A Vietnamese seller pinged us last month with a problem that sounds simple until you try to solve it on the Lazada UI. Their product sat at a 4.2-star average and they couldn't figure out why. The page shows the average; it does not show, in any sortable way, that one specific size variation — the XL — was at 3.1 stars and dragging the whole listing down. They needed the reviews in a spreadsheet with the variation column intact, across all five storefronts they sell on. That's the kind of question Lazada's product page won't answer for you. This guide walks you through using ExportComments' Lazada Reviews exporter to pull every public review for any product into Excel, CSV, or JSON — across all six Southeast Asian storefronts.

Why export Lazada reviews

Lazada is six marketplaces wearing one logo. lazada.co.id, lazada.vn, lazada.sg, lazada.co.th, lazada.com.my, and lazada.com.ph each have their own catalogs, seller accounts, review pools, and languages. The same product sold by the same seller in Indonesia and the Philippines will have two separate review streams that never mix on the page. If you sell on more than one storefront, or if you're doing competitive research across the region, the only way to see the full picture is to pull each storefront and stack them in one spreadsheet. Once it's there:

  • Variation pivot — group reviews by SKU variation (size, color, capacity) and find which variant is dragging the average down. Lazada exposes variation per review; the page just won't let you sort on it.
  • Seller-reply audit — measure how many 1- and 2-star reviews your team actually responded to. Reply rate on negatives quietly correlates with recovery curves.
  • Multi-market validation — launching in Thailand? Pull Indonesian and Vietnamese reviews of the same product first. Cross-market grievances tend to be real product issues; single-market ones are usually shipping or expectation gaps.
  • Sale-cycle review-bomb pattern — overlay timestamps against Lazada's mega-sales (11.11, 12.12, 9.9). Spikes and crashes both happen around these dates.
  • Verified-buyer slice — split by the verified flag and re-run the average. Unverified reviews skew differently; you want to know by how much.
  • Photo-review extraction — buyer-uploaded photo URLs sit in their own column, ready for a UGC bank.

How to export — step by step

Step 1: Grab the Lazada product URL

Open the product page on the right storefront — for example https://www.lazada.co.id/products/i123456789-s987654321.html for an Indonesian listing. Any canonical product URL works; you don't need to scroll into the reviews tab first. The exporter reads the product ID and the storefront TLD from the URL, so an .sg link pulls the Singapore reviews and a .vn link pulls the Vietnamese ones. Same product, different storefronts, different URLs.

Step 2: Paste the URL into the exporter

Head to the Lazada Reviews exporter and paste the URL into the input field. If you're auditing a product across the whole region, switch to bulk mode and paste one URL per storefront, one per line. Bulk runs return one Excel file per URL, bundled together in a single ZIP at the end of the job, so the Indonesian reviews stay separate from the Thai ones — which is exactly what you want when you're going to pivot by storefront.

Step 3: Pick a format

Excel (.xlsx) is the default for a reason — you almost certainly want to pivot on variation, rating, and verified flag the second the file lands. CSV is the cleanest pick if it's heading into a BI pipeline or a spreadsheet that already has an import template. JSON is the right pick if you're feeding the export into a sentiment model or piping it through a notebook for the cross-market analysis described above.

Step 4: Start the export

Click Export. The job runs server-side and paginates through Lazada's review feed for that product on that storefront until it has every public review — variation, verified-purchase flag, seller reply where present, and any buyer-uploaded photo URLs. Popular listings with thousands of reviews take a few minutes; close the tab and the file lands in your dashboard and inbox when it's ready.

Step 5: Open the file

Open the .xlsx in Excel, Numbers, or Google Sheets. Each row is one review, each column is one field, and you're ready to filter, pivot, and chart.

Inside the export — what fields you get

Each row is a single Lazada review. You'll find columns for:

  • Reviewer name — the display name shown on the listing.
  • Rating — the 1–5 star score the buyer left.
  • Title — short headline the reviewer wrote, when present.
  • Body — full review text in the storefront's primary language.
  • Variation — the specific SKU variation the buyer purchased (size, color, capacity).
  • Verified buyer — true if Lazada flagged the review as a verified purchase.
  • Photos — URLs of any buyer-uploaded review photos.
  • Helpful count — how many other shoppers marked the review useful.
  • Seller reply — the seller's response text, if they posted one.
  • Storefront — which Lazada TLD the review came from (so a stacked multi-market export stays self-describing).
  • Created at — review timestamp in UTC.

Common workflows

  • The variation autopsy — pivot rating by variation. The variation a full star below the listing average is your problem SKU. Usually it's a size that runs small or a color that arrives different from the photo.
  • Seller-reply audit — filter to ratings 1 and 2 and count rows with a non-empty seller reply. Below 60% reply rate on negatives is leaving listing recovery on the table.
  • Multi-market validation — export the same product across three or more storefronts and pivot rating by storefront. Issues in every storefront are the product. Issues in only one are usually fulfillment or expectation mismatch local to that market.
  • 11.11 / 12.12 / 9.9 review bomb — bucket the timestamp column by week and overlay against Lazada's sale calendar. Mega-sale weeks reliably produce both review spikes and review crashes when shipping slips.
  • Verified-vs-unverified gap — split the export by the verified flag and recalculate the average for each subset. The gap shows how much your headline number is shaped by drive-by raters.
  • UGC photo bank — filter for the photos column being non-empty and pull the highest-rated photos for your product page or ad creatives. Real lighting, real market, free.

Plan limits and API access

The Free tier returns up to 100 reviews per export, which is plenty to evaluate the format and shape of the data. Personal scales to 5,000 reviews per export, Premium to 50,000, and Business to 250,000 — enough to capture every review for the deepest catalog listings on the platform. If you'd rather pull reviews on a schedule (say, refresh your top 200 SKUs every Monday morning) 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

  • Does the exporter work across all six Lazada storefronts?
    Yes — .co.id, .vn, .sg, .co.th, .com.my, and .com.ph all work. Each storefront is its own review pool, so the URL you paste decides which market's reviews you get.
  • How do I see which size or color variant is dragging the rating down?
    The variation column is included on every row. Pivot rating by variation in the spreadsheet — the variant a full star below the listing average is almost always the issue.
  • Can I export the seller's reply along with the review?
    Yes. The seller reply text sits in its own column so you can audit your team's response rate on 1- and 2-star reviews directly.
  • What about reviews left during 11.11 or 12.12 sales?
    They're all included — the timestamp column lets you bucket by week and see the sale-cycle pattern explicitly. Both review spikes and shipping-related dips show up clearly around the mega-sale dates.
  • Can I tell verified buyers from unverified ones?
    Yes. The verified-buyer flag is a separate column you can filter on. Splitting the average by verified versus unverified is one of the fastest ways to spot pattern drift.
  • What if I want to refresh fifty product listings every week?
    Use bulk mode — paste one URL per line and the run returns one file per URL packaged into a ZIP. For full automation, schedule the same job through the REST API or trigger it via webhooks.