Product data quality checklist for retailers

1. Pick one category with at least thirty products and real commercial importance. One category is enough to expose every systemic problem, and small enough to finish.
2. Sample twenty products across the range: bestsellers, long tail, and anything recently added by a new supplier. New-supplier products are where data debt enters.
3. Score each check as present, partial or absent. Partial matters — a field that exists on 60% of products is a field your own filters cannot rely on.
4. Look at the page as a machine would. Open a product page and ask whether each fact is stated as data, or only implied by layout, an image, or a dropdown.
5. Record the gap, not the fix. The output of an audit is a ranked list of what is missing. Deciding what to do about it is a separate conversation with different people in the room.

1. Product and variant identifiers

• A GTIN is published wherever the manufacturer assigns oneEAN, UPC or ISBN. This is the only genuinely cross-retailer key, and its absence is the single largest cause of a product failing to be matched to itself elsewhere.
• The manufacturer part number or model number is publishedEssential in categories where GTIN assignment is inconsistent — furniture, industrial supplies, own-brand ranges.
• Your own SKU is present and stableIt is retailer-specific, so it is only meaningful alongside your name, but it must not change when a page template does.
• Every purchasable variant has its own stable, crawlable URLWhere colour, size, capacity or pack quantity change the price or the identifier, that is a different purchasable thing.
• Variant attributes are stated as data, not only as selector labelsA dropdown option is not a machine-readable attribute.
• Retired identifiers are never reusedReassigning an old SKU to a new product corrupts every downstream record built from it, silently and permanently.

2. Category and attribute consistency

• Every product sits in exactly one primary categoryMultiple parents make attribute expectations ambiguous for you and for everyone reading your pages.
• One attribute name per concept across the categoryNot screen_size on half the range and display_size on the rest. This one is usually already hurting your own faceted navigation.
• Enumerated values are controlled“Stainless steel”, “Stainless Steel” and “S/Steel” are three values to a machine and one to a shopper.
• Attribute expectations are documented per categorySomeone should be able to state which attributes a product in this category must carry before it can go live.
• New supplier data is mapped on ingestionNot accepted as-is and normalised later. Later never arrives.

3. Comparable specifications

• The two or three deciding attributes are present on every productThe ones shoppers in this category actually compare. Not forty fields — the right three.
• Every numeric value carries a unitAnd the same unit throughout the category. Mixing centimetres and inches makes numeric comparison meaningless.
• Numbers exist outside the marketing proseKeep the persuasive sentence, but do not let it be the only place a fact appears.
• Ranges and tolerances are structured“40–45 dB” needs a representable form, not a string a machine has to parse.
• No unquantified comparative claims standing alone“Our quietest yet” is fine beside a figure and useless instead of one.
• Specifications match the imagesA capacity in the spec table that contradicts the pack shot creates a conflict someone downstream has to resolve, usually wrongly.

4. Description quality

• A short factual summary existsOne or two sentences: what the product is and who it is for. This is the version most likely to be quoted elsewhere.
• Longer persuasive copy is separate from itBoth are useful. They are not the same field.
• Key selling points are a short listThree to five concrete points, not a paragraph.
• Descriptions are product-specificBoilerplate repeated across a range tells nobody which product to pick.
• Facts in the description agree with the specification tableWhere they disagree, whoever reads your page has to guess — and may guess against you.

5. Image coverage

• Every product has a hero imageRetailer-hosted, HTTPS, and durable.
• Image URLs do not expireSigned or short-lived CDN URLs break every downstream reference within days. This is the most common avoidable image failure.
• Images are genuine image assetsNot placeholders, spinners or tracking pixels.
• Variants have variant-correct imageryShowing the black model on the white variant page is a returns problem, not a design detail.
• A gallery exists where the category needs oneAdditional angles, scale references, and specification or nutrition labels.
• Label imagery is legibleUnusually valuable, because it carries facts in a form a visual system can read directly.

6. Price, currency and checked time

• Price and currency are stated unambiguouslyNear the product name, in a consistent position across the category.
• Promotional and reference prices are structurally distinctSo a discount is never misread as the base price.
• Every purchasable variant shows its own priceA single price on a multi-variant page is ambiguous by construction.
• Price changes reach the page promptlyThe gap between your system and your page is a gap that lands on the shopper.
• Structured markup matches the visible priceA mismatch is worse than no markup — it is the fastest route to losing rich-result eligibility.
• Unit pricing is shown where relevantPrice per 100g or per litre supports comparison across pack sizes, and is a legal requirement in many UK categories.

7. Availability context

• Availability is stated, not impliedIn stock, out of stock, pre-order, discontinued — as words, not as the presence or absence of a button.
• Discontinued products are distinguished from temporarily unavailable onesThey warrant different treatment everywhere downstream.
• Availability is variant-levelA product page marked in stock when only one of six sizes remains is misleading.
• Stock-outs propagate quicklyThis is the change that most directly wastes a shopper's click.
• Discontinued pages resolve sensiblyKept with clear status, or redirected to a successor product — not left to 404 silently.

8. Canonical retailer links

• One canonical URL per purchasable productDeclared with a canonical tag, and self-referential on the canonical page itself.
• URLs survive site releasesEvery reference to your product anywhere depends on this. Referral traffic decays silently when it is not true.
• Redirects are in place where URLs did changePermanent, single-hop, to the equivalent product rather than to the category page.
• Product URLs are free of session and tracking parametersThe canonical form should be clean.
• Filtered and sorted views do not compete with product pagesCanonicalise or exclude them from indexing.

9. Market scope

• Currency and market are unambiguous on the pageEspecially where you operate more than one storefront.
• Regional variants are properly separatedDifferent specification, different pack size, different price — not one page serving several markets.
• Language and region targeting is declaredWhere multiple regional sites exist, use hreflang so each is understood as a regional variant rather than duplication.
• Shipping and availability constraints are statedA product that cannot be shipped to a region should say so on the page.

10. Monitoring and refresh checks

• Someone owns catalogue data qualityNamed, with time allocated. Shared ownership across merchandising and engineering means no ownership.
• Completeness is measured, not assumedPercentage of products carrying a GTIN, a hero image, the deciding attributes. A number you can watch move.
• New products are validated before going liveBlocking on missing critical fields is cheaper than a remediation project a year later.
• Broken image URLs are detected automaticallyThey fail quietly and stay broken for months.
• Structured markup is validated after every releaseTemplate changes break markup routinely.
• Crawlability is checked after every releaseAn accidental disallow or a noindex on a template can remove a whole category from every downstream system.
• The audit is repeated on a scheduleQuarterly, on a different category each time. Data quality is a rate, not a state.

Before and after: one record

Nothing in the “after” column requires a new platform. It requires deciding which facts are data and which are prose, and then being consistent about it.

Frequently asked questions