You're sitting on twelve ASINs across .com, .co.uk, and .de. Your competitor just dropped a v2 of their hero product and the one-star spike on the launch ASIN looks suspicious. You don't have a week to scroll through 8,000 reviews on the site filter, and Amazon caps you at the first few hundred anyway. You need every review, in a spreadsheet, with the verified-purchase flag intact and the country code per row. That's the job. This guide walks you through using the ExportComments Amazon Reviews exporter to pull the full review history for any ASIN — across all 18 marketplaces — into Excel, CSV, or JSON.

Why export Amazon reviews

Amazon is still the deepest single source of consumer-product feedback on the public web. A best-selling charger, a kitchen appliance, a pair of running shoes — each one accumulates thousands of reviews per ASIN, often across half a dozen country marketplaces, with helpful votes, verified-purchase badges, variant breakdowns, and Vine reviewer flags layered on top. Once you have it offline, you can do the analysis the on-site filter set quietly refuses to let you do:

  • Multi-ASIN brand monitoring — pull the entire review history for every SKU in your portfolio in one bulk run, then track week-over-week sentiment by ASIN.
  • Verified-purchase-only sentiment — filter verified_purchase = true before you compute averages so unverified noise doesn't inflate or sink the score Amazon shows shoppers.
  • Helpful-vote-weighted sentiment — give a 1-star review with 800 helpful votes more weight than a 5-star drive-by, the way actual shoppers do.
  • Country-marketplace splits — same product, different stories on .com vs .co.uk vs .de vs .co.jp. The Anker PowerCore line has years of pooled review history that read very differently in Berlin and Tokyo.
  • Competitor benchmarking — bulk-pull the top three rivals in your category, cluster the complaints, and walk into a roadmap meeting with a list of pain points your competitors haven't fixed yet.
  • BSR-tracked weekly schedules — set a recurring export on your watch list, fire a webhook when a new wave of reviews lands, and catch a review-bombing campaign before the average tanks.

How to export Amazon reviews — step by step

Step 1: Grab the Amazon product URL

Open the product page on any Amazon TLD — say https://www.amazon.com/dp/B0CHX1W1XY. Either the canonical product URL or the /product-reviews/ URL works. The 10-character ASIN is what the exporter uses to address the listing, and the TLD tells it which marketplace to pull from. Switching from .com to .co.uk pulls the UK review pool. Same SKU, different country, different review universe.

Step 2: Paste the URL into the exporter

Open the Amazon Reviews exporter and drop the URL into the input field. Doing your full catalog or your competitor watchlist? Switch to bulk mode and paste one URL per line. Bulk runs return one Excel file per URL, bundled together in a single ZIP at the end of the job — each ASIN stays cleanly separated, which is what you want when you're going to pivot per-product anyway.

Step 3: Pick a format

Excel (.xlsx), CSV, or JSON. Excel is the right pick if you want to sort, filter, and chart in the next ten minutes. CSV is the safest pick for a BI import — Snowflake, BigQuery, Looker, whatever you use. JSON is the right pick when the data is on its way to a notebook, a clustering script, or an LLM that's going to summarize the complaint themes.

Step 4: Start the export

Hit Export. The job runs server-side and paginates through Amazon's review feed for that ASIN until it has every public review, including the helpful votes, the verified-purchase flag, the Vine flag, and the variant the reviewer bought. Bigger ASINs with thousands of reviews take a few minutes. You can close the tab — the file lands in your dashboard and your inbox when it's done.

Step 5: Open the file

Open the .xlsx in Excel, Numbers, or Google Sheets and you're ready to filter, pivot, and chart. Each row is one review, with the columns described in the next section.

Inside the export — what fields you get

Each row is a single Amazon review. You get columns for:

  • reviewer_name — the display name shown on the review.
  • reviewer_profile_url — link to the reviewer's public Amazon profile when one exists.
  • rating — the 1–5 star score.
  • title — the headline the reviewer wrote.
  • review_text — the full review body.
  • verified_purchase — true if Amazon confirmed the buyer purchased the item on Amazon.
  • helpful_votes — community helpful-vote count.
  • variant — the size, color, or spec the buyer chose, when Amazon exposes it.
  • reviewer_location — the country code the reviewer is posting from.
  • vine — true if the review came from the Amazon Vine program.
  • has_image, has_video — flags plus the URLs when present (no binary download).
  • permalink — direct link to the individual review.
  • created_at — review date.

Common workflows

  • Brand monitoring across multiple ASINs — bulk-export your full SKU list weekly, append the deltas into a master sheet, and watch the trend line per ASIN. The first time a 1-star spike shows up before your CS team escalates it, the workflow has paid for itself.
  • Verified-purchase-only sentiment — keep verified_purchase = true and recompute the average. Amazon's verified-purchase badge crackdown after the 2022 review-bomb wave on Russian-product listings made this filter a baseline expectation, not a nice-to-have.
  • Helpful-vote weighting — multiply each rating by (helpful_votes + 1) before averaging. That 1-star teardown of the Anker PowerCore that 1,200 shoppers found helpful tells you more about the product than fifty 5-star one-liners.
  • Country-marketplace splits — pull the same ASIN on .com, .co.uk, .de, and .co.jp, then pivot on reviewer_location. Localization issues, packaging complaints, and shipping damage patterns live in these splits.
  • Competitor benchmarking — drop the top three competitor ASINs into a bulk run, paste the review_text columns into ChatGPT, and ask for the recurring complaint themes. You'll spot the gaps in their roadmap fast.
  • BSR-tracked weekly schedules — schedule a recurring export on your top sellers and use webhook delivery to push new reviews into Slack or BigQuery every Monday morning. Catch review-bombing as it happens, not three weeks later when the average has already moved.

Plan limits and API access

The Free tier returns up to 100 reviews per export, which is enough to evaluate the format and run a small spot-check. Personal scales to 5,000 results per export, Premium to 50,000, and Business to 250,000 — enough to capture the full review history of even the deepest ASINs on Amazon.com. If you'd rather pull reviews on a schedule or trigger an export from your own pipeline, the same job is available through the REST API and via webhooks. See pricing for the full breakdown.

FAQ

  • Which Amazon marketplaces are supported?
    All 18 — .com, .co.uk, .de, .fr, .it, .es, .in, .ca, .com.au, .co.jp, .com.mx, .com.br, .nl, .se, .pl, .com.tr, .ae, and .sa. Marketplace detection is automatic from the URL.
  • Can I get only verified-purchase reviews?
    The export includes the verified_purchase column for every row, so filter on that in Excel after the run. Keeping the unverified rows is useful too — they're often where review-bombing campaigns live and you want them visible, not silently dropped.
  • Are Vine reviews flagged separately?
    Yes. The vine column is true for reviews submitted through the Amazon Vine program, so you can isolate or exclude them when computing organic sentiment.
  • Do I get image and video URLs?
    Yes. has_image and has_video flag whether media is attached, and the URLs are included in the row. The exporter doesn't download the binary files — that keeps the export fast and your sheet small.
  • Can I schedule a weekly export?
    Yes, on Premium and Business. Pair it with webhook delivery to push new reviews straight into Slack or BigQuery each Monday morning. This is how you catch a review-bombing wave before it tanks your average.
  • What if I have hundreds of ASINs to export?
    Use bulk mode. Paste one URL per line and the run returns one file per URL packaged in a single ZIP, so each ASIN's data stays cleanly separated for downstream analysis.