PRODUCT DATA GUIDE

When your product feed and product page disagree

Three public products · No account or card

Check price, stock, variants and structured data across a product feed and its landing page.

Measured evidenceCatalog-level diagnosticsImplementation-ready findings

Checking one product? Use the free Product Schema Checker to review brand, GTIN/MPN, price and availability in Product JSON-LD.

A product feed says a jacket costs $89 and is in stock. The product page shows $99 and “sold out.” Its JSON-LD still says $89 and InStock. Which value should a shopping system trust?

This is a concrete data problem, not a mysterious AI visibility score. Google Merchant Center compares product data with landing pages and structured data; price or availability differences can trigger warnings, automatic updates, or disapproval, depending on the case and settings. Other shopping systems have their own data paths. A clean Google feed alone does not prove placement in ChatGPT or any AI assistant.

Price or availability mismatch: a quick triage checklist

If Merchant Center reports a price or availability mismatch, start with the exact affected item rather than changing the whole feed:

  1. Open its landing URL with the same variant, destination country and currency as the feed row.
  2. Compare the purchasable price and stock status with the corresponding Offer in the page markup.
  3. Compare both with the feed item, including any sale-price dates and selected variant identifiers.
  4. Record the last feed update and page-cache refresh. Fix the source that is stale, then recheck all three surfaces.

For example, if the page and markup both show $99 but the feed still shows $89, investigate feed freshness first. If the page shows $99 and its markup still shows $89, investigate markup generation or caching. If all values match now, compare the diagnostic crawl timestamp before making another change.

Run a free three-product scan to check public product-page evidence, or see a sample assessment. The free scan does not inspect a private Merchant Center account or an unpublished feed; use the checklist above to compare those sources yourself.

Check one actual product, end to end

Choose a product with a stable URL, active offer, and variants if relevant. Record the time, country, currency, product ID, URL, and selected variant. Then compare four views:

Surface What to record
Product page Visible title, selected variant, price, currency, stock, shipping promise
Page markup Product/Offer name, SKU or GTIN where applicable, price, priceCurrency, availability, URL
Product feed Item ID, link, price, currency, availability, variant identifiers
Merchant Center diagnostics Item status, mismatch reason, last crawl or update timestamp

Use the Google Rich Results Test for the page markup and compare its extracted values with what a customer sees. Then inspect the corresponding feed item rather than assuming the feed generator is correct. A logged-in price, geolocation rule, currency switcher, or cache layer can change what different visitors receive. Test the same destination country and variant that the feed item represents.

Common causes worth checking

Stale feed schedule. The store changed inventory or price after its last feed export. Shorten the update interval or push item updates when the source of truth changes.

Variant mismatch. The feed represents a blue size M jacket, while the URL or default page selection displays black size L. Make variant links and attributes explicit, and verify the selected offer on landing.

Cached markup. The visible page updated, but JSON-LD was rendered from an older cache entry. Ensure price and stock on the page and markup use the same current source and cache invalidation.

Currency or market mismatch. The feed targets one country and currency, but the landing page redirects or defaults to another. Check market-specific URLs, shipping settings, and user-agent behavior.

Sale-price confusion. A struck-through price, member-only price, coupon, or time-limited sale can be represented differently across feed and page. Compare the price a buyer can actually obtain under the feed's conditions.

Repair paths by commerce platform

Adobe Commerce / Magento. Compare the configurable product with the exact child simple SKU exported to the feed. Confirm that the child SKU, swatch selection, store-view price, salable quantity and Offer markup all describe the same purchasable variant. Use the Adobe Commerce product-data audit checklist to trace attribute sets, configurable links and store-view overrides.

Shopify. Check the exported variant ID, market, currency and availability against the variant selected by the landing-page URL. A product-level link that opens a different default variant can create a mismatch even when the feed row is correct. Review the Shopify product-data readiness guide for a platform-specific sequence.

WooCommerce. Verify that variable-product attributes and the selected variation resolve to the same SKU, price and stock status in the feed and page markup. The WooCommerce product-data readiness guide covers the corresponding catalog checks.

Fix the underlying catalog and synchronization path first. Google's automatic item updates may correct some differences, but they are a fallback, not a substitute for accurate source data; Google says a product may still be disapproved instead of updated.

Verify the repair

Rebuild or resync the affected item, clear relevant caches, and check the page, markup, and feed again at the same timestamp and market. Review Merchant Center diagnostics after its next processing cycle. Repeat with one product from each major category and with variant families, sale items, and out-of-stock items. Keep a dated before-and-after list of exact URLs and item IDs so a future recheck compares the same products.

If you want a fast starting point, use Lumen's free product-data scorecard, then run its free three-product public scan. The scorecard is a self-reported worksheet, and the scan is a limited public sample. Lumen's optional $299 one-store assessment goes deeper with a representative sample of up to 100 products and a documented improvement plan. Neither guarantees a particular ranking, AI appearance, traffic level, or sale.

References