# Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → metrics for merchandising and catalog
A shopper searches "black midi dress" on your site. Forty results load. Twelve have no fabric composition listed, five show only a flat-lay image with no on-model shot, three have a size chart that maps "M" to a 32-inch waist (wrong for this brand), and two are the same dress listed twice under different SKUs. That shopper bounces. You never see why in your revenue dashboard. This is catalog data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète →, and it quietly kills both conversion and allocation.
This lesson shows you how to measure it.
Merchandisers often treat missing product data as a "someone forgot to fill it in" issue. It is actually a measurable, trackable data-quality problem with direct downstream effects.
Two systems consume your catalog data:
Bad catalog data breaks discovery and it breaks the math that decides where inventory goes.
We track four metrics. Each is a simple ratio you can compute today from a product export.
The share of required attributes actually populated across your catalog.
First, define a required attribute set per category. For dresses that might be: color, fabric composition, sleeve length, neckline, dress length, care instructions, country of origin. For handbags it differs: material, dimensions, strap type, closure.
Then:
Attribute completeness = filled attribute cells / (products x required attributes per product)Worked example. You have 500 dresses, each needing 7 attributes. That is 3,500 required cells. Your export shows 2,940 are populated.
2,940 / 3,500 = 0.84 = 84% completenessBenchmark note: retailers commonly target 95 percent or higher for search-critical attributes. Anything below 90 percent usually means visible gaps in filtered navigation. (Treat "95 percent" as a widely cited operational target, not an official standard.)
Do not average all attributes equally. Weight the ones customers filter on. A missing "care instruction" hurts less than a missing "color" that removes the item from every color filter.
Images drive fashion conversion more than any single text field. Measure two things.
Minimum image count compliance: the share of SKUs meeting your image standard. If your standard is "at least one on-model shot plus three detail shots," a product with only a flat-lay fails.
Image compliance = SKUs meeting image standard / total SKUsOn-model coverage specifically, because apparel converts far better with a body shot than a flat product-only image.
Example: 500 dresses, 430 have an on-model image.
430 / 500 = 86% on-model coverageThe 14 percent gap is often new-season arrivals or marketplace-sourced items where you inherited only the vendor's flat image.
The most under-measured and the most expensive. Wrong size charts drive returns, and apparel return rates are high: for online apparel, returns commonly run in the 20 to 30 percent range, with a large share driven by fit (widely cited industry estimates, as of 2025). Fit-driven returns are pure margin leakage plus reverse-logistics cost.
Size-chart accuracy is harder to compute than the others because "correct" is not a filled-or-empty cell. Practical measures:
# Flag non-monotonic size charts (a data-entry error signal)
def flag_bad_charts(chart):
chests = [chart[s] for s in ["XS","S","M","L","XL"]]
return chests != sorted(chests) # True = flagged as brokenRun this across every chart weekly. It catches the "M and L swapped" errors that generate return spikes.
Duplicates arise when the same physical product enters your system twice: once from a vendor feed, once from manual entry, often with slightly different titles.
Duplicate rate = duplicate SKU records / total SKU recordsDetecting duplicates needs matching logic, not just exact-match on title. Match on combinations: brand + style number + color + size, or use GTIN (Global Trade Item Number, the barcode standard) where available. If two records share a GTIN, they are the same product.
Example: 12,000 SKU records, matching logic finds 360 that are duplicates of an existing record.
360 / 12,000 = 3% duplicate rateEven 3 percent matters: those 360 split inventory and demand across two records, corrupting your allocation model.
🎬 [VIDEO: "Product Data QualityData QualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → Fundamentals" - youtube.com - a clear walkthrough of completeness, consistency, and accuracy dimensions applied to retail catalogs]
Your metrics are only as good as your sources. The typical apparel catalog data flowdata flowAn automated sequence of steps that moves data from source to destination: ingestion, transformation, validation, and loading, so it arrives clean and ready to use.Voir la définition complète →:
The governance principle: define required attributes and image standards inside the PIM as validation rules, so bad records cannot be published in the first place. Measuring quality after publication is triage. Blocking at ingestion is prevention.
For a solid vendor-neutral primer on the discipline behind these dimensions, see the DAMA data quality dimensions overview, the industry body for data management.
Combine the four into one catalog-health view, refreshed weekly, sliced by category and by data source.
| Metric | Example value | Target | Status |
|---|---|---|---|
| Attribute completeness (weighted) | 84% | 95% | Below |
| On-model image coverage | 86% | 95% | Below |
| Size-chart linkage | 91% | 99% | Below |
| Duplicate-SKU rate | 3% | Under 1% | Below |
Slicing by source usually reveals that marketplace and dropship feeds carry most defects, while first-party in-studio products score high. That tells you exactly where to invest: vendor onboarding rules, not more internal staff.
Vérification des acquis
1. Why does the lesson argue that catalog quality should be treated as a data problem rather than a content problem?
2. A shopper filters for "long sleeve" dresses but a suitable dress does not appear. Based on the lesson's reasoning, what is the most likely cause?
3. How do duplicate SKUs specifically harm the allocation and planning system?
4. Select ALL correct answers about the attribute completeness rate metric.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers about why bad catalog data quietly hurts a business without showing up clearly in a revenue dashboard.
Sélectionnez toutes les réponses correctes.
To get budget for data-quality work, connect metrics to money using data, not guesses.
Discovery lift. Run a before/after test. Take 200 dresses missing "sleeve length," populate it, and measure their impressionsimpressionsThe total number of times an ad or piece of content is displayed, regardless of clicks. Each display counts as one impression, even to the same person.Voir la définition complète → in filtered search over four weeks versus a control group. Products newly appearing in filters should gain impressionsimpressionsThe total number of times an ad or piece of content is displayed, regardless of clicks. Each display counts as one impression, even to the same person.Voir la définition complète →. This is a clean, measurable causal test.
Return reduction. Track return rate for SKUs before and after size-chart correction. If a corrected chart on a jeans style drops its fit-return rate from 28 percent to 22 percent, that six-point drop is directly attributable and quantifiable per unit.
Allocation accuracy. After de-duplicating, compare forecast error (measured as MAPE, Mean Absolute Percentage Error, the average size of forecast misses) for merged SKUs. Consolidated demand signal typically tightens the forecast.
The point: never argue for data qualitydata qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → on principle. Instrument it. Every one of these metrics has a measurable downstream effect you can test.