Walmart.com is the second-biggest review surface in US e-commerce, right behind Amazon. It also has a quirk Amazon doesn't: a single product page mixes Walmart's own first-party listing with whatever third-party Marketplace sellers have piggybacked onto it, and the headline star rating averages across all of them. If you've ever watched your own SKU's average tank because another seller shipped a bad batch on the same listing, you already know why getting this data offline matters. This guide walks you through using ExportComments' Walmart Reviews exporter to pull every review for any product into Excel, CSV, or JSON in one pass.

Why export Walmart reviews

Walmart's review form asks shoppers for more than most retailers do: star rating, title, body, separate pros and cons, a would-recommend signal, and a verified-buyer tag. The seller dimension is the part that bites. Picture an electronics drop-shipper who ranks #1 on a popular speaker listing — a competing seller floods the same product page with a defective batch, the negative reviews land on the shared listing, and suddenly the drop-shipper's offer reads "3.4 stars" even though their own units never had a problem. You can't fix that from inside the storefront UI. You can fix it in a spreadsheet.

  • Marketplace seller triage — filter to seller_name = your-brand and rating <= 2, then route to customer service via Zapier the moment a new low-star review lands.
  • Mine pros and cons without an NLP pipeline — Walmart already collects them as separate fields, so you can pivot directly on what shoppers love and what they regret.
  • Compute verified-only sentiment by filtering on the verified_purchase column to strip out drive-by ratings.
  • Track variation-level quality on multi-SKU listings (size, color, pack count) to see which variant is dragging the rating down.
  • Benchmark a competitor's third-party listing against yours on the same product page using seller_name as the cut.
  • Detect helpful-vote patterns over time to find the reviews shoppers actually trust.

How to export Walmart reviews — step by step

Step 1: Grab the Walmart product URL

Open the product page on Walmart.com. Something like https://www.walmart.com/ip/Apple-AirPods-Pro-2nd-Generation/1756358944. Any canonical product URL works. You don't need to scroll into the reviews section first — the numeric Walmart Item ID at the end of the URL is what the exporter keys off.

Step 2: Paste the URL into the exporter

Open the Walmart Reviews exporter and paste the URL into the input field. Got a list to pull? A category, a competitor's catalog, your full SKU portfolio? 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, so each product stays cleanly separated.

Step 3: Pick a format

Excel (.xlsx), CSV, or JSON. Excel if you want to pivot, filter, and chart immediately. CSV if you're feeding a BI tool. JSON if you're piping straight into a notebook or a sentiment model.

Step 4: Start the export

Click Export. The job runs server-side and paginates through Walmart's review feed until it has every public review for that product — pros and cons, seller attribution, the variation each review is tied to. Big listings with thousands of reviews take a couple of minutes. You can close the tab; the file lands in your dashboard and your inbox when it's ready.

Step 5: Open the file

Open the .xlsx in Excel, Numbers, or Google Sheets. Each row is one review. Columns below.

Inside the export — what fields you get

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

  • Reviewer name — the display name shown on the review.
  • Reviewer location — the city/state Walmart attaches to the review when available.
  • Rating — the 1–5 star score.
  • Title — the short headline the shopper wrote.
  • Body — the full review text.
  • Pros and Cons — the two structured fields Walmart prompts shoppers to fill in separately.
  • Verified purchase — true if Walmart confirmed the buyer purchased the item.
  • Helpful count — how many shoppers marked the review helpful.
  • Seller name — the seller the review is attributed to (Walmart.com or the Marketplace seller's brand). This is the column that lets Marketplace sellers isolate reviews for their own SKU on a shared product page.
  • Variation — the size, color, pack count, or other variant the reviewer bought.
  • Created at and Updated at — original timestamp and last-edit timestamp in UTC.

Common workflows

  • Marketplace seller triage — filter to seller_name = your-brand and rating <= 2, then push the result through Zapier or Make into a customer-service queue. New 1-star reviews hit an agent within minutes instead of festering for a week.
  • Pros/cons mining without NLP — pivot the pros and cons columns to count exact phrases. Walmart already did the structured-input work, so the top complaints surface without an LLM in the loop.
  • Verified-only sentiment — filter to verified_purchase = true before computing your average. The headline number on the listing includes unverified ratings; the one you report internally shouldn't.
  • Variation-level quality — pivot rating by variation to find the size or color dragging the listing down. A 4.6 average can hide a 3.2 sitting on the "XL black" variant — the exact kind of thing the storefront UI will never tell you.
  • Competitor benchmarking — export reviews for the same product page and split by seller_name to compare how shoppers rate your offer versus the third-party seller next to you.
  • Helpful-negative mining — sort by helpful_count within 1–2 star reviews to find the criticism shoppers have already endorsed by upvote. That's almost always the most actionable feedback for product and packaging fixes.

Plan limits and API access

The Free tier returns up to 100 reviews per export, which is enough to evaluate the format. Personal scales to 5,000 results per export, Premium to 50,000, and Business to 250,000 — enough to capture every review for the largest catalogs on Walmart.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

  • Can I isolate reviews for my Marketplace SKU on a shared product page?
    Yes. Filter the export on the seller_name column to keep only the reviews attributed to your seller name. This is the single most-used field for Marketplace operators, because Walmart pools reviews across all sellers on the same listing.
  • Are pros and cons really separate columns?
    Yes. Walmart's review form asks the shopper to fill in pros and cons in two distinct fields, and the export keeps them separate so you can pivot on each without parsing free text.
  • Does the export include unverified reviews?
    Yes — and they're flagged. The verified_purchase column is true/false, so you can include or exclude them depending on whether you want the headline number or a verified-only signal.
  • What if a product has multiple variations?
    Each review carries the variation the shopper bought in the variation column. Pivoting rating by variation is the fastest way to spot a quality issue on one specific size or color.
  • Can I schedule a weekly export to monitor new reviews?
    Yes. Scheduled exports are available on Premium and Business — useful for marketplace sellers who want a fresh CSV every Monday morning without re-running the job by hand. Pair with webhook delivery to push results straight into your CS or BI stack.
  • What if I have hundreds of products to export?
    Use bulk mode: paste one Walmart URL per line and the run returns one file per URL packaged in a single ZIP, so each product's data stays cleanly separated for downstream analysis.