Walmart Seller Analyzer — $2/1K · Prices & Margin Estimates

Walmart Seller Analyzer | Product Samples, Scenarios & Niche Research

Analyze public Walmart US product observations, storefront samples or keyword search results. Get observed price, seller, reviews, badges, availability, fulfillment signals and explicitly assumed cost/opportunity scenarios. These are public samples, not verified seller turnover or private Seller Center sales. Unknown demand remains null. No Walmart login/API key required; Apify API/MCP requires your token/account.

Walmart product and storefront research for merchandising teams

Use this Walmart Seller Analyzer for public product observations and cost scenarios you can inspect. A merchant can review a known item, an agency can inspect a bounded storefront sample, and a researcher can build a keyword shortlist. The product does not reveal a seller's private sales, verified turnover or a complete secondary-offer history.

Workflow	Useful evidence	Main qualification
Inspect a known item	usItemId, observed price, seller and detailStatus	Requested item identity must match
Review a storefront sample	requestedSellerId, sellerAttribution and coverage	Store association does not prove exclusive seller sales
Compare a keyword sample	Returned product IDs, query and opportunity reasons	Heuristic ordering is not official market ranking
Test your cost assumptions	COGS/shipping/WFS/ads/refund/referral scenario fields	Model inputs are not verified contract fees or actual profit
Prepare a research export	Source warnings, location observations and null estimates	Requested ZIP/store is not enforced source location

Begin with a known product or a very small keyword sample. A useful product row can be billed even when revenue/profit estimates remain unknown. Keep source observations and hypothetical financial scenarios in separate worksheet columns.

First run

{"mode":"niche","searchQueries":["air fryer"],"maxResultsPerQuery":3,"maxDetailedProducts":3}

Use product rows for comparison; inspect SUMMARY catalogs for coverage/continuation and ERRORS for source failures. Export JSON/CSV/Excel through Apify. A product can be useful with unknown revenue; do not interpret null as zero sales.

Modes

• seller: sellerUrls such as https://www.walmart.com/global/seller/101644973. Returns observed storefront products plus a free seller_summary. Storefront carousels are incomplete and subsequent pages may repeat; catalogCoverageComplete remains false. This is not an entire seller catalog or turnover report. requestedSellerId/sellerAttribution distinguish the requested storefront from the featured product seller. Association does not prove exclusive seller sales or Buy Box share.
• product: productUrls or numeric productIds, e.g. 16954719851. Product identity must match the requested ID. Duplicate products across inputs are delivered once.
• niche: searchQueries, e.g. air fryer. Real keyword pages are read and deduplicated, then the collected sample is ordered by a heuristic opportunity score. This is not Walmart's official ranking or a full market census.

Interpret the evidence: agents and workflows

price, rating, reviewCount, sponsored, isWfs and offerCount remain null when unavailable. offerCount is an observed count when present; no complete secondary offers list or Buy Box-share estimate is promised. detailStatus identifies listing_only, complete, source_unavailable or identity_mismatch. Failed detail requests retain useful listing observations with warnings.

An observed bought-since-yesterday bucket is extrapolated into hypothetical monthly units as 30/42/60 times the bucket count, assuming it repeats. This is a scenario, not a measured monthly range, guaranteed lower bound or confidence interval. Lifetime reviews and cart counts do not produce monthly sales estimates. Without a usable purchase badge, estimatedMonthlyUnits/Revenue/Profit fields are null. Estimates concern the product across sellers; estimatedSellerRevenueMid remains null.

seller_summary exposes matched/sample/estimate counts and modeledProductRevenueMidSubtotal for products with usable signals only. Its estimatedMonthlyRevenueMid and estimatedSellerRevenueMid stay null: no entire seller revenue is established. estimateCoverageComplete is about modeled rows, separate from catalogCoverageComplete. Do not treat an incomplete subtotal as a complete portfolio.

Profit is a scenario using COGS, shipping, WFS, advertising, refunds and an assumed category referral fee. Optional referralFeePercent overrides the assumption for your contract. Defaults are COGS30%, refund2%, shipping/WFS/ads0. ROI uses COGS as its base; opportunityScore and dataCoverageScore are heuristics, not accuracy, probability or investment advice. Verify current contract-specific rates with the official fee schedule.

requestedPostalCode/requestedStoreId are recorded intent only. This Actor does not force or verify a session ZIP/store. locationText/observedPostalCode are observations; mismatch warnings must be retained when comparing prices and availability. Missing location is unknown, not confirmation of the requested ZIP.

