# Walmart Reviews API

> Walmart customer reviews by usItemId as JSON: paged reviews, star distribution, verified-purchase filter, aspects and Walmart's AI summary. US & Canada.

- URL: https://everydata.io/walmart-reviews-api
- Updated: 2026-09-20
- Publisher: everydata.io (https://everydata.io)

Walmart product reviews are one of the richest free sources of product feedback in US retail – millions of verified-purchase reviews, each tagged with the seller who sold the item and the aspects the reviewer talked about. The everydata.io Walmart Reviews API returns them as structured JSON for any usItemId on walmart.com or walmart.ca: paged review lists, the full star distribution, Walmart's own AI-generated review summary and the aspect list that powers the "frequently mentioned" filters on the page.

## Key facts

- Platform: [Walmart Product Data API](https://everydata.io/apis/walmart) (live)
- Main endpoint: `GET /wlm/walmart-lookup-reviews`
- Quota: A call to the Walmart API counts as 1 request against the monthly quota.
- Pricing: 100 free requests a month; paid plans from €30 a month for 5,000 requests (€1.00–€6.00 per 1,000 requests). One quota is shared across all 35 platforms; failed requests (5xx, blocked pages) are not counted.

## What you get

The response echoes your filters (productId, page, sort, domainCode, ratings, vp) and then returns a reviews object. customerReviews is the page of reviews: rating, reviewTitle, reviewText, reviewSubmissionTime, userNickname, a badges array that includes VerifiedPurchaser, positiveFeedback / negativeFeedback vote counts, sellerName and fulfilledBy for the transaction behind the review, itemName and itemId for the exact variant bought, features such as Color, media and photos, and reviewAspects linking the review to aspect ids with a polarity.

Above the list you get the aggregates: totalReviewCount, averageOverallRating, recommendedPercentage, ratingValueOneCount to ratingValueFiveCount and the percentage variants, reviewsWithTextCount and totalMediaCount. Two Walmart-generated blocks make analysis easier: reviewSummary is the AI summary paragraph with a summaryTitle and sentence-level summaryAspects, and bulletReviewSummary lists aspects with aspectName, aspectPillText, aspectPolarity and a one-line aspectSummary. topPositiveReview and topNegativeReview are also included.

- Paged customerReviews with rating, title, text, date, nickname and VerifiedPurchaser badge
- sellerName, fulfilledBy, itemId and itemName per review – know which seller and variant the review belongs to
- Star distribution (ratingValueOneCount … ratingValueFiveCount), averageOverallRating and recommendedPercentage
- aspects[] with id, name and snippetCount; filter reviews by aspect id
- Walmart's AI reviewSummary and bulletReviewSummary with per-aspect polarity
- Filters: sort (relevancy, submission-desc, helpful, rating-desc, rating-asc), vp verified-only, ratings "1,2,5"
- domainCode com or ca for walmart.com and walmart.ca

## How it works

Call GET /wlm/walmart-lookup-reviews with productId set to the usItemId – the number at the end of the product URL or the usItemId field from a search result. Add page and sort to walk through the reviews, ratings to restrict to specific stars, vp=true for verified purchases only, and aspect with an id from reviews.aspects to see only reviews about, say, Charging. The example below is cut to two aspects and one review.

```bash
curl -s "https://api.everydata.io/wlm/walmart-lookup-reviews?productId=5364974201&domainCode=com&page=1&sort=relevancy" \
  -H "x-api-key: YOUR_API_KEY"
```

Response (shortened):

```json
{
  "responseStatus": "PRODUCT_FOUND_RESPONSE",
  "responseMessage": "Product successfully found!",
  "productId": 5364974201,
  "page": 1,
  "sort": "relevancy",
  "domainCode": "com",
  "ratings": [],
  "vp": false,
  "reviews": {
    "activeSort": "relevancy",
    "aspects": [
      {
        "id": "6051",
        "name": "Charging",
        "snippetCount": 140
      },
      {
        "id": "160",
        "name": "Led Light",
        "snippetCount": 107
      }
    ],
    "reviewSummary": {
      "summaryTitle": "Compact and rechargeable fan offers impressive speed and convenience",
      "summary": "Customers praise the product's speed, power level, and charging features, highlighting its strong air current, multiple speed settings, and quick charging.",
      "summaryAspects": [
        {
          "aspectId": 184,
          "aspectStart": 31,
          "aspectEnd": 35,
          "aspectSentiment": "positive"
        }
      ]
    },
    "bulletReviewSummary": {
      "bulletSummaryAspects": [
        {
          "aspectId": "180",
          "aspectName": "noise level",
          "aspectPillText": "Super quiet noise level",
          "aspectRank": 7,
          "aspectPolarity": "Positive",
          "aspectSummary": "Operates at a barely audible sound level."
        }
      ]
    },
    "averageOverallRating": 4.4,
    "totalReviewCount": 2054,
    "reviewsWithTextCount": 670,
    "recommendedPercentage": 87,
    "ratingValueOneCount": 176,
    "ratingValueTwoCount": 67,
    "ratingValueThreeCount": 83,
    "ratingValueFourCount": 143,
    "ratingValueFiveCount": 1585,
    "totalMediaCount": 35,
    "customerReviews": [
      {
        "reviewId": "393593280",
        "rating": 5,
        "recommended": true,
        "reviewTitle": "great deal!",
        "reviewText": "This fan was a lifesaver to survive in hot camping tent! ...",
        "reviewSubmissionTime": "7/28/2025",
        "userNickname": "colette",
        "badges": [
          {
            "badgeType": "Custom",
            "contentType": "REVIEW",
            "id": "VerifiedPurchaser"
          }
        ],
        "userBadges": [
          {
            "badgeId": "TOP_REVIEWER",
            "badgeValue": "TOP_REVIEWER"
          }
        ],
        "positiveFeedback": 0,
        "negativeFeedback": 0,
        "sellerName": "GVDV Hub",
        "fulfilledBy": "Walmart",
        "itemId": "14779413575",
        "features": [
          {
            "name": "Color",
            "value": "Yellow/Black"
          }
        ],
        "externalSource": "bazaarvoice"
      }
    ]
  }
}
```

