AmazonAmazonIf you’re sourcing a product, vetting a supplier, or sizing up a competitor’s listing, you’ll eventually end up reading reviews on both Amazon and AliExpress. They look similar on the surface (stars, text, a date), but the two platforms expose genuinely different information, attract different buyers, and reward different research habits. Knowing what each one actually gives you before you start exporting saves a lot of wasted spreadsheet time.
This isn’t a “which platform is better” argument. It’s a practical breakdown of what you get from each, so you can pick the right one (or both) for the research question in front of you.
When you export Amazon reviews with ExportReviews, each row includes:
The two fields that matter most for research are the review title and helpful votes. A review title is a compressed version of the reviewer’s opinion, often written before the emotion has cooled, which makes it useful for scanning hundreds of rows quickly without reading every paragraph. Helpful votes tell you which complaints or praise other shoppers actually agreed with, rather than treating every review as equally weighted. A one-star review with dozens of helpful votes describing a recurring defect is a very different signal than a one-star review nobody engaged with.
Reviewer name and verified purchase together give you a sense of how much weight to put on a given review. Verified purchase confirms an actual transaction; the reviewer name, while limited, at least lets you spot the same buyer showing up across a seller’s other listings, which sometimes matters for competitor research.
Exporting AliExpress reviews gives you:
No title field on AliExpress, and no helpful votes. Instead, the standout fields are country and photos. Country tells you where the buyer is shipping from, which is genuinely useful when you’re evaluating a supplier: a product with strong ratings mostly from buyers in one region and weak ratings from another can point to shipping damage, customs issues, or regional quality inconsistency rather than a flaw in the product itself.
Photos are arguably the single most useful AliExpress review field for sourcing work. Buyers on AliExpress post product photos far more often than on most other platforms, and those photos show you the item as it actually arrived, not as the listing photos present it. When you’re vetting a supplier before placing a bulk order, buyer photos are often more reliable than the product page itself.
Both platforms expose product variant and verified purchase, which matters more than it sounds. Reviews attached to the wrong variant (wrong color, wrong size, wrong bundle) are one of the most common sources of bad research conclusions, so having variant data on every row lets you filter it out before it skews your read.
An Amazon listing and an AliExpress listing with the same average rating are not describing the same purchase experience. Amazon’s buyer base skews toward shoppers expecting fast, reliable domestic shipping and standard return policies, so complaints tend to focus heavily on the product itself once shipping issues are filtered out. AliExpress buyers are, by the nature of the platform, more accustomed to longer shipping windows and cross-border logistics, so when they complain specifically about the product (not the wait), that complaint carries more weight, since it survived a buyer pool that’s already more shipping-tolerant.
This is exactly why exporting and filtering matters more than reading a few reviews on the page. A handful of visible reviews on either platform can be recent, unrepresentative, or buried under sort order. A full export lets you sort by date, filter by variant, and separate shipping noise from product noise.
For dropshippers and private-label sellers, AliExpress review exports are usually about de-risking a supplier before committing to a bulk order. Pull the full review set, sort by date to check whether quality has changed recently, and scan the photos field for recurring visual defects across multiple buyers rather than a single unlucky review. Country data helps you separate “this product has a quality issue” from “this specific shipping route has a problem.”
For Amazon sellers, review exports are more often about understanding what a competing listing’s buyers actually like or dislike, at scale. Sorting by helpful votes surfaces the complaints that resonated with the most people, which is a stronger signal than sorting by recency alone. Cross-referencing review title against review text lets you quickly build a list of recurring themes (packaging complaints, sizing confusion, a specific feature buyers love) without reading every single review in full.
This is where the product variant field earns its place on both platforms. A product with an overall solid rating can still have one specific color, size, or bundle option quietly dragging down satisfaction. Filtering an export by variant is the fastest way to catch that pattern, whether you’re deciding which variant to source on AliExpress or which variant a competitor is struggling with on Amazon.
ExportReviews runs the extraction locally, in your browser, using your own logged-in session on the page you’re viewing. The review content, ratings, and reviewer details go straight from the page into your CSV, Excel, or JSON file; none of it is sent to any server. The backend only tracks anonymous counters, like how many credits you’ve used, never the actual review data.
You get the first 25 exported rows free, no account needed, which is usually enough to test whether a listing or supplier is worth a deeper look. Beyond that, 1 credit equals 1 exported row, and credits never expire; they’re also shared across every ExportReviews exporter, so credits you buy for Amazon work just as well when you switch to AliExpress. If a platform changes its layout and extraction breaks, you’ll see an honest message telling you the platform changed, rather than a CSV that looks fine but is silently wrong or empty.
For a full research picture, most sellers end up using both exporters: AliExpress reviews (with photos and country data) to vet the supplier side, and Amazon reviews (with titles and helpful votes) to understand how the finished listing performs with real buyers. The two how-to guides walk through the export process for each platform in detail: exporting Amazon reviews to CSV, and exporting AliExpress reviews to CSV.
A last note on responsible use: only export reviews that are publicly visible on the page you’re browsing, respect each platform’s terms of service, and treat reviewer names with the same care you’d want applied to your own data if you were the one leaving the review.
Yes. ExportReviews has a dedicated exporter for each platform (Amazon and AliExpress are separate extensions, each built for that platform's page structure), and credits are shared across every ExportReviews exporter, so you don't buy separate credit pools for each site.
No. Extraction runs locally in your browser using your own session on the page you're viewing. Review text, ratings, and reviewer details never leave your machine. The backend only receives anonymous counters (like credits used), never review content.
It comes down to how each platform designs its review display. AliExpress surfaces the buyer's country as part of its cross-border shopping experience, while Amazon shows a reviewer name and a helpful-votes count instead. ExportReviews exports exactly what each platform exposes, nothing invented.
Yes. The first 25 exported rows are free with no account required. After that, 1 credit equals 1 exported row, and credits never expire.
If a platform change breaks extraction, ExportReviews shows an honest message telling you the platform changed, instead of silently producing an empty or incorrect CSV. You're never left guessing whether the data is trustworthy.
Ready to export Amazon reviews?
Export the customer reviews of any Amazon product to CSV, Excel or JSON. Rating, title, full text, verified purchase, helpful votes and variant, in your browser.
See the Amazon exporter