Input limits

Field	Default	Meaning
mode	niche	seller, product or niche
sellerUrls/productUrls/productIds/searchQueries	mode-dependent	Maximum20 targets per array; public HTTPS Walmart URLs and numeric item/storefront IDs only
maxProductsPerSeller/maxResultsPerQuery	5	1–500 sample product limit per target; event budget may return fewer
includeProductDetails	true	Fetch optional exact-item details
maxDetailedProducts	5	0–100 detail attempts; zero disables enrichment
maxConcurrency	2	1–10 parallel detail requests
startPage/startOffset	1/0	Start page and unique-card offset on that page
maxPages	10	1–20 catalog requests; cap/failed/repeated pages produce partial coverage
postalCode/storeId	95829/empty	Recorded requested location only
costOfGoodsPercent/refundRatePercent/advertisingRatePercent	30/2/0	Cost scenario percentages
shippingCostPerOrderUsd/wfsFeePerOrderUsd	0/0	Per-order scenario costs
referralFeePercent	category assumption	Optional0–100 override
proxyConfiguration	Apify Residential	Supports Apify proxy settings or custom proxyUrls; proxy/compute costs are separate

Use SUMMARY catalog nextInput to continue, preserving your limits and cost assumptions. Ordering can change between requests; compare IDs. Seller repeated-page detection discloses source limits and does not fabricate new pages. Complete search coverage is claimed only after a valid empty page, not after reaching a row/page cap. Change monitoring is not implemented in this release.

Pricing and failures

Product analyzed: $2 per 1,000 useful delivered rows on every plan. Startup is $0.00005 per GB, minimum one event, not an unconditional per-run fee at any memory size. User also pays platform/proxy usage. Seller summaries are free. Failed/skipped products have no product event; source attempts can still consume resources. A useful product observation with unknown estimates is billable once.

Set maxTotalChargeUsd on the run API to cap event charges; startup is accounted for before product work. SUMMARY budgetLimited discloses stops. Errors are stored in ERRORS, not paid error rows. Pure source failure with no observations fails; a valid empty search can succeed. Public Walmart access can be temporarily unavailable or return a challenge. A successful run alone does not establish complete or accurate financial estimates.

REST API

curl -X POST 'https://api.apify.com/v2/acts/apivault_labs~walmart-seller-revenue-product-opportunity-analyzer/runs?build=latest&memory=512&timeout=360&maxTotalChargeUsd=0.05' \
  -H "Authorization: Bearer $APIFY_TOKEN" -H 'Content-Type: application/json' \
  -d '{"mode":"product","productIds":["16954719851"]}'

Poll /v2/actor-runs/{runId} to terminal. Read /v2/datasets/{defaultDatasetId}/items?clean=true (format=csv for export), then /v2/key-value-stores/{defaultKeyValueStoreId}/records/SUMMARY and ERRORS. Keep tokens out of shared input/URLs; use Bearer headers.

MCP and agent instructions

Connect an authenticated client to https://mcp.apify.com/?tools=apivault_labs/walmart-seller-revenue-product-opportunity-analyzer. Discover the Actor with tools/list and use the exact returned tool name. The hosted server currently abbreviates this long slug to apivault_labs--walmart-seller-revenue-product-opportunity-a-dbe5. tools/call takes normal inputs plus waitSecs:30. Use returned run/storage IDs to poll and read get-dataset-items and get-key-value-store-record for SUMMARY/ERRORS. For strict event caps start through REST maxTotalChargeUsd.

Agents must preserve nulls, sellerAttribution, estimateScope/status, detailStatus, observed location, warnings and catalog coverage. Keep source observations separate from extrapolated scenarios; never describe a product-wide estimate or sample subtotal as seller turnover. Follow actual nextInput and inspect stopReason rather than assuming complete pagination. In n8n/Make/Zapier map those evidence fields alongside price/ID, and split free seller_summary rows from paid products.

Small Walmart research inputs

The item/storefront below are recorded examples and can become unavailable. Use exact IDs relevant to your research. These examples do not establish current stock, a specific observed ZIP or a profitable product.

Inspect one exact Walmart item

{
  "mode": "product",
  "productIds": [
    "16954719851"
  ],
  "includeProductDetails": true,
  "maxDetailedProducts": 1,
  "maxConcurrency": 1
}

Check the returned usItemId and detailStatus, then inspect warnings and the raw source observations. A missing purchase badge means modeled monthly units/revenue can be null; it is not evidence of zero demand.

Use an explicit product URL

{
  "mode": "product",
  "productUrls": [
    "https://www.walmart.com/ip/16954719851"
  ],
  "includeProductDetails": true,
  "maxDetailedProducts": 1,
  "maxConcurrency": 1
}

Numeric item identity is preserved. The Actor may retain a useful listing observation if optional detail enrichment fails; that remains a billable product row with an explicit limitation.