Paging is page-number based. Keep sort fixed while you walk pages – submission-desc is the safest choice for incremental syncs because new reviews always land on page one, so you can stop as soon as you hit a reviewId you already stored. totalReviewCount counts all ratings; reviewsWithTextCount is the number you can actually page through, since star-only ratings have no review body.

Aspects are Walmart's topic tags. Fetch page one without filters, read reviews.aspects to see what customers mention (Charging, Led Light …) and how often, then request aspect=<id> to isolate those reviews; each review's reviewAspects tells you the polarity for every aspect it touches. Combine with ratings=1,2 to surface negative feedback on a specific topic quickly.

Walmart pools reviews across variants and, for marketplace items, across sellers – sellerName, fulfilledBy, itemId and itemName on each review tell you exactly which offer the reviewer bought. reviewSubmissionTime is a US-formatted date string (M/D/YYYY); parse it explicitly.

## What teams build with this

### Voice-of-customer analytics

Brands aggregate their reviews per aspect and polarity to find the defects and delights customers mention most, using Walmart's aspect ids instead of training a classifier first.

### Marketplace seller quality control

Marketplace sellers filter negative reviews (ratings=1,2) that carry their sellerName to distinguish product complaints from fulfilment problems and to respond quickly.

### Product research and sourcing

Before listing a product, compare star distributions, recommendedPercentage and the AI summary of competing items to gauge demand and quality risks.

### Review monitoring and alerts

Poll submission-desc daily, diff on reviewId and alert your team about new one-star reviews or reviews with photos on key items.

## Pricing

A page of reviews is one request, regardless of the filters applied. Monitoring 200 items daily for new reviews costs about 6,000 requests a month, which fits the Production plan (50,000 requests, €80) with room for full historical backfills; a first crawl of a 2,000-review item needs roughly 70 pages. Upstream 5xx responses are never billed.

## FAQ

### What is the productId parameter?

It is Walmart's usItemId – the number at the end of a product URL such as /ip/<slug>/5364974201, also returned as usItemId in search results.

### Can I fetch only verified-purchase reviews?

Yes, set vp=true. Each review also carries a VerifiedPurchaser badge in badges[], so you can verify the flag in the data itself.

### How do I filter by star rating?

Pass ratings as a comma-separated list, for example ratings=1,2,5. The response echoes the applied list in the ratings field.

### What is the AI review summary?

reviewSummary is the summary Walmart itself generates and displays on the product page, including a title and aspect spans with sentiment. bulletReviewSummary contains the shorter aspect pills with a polarity and one-sentence summary each.

### Does this work for walmart.ca?

Yes. Set domainCode=ca with the Canadian usItemId; the default is com.

### How many reviews per page?

Walmart returns its standard page size, typically 20 reviews with text. Use reviewsWithTextCount to compute how many pages you need.

## Related use cases

- [Walmart Price Tracker & Product API](https://everydata.io/walmart-product-api)
- [Walmart Product Search API](https://everydata.io/walmart-search-api)
- [Amazon Review Scraper API](https://everydata.io/amazon-reviews-api)

## More

- [All platforms](https://everydata.io/apis) · [Pricing](https://everydata.io/pricing) · [API reference](https://everydata.io/docs) · [Getting started](https://everydata.io/docs/getting-started) · [MCP server](https://everydata.io/docs/mcp) · [Status](https://everydata.io/status)
- Machine-readable: [llms.txt](https://everydata.io/llms.txt), [llms-full.txt](https://everydata.io/llms-full.txt), [OpenAPI](https://api.everydata.io/openapi.json)