviews": 200,
  "language": "English"
}

Example: your own reviews (from Seller Central export or another tool)

{
  "reviews": [
    {"text": "Great quality for the price, feels sturdy.", "rating": 5},
    {"text": "Runs small, had to return and order a size up.", "rating": 2},
    "Arrived with a crushed box but the product was fine."
  ],
  "businessName": "Trail Backpack 30L"
}

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Pricing

This Actor charges per event. Current prices are on the Pricing tab; at the time of writing:

Event	When	Price
actor-start	Once per run	$0.0001
reviews-processed-100	Per 100 reviews analyzed (rounded up)	$0.40
report-generated	Once per report	$0.25

In URL mode, the review scraper that collects the reviews bills your account separately at its own price. The first source in the chain costs about $0.001 per review; a spending cap is set on every call.

Reviews	This Actor	Collection (approx.)	Total (approx.)
50 (your own)	$0.65	$0	$0.65
100 from URL	$0.65	$0.10	$0.75
300 from URL	$1.45	$0.30	$1.75
500 from URL	$2.25	$0.50	$2.75

For comparison, reading and summarizing 300 reviews by hand takes a person several hours.

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Glossary

• Theme: a topic that recurs across reviews, such as sizing, durability or delivery, with how often it appears and whether it is mostly positive or negative.
• Risk of inaction: what is likely to happen to reviews, returns or rating if the issue is left as it is.
• Benefit / Cost: the model's estimate of the upside of acting and the effort it takes, used to rank actions.
• Score: the combined priority used to sort the action table.
• Quick win: an action with low cost and visible benefit, usually on the listing itself.
• Critical step: the one action the report recommends doing first.
• Competitor opportunity: a weakness in how customers experience this product that a better product or listing could exploit.

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Integrations

API

curl -X POST "https://api.apify.com/v2/acts/enezli~amazon-review-intelligence/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrl":"https://www.amazon.com/dp/B0XXXXXXXX","maxReviews":100}'

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run = client.actor("enezli/amazon-review-intelligence").call(
    run_input={"startUrl": "https://www.amazon.com/dp/B0XXXXXXXX", "maxReviews": 100})
report = client.key_value_store(run["defaultKeyValueStoreId"]).get_record("MANAGER_REPORT")["value"]
print(report["critical_step"])

Scheduled monitoring (only new reviews)

1. Turn on onlyNewReviews and save the input as a task for each product URL.
2. Add a schedule to the task (weekly suits most teams; daily for busy listings).
3. The first run analyzes every review and stores it as the baseline.
4. Each later run collects the latest reviews, skips the ones it has already seen, and builds the action plan from new reviews only. MANAGER_REPORT.monitoring shows new_reviews and new_negative_reviews (1-2 stars), so you can alert on bad weeks.
5. A week with no new reviews returns one "No new reviews" row and costs only the start fee. You never pay twice for the same review.

Seen reviews are kept in a named key-value store whose name starts with review-watch- in your own Apify account. Delete it to start a fresh baseline.

n8n, Make, Zapier

Run Actor → read MANAGER_REPORT → post the executive_summary and critical_step to Slack or email, and append the action table to Google Sheets.

AI agents (MCP)

Available through the Apify MCP server, so an assistant can analyze a product's reviews on request and answer "what should we fix first?".

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Workflow recipes

Monthly review health check. Schedule a run for each of your top products on the first of the month. Send critical_step and the top three actions to a Slack channel. Over time, the history of reports shows which fixes moved the rating.

Competitor teardown. Run the same settings on the five best-selling products in your category. Put top_complaints side by side in a sheet; complaints shared by all five are the gap a new product can fill.

Supplier conversation pack. Before a supplier call, run the Actor on your listing and export the defect-related actions with their review counts. Concrete, counted evidence makes quality requests easier to negotiate.

Launch follow-up. Two to four weeks after launching a new product, run on the first reviews to catch problems early, while they are still cheap to fix.

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Tips for better results

• Use 100-300 reviews. Fewer than 30 gives thin themes; more than 300 rarely changes the top priorities.
• Add a domainContext when the category has special concerns (safety, allergens, compatibility).
• Compare against competitors by running the same settings on their listings.
• Pick the language your team works in; review text in any language is understood.
• Re-run after changes and compare top_complaints to measure impact.

Limitations

• Review availability depends on Amazon and on the scraper sources; some marketplaces or products return fewer reviews than requested.
• Benefit, cost and risk are model estimates from review content, not financial calculations. Treat scores as a prioritization aid.
• The analysis reflects the reviews collected. Very recent changes to your product may not be visible yet.

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Troubleshooting

Symptom	Cause	Fix
"No reviews could be collected from this URL"	A review source failed for that page; the message lists which one and why	Check the URL opens a public Amazon product page; try again later. This Actor charged nothing
"This page has no public reviews yet"	Every review source finished normally and found 0 reviews (common for newly listed products)	Run it again once buyers have reviewed it. This Actor charged nothing
"No valid reviews found"	The pasted reviews list was empty or malformed	Paste reviews as {"text": ..., "rating": ...} objects, or use a URL
Fewer reviews than requested	The product has fewer reviews, or the source returned a partial set	Normal; the log shows which sources were tried
Themes feel generic	Very small sample	Increase maxReviews
Report in the wrong language	language left at English	Choose the output language in the input

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FAQ

Do I need my own Amazon scraper? No. The Actor collects reviews for you. If you already have reviews, paste them and skip collection costs.

Which Amazon marketplaces work? Product URLs from the marketplaces supported by the underlying review scrapers, with amazon.com the most reliable.

Why two layers of output? The dataset is a sortable table for quick decisions; the report holds everything, including themes and the full action list, for deeper work or automation.

Can I analyze competitor products? Yes. Any public product URL works.

What happens if a scraper source breaks? The next source in the chain is tried automatically, each with its own spending cap.

Am I charged if nothing is collected? No. This Actor charges only after reviews are available for analysis.

Is my data stored? Only in your own Apify storage. Review text is sent to the language model provider solely to produce your report.

Can I change the number of actions? Yes, with maxAksiyon (3-10) for the table. The report always contains all actions.

Does it read reviews in other languages? Yes. Reviews in any language the model understands are analyzed; the report is written in the output language you choose.

How long does a run take? Run time depends on how fast the review source responds and on the number of reviews; analysis runs ten reviews per step, so larger sets take proportionally longer.

Can I run many products at once? Run one product per run and start several runs in parallel, or loop over URLs with the API. Each report stays separate and easy to compare.

Can I get the raw reviews? Yes. In URL mode the collected reviews are in the scraper's own run in your account (Runs tab), where you can export them.

Why is the price per 100 reviews? Analysis cost grows with review volume. Rounding up per 100 keeps the price simple and predictable before you run.

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What's new

• Automatic source fallback: if one Amazon review scraper fails or returns too few reviews, the next one is tried, each with a spending cap.
• Split-and-retry analysis: long responses are never silently cut; the chunk is split and analyzed again.
• No charge on empty collection: if no reviews are found, the run stops before any analysis charge.

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Trademarks

This Actor is not affiliated with, endorsed by, or connected to Amazon. All product names are trademarks of their respective owners.