Build a small listing-only niche sample

{
  "mode": "niche",
  "searchQueries": [
    "air fryer"
  ],
  "maxResultsPerQuery": 3,
  "maxPages": 1,
  "includeProductDetails": false,
  "maxDetailedProducts": 0,
  "maxConcurrency": 1
}

This intentionally avoids detail enrichment. maxResultsPerQuery caps collected products, not the whole niche. Compare actual listing fields and coverage before making a sourcing decision.

Apply explicit cost assumptions to one product

{
  "mode": "product",
  "productIds": [
    "16954719851"
  ],
  "maxDetailedProducts": 1,
  "maxConcurrency": 1,
  "costOfGoodsPercent": 40,
  "shippingCostPerOrderUsd": 4,
  "advertisingRatePercent": 8,
  "refundRatePercent": 3,
  "referralFeePercent": 15
}

All percentages and per-order values here are illustrative assumptions, not verified Walmart charges or a recommendation. Replace them with your own documented costs. A model with no supported demand evidence still cannot supply measured monthly profit.

Inspect a bounded seller-storefront sample

{
  "mode": "seller",
  "sellerUrls": [
    "https://www.walmart.com/global/seller/101644973"
  ],
  "maxProductsPerSeller": 2,
  "maxPages": 1,
  "maxDetailedProducts": 1,
  "maxConcurrency": 1
}

The free seller summary is about the observed sample. Repeated storefront carousels and incomplete seller attribution prevent it being treated as full catalogue turnover. Inspect requestedSellerId and the featured seller separately.

Keep item evidence separate from scenario arithmetic

Preserve scrapedAt, query/target identity, detailStatus, estimateScope/status and warnings when exporting. Missing price, rating, purchase badge, WFS or offer count is unknown. A source field omitted by the page is not an independently measured zero. An observed count of offers does not establish a complete offers list or the target seller's Buy Box share.

The bought-since-yesterday bucket can support a hypothetical monthly extrapolation only under the stated repeated-demand assumption. Reviews accumulated over a product's lifetime and current cart observations do not independently provide monthly sales. A subtotal over products with usable badges must not be presented as the complete seller's revenue. estimatedSellerRevenueMid remains unknown in this contract.

Cost inputs change the scenario, not the observed item data. Check the base used for ROI and margin before comparing outputs. Default zero shipping/WFS/advertising values are modeling defaults, not proof those expenses do not exist. When working from a contract, supply the appropriate assumptions and retain their source/date outside the Actor. No guarantee of investment return or realized profit follows from an opportunity score.

Record both requestedPostalCode/requestedStoreId and observed location context. A recorded request is not a session-location enforcement feature. If location is missing or mismatched, do not compare the observed offer as if it were a verified price in your requested store.

Walmart analyzer API questions

Can I get the seller's entire business revenue? No. Storefront samples can be incomplete or repeat, and product-level demand is across sellers. Preserve free summary coverage and unknown seller-attribution fields rather than filling an estimated turnover number.

Does a useful row need a revenue estimate? No. Valid item identity plus available listing observations can form a useful paid product record while estimates remain null. Optional detail failure does not necessarily invalidate the listing evidence.

What is billed? One delivered product-analysis row costs $0.002 on every saved tier. Seller summaries and diagnostics are free. At 512 MB, one product has $0.00205 in event fees including the minimum startup, before separately paid compute/proxy/storage. Dataset count can exceed paid-product count when a free summary is included.

How does continuation work? Follow the actual SUMMARY catalog nextInput. It preserves selectors, offsets and assumptions, but dynamic page order and repeated seller pages prevent a frozen-snapshot guarantee. A returned cap is not evidence of source exhaustion.

Can I monitor changes automatically? Persistent change monitoring is not implemented in this release. Save dated observations externally if you need comparisons, and match item/seller/location scope carefully.

Can I use n8n or an AI assistant? Use the matched integrations below or API/MCP, poll the existing run and read its returned stores. Keep source warnings and nullable estimates in the destination; do not let a summary convert a hypothetical product scenario into verified seller sales.

n8n and developer integrations

Use this Actor in your automation or application with the existing community integrations below. An Apify account and API token are required; Actor and platform charges follow the current pricing.

• n8n community node: n8n-nodes-apivault-walmart-seller-analyzer. Install package name n8n-nodes-apivault-walmart-seller-analyzer in a self-hosted n8n instance that permits community packages.
• Python client and examples: GitHub.
• n8n source and usage: GitHub.
• Python client and examples: GitHub.
• n8n source and usage: GitHub.

These are community packages. Publication on npm does not establish n8n verification or availability in n8n Cloud. Review the linked package README for supported inputs and setup.