Choose a sample that can reveal different kinds of problems
Reviewing only your best sellers can miss problems in new, seasonal, or rarely sold products. Build a sample that includes high-volume variants, recent additions, products with returns or support questions, items rejected by a channel, and a few randomly selected records. Include different pack sizes and fulfillment locations if those matter to your assortment.
Use the sample to understand patterns, then expand the review where a problem appears. If three new products have missing dimensions, inspect the recent product intake process rather than checking every old dimension immediately. Keep the sample definition consistent enough to compare one month with the next. This turns the review into a repeatable quality check instead of a collection of whichever products someone happened to remember.
Check identity before content quality
Confirm that each sellable variant has the correct internal SKU and the appropriate external identifiers. Look for duplicates, reused codes, mismatched pack quantities, and variants linked to the wrong channel item. A beautifully written description cannot compensate for sending the wrong size because two records were treated as the same product.
GS1's guidance explains that individual products and variations require distinct identification under its GTIN system. Apply the relevant identification standard and marketplace requirements to your assortment rather than assuming every identifier is interchangeable. Keep internal SKUs, GTINs, and channel-specific listing IDs in their own fields. The review should establish what each identifier means and which source is responsible for maintaining it.
Compare facts that affect the buying decision
Review dimensions, weight, materials, compatibility, pack quantity, included components, and care information where they matter to the product. Check that the title, images, variant options, and structured attributes describe the same item. A product page that says one unit while its image shows a set can create confusion even when every required field is technically filled.
Use actual customer questions and return reasons to guide the review. If buyers repeatedly ask whether a stand is included, make that fact easier to find. Avoid solving every issue by adding more copy. Sometimes a corrected image, a clearer option name, or one precise attribute is more useful than a longer description. The goal is accurate, consistent product understanding across the places customers encounter the item.
Verify that availability reflects the intended workflow
Check inventory tracking, location assignments, channel exposure rules, and whether incoming or unavailable stock is being presented as sellable. Review the exact variant rather than relying on a product-level total. A record may look healthy overall while one size or color is repeatedly unavailable.
Compare a few recent stock changes with their source events. Shopify's inventory adjustment history can help explain changes inside Shopify, while other systems may have their own records. Confirm that returns, transfers, and manual corrections are handled according to your process. The monthly review is not a replacement for daily inventory monitoring. It is a chance to detect structural issues that make the daily numbers harder to trust.
Look beyond published to whether the listing is usable
A listing may exist without being available to purchase in the intended way. Check the relevant channel record for publication status, offer availability, variant selection, price, and any unresolved data feedback. The exact conditions differ by marketplace, so use the platform's current requirements rather than assuming one status field tells the whole story.
Review a small number of actual customer-facing pages where appropriate. Confirm that the intended product and variant appear, the images load, and the displayed facts match the catalog. Record the difference between a source-data problem and a channel-specific problem. That distinction tells you whether the correction belongs in the central product record or in a marketplace's separate listing configuration.
Turn findings into a prioritized correction queue
A useful issue record identifies the product, variant, channel if relevant, incorrect field, expected value, source evidence, and owner. Add a priority based on consequence. A wrong pack quantity on a high-volume item deserves a different response from an optional attribute missing on a low-exposure product.
Group related corrections when they share a cause, but verify the intended scope before bulk editing. A rule that is correct for one category can be wrong for another. Keep a record of the change and check the result in the destination that matters. The correction is complete when the relevant record is accurate and usable, not merely when someone saved a form in the source system.
A correction record for a mismatched two-pack might identify the central variant, the channel listing, the incorrect unit description, the supplier specification supporting the correction, and the person responsible. After the edit, check the customer-facing quantity, the warehouse pick instruction, and any mapping that depends on pack size. Fixing only the title can leave the operational error intact. This is why the health check should follow a consequential fact through the relevant systems rather than treating each field as an isolated box to complete. The result is one coherent product record that means the same thing to the buyer and the fulfillment team.
Measure the health of the process, not a vanity score
A single catalog completeness percentage can conceal important differences. A product with every optional field filled may still have the wrong barcode. Track a few meaningful measures instead: unresolved identity issues, time to correct channel feedback, repeated intake errors, and the share of reviewed products with a verified buying-critical discrepancy.
Keep the denominator clear. If this month you deliberately sampled problematic products, the error rate should not be compared directly with last month's random sample. Record whether findings came from a targeted review or a consistent baseline sample. Use the measures to choose improvements, not to reward teams for making issues disappear from a report before the underlying record is fixed.
Improve the next product before it enters the catalog
End the review by choosing one change to the product intake process. That might be a required pack-quantity check, a controlled naming convention, a photo checklist, or a review of the channel mapping before publication. Assign an owner and a date so the lesson becomes a working change rather than another note in a meeting.
Preserve a few anonymized examples of good records and common mistakes. They make training more practical and reduce repeated interpretation debates. Next month, check whether the chosen improvement reduced the relevant issue. A catalog health check earns its place when it does more than repair old data: it makes the next product easier to launch, understand, sell, and fulfill correctly.
Sources & further reading
Official documentation for the concepts discussed. Platform requirements can change; check the linked source for the current details.