Data Quality Issues
Google's algorithms have detected patterns in your feed that suggest inaccurate, misleading, or low-quality data. This is a catch-all warning covering many specific issues: pricing that doesn't match market rates (suspected spam), titles with excessive keyword stuffing, descriptions copied across products, attribute values that contradict each other (e.g., color = 'Blue' but image is red), or unrealistic inventory claims. Data quality issues erode shopper trust and Google's trust in your feed.
Data quality issue / Data quality [various specific issues]Impact: Data quality warnings are Google's way of flagging patterns that reduce product performance — misleading attributes, inaccurate pricing relative to market, unusual values that seem fake, or inconsistent data. Individual warnings don't disapprove products, but accumulated data quality issues trigger account-level reviews. Repeated data quality violations lead to account suspension.
Root Causes
- 1Pricing at suspicious levels — products priced 80%+ below market average are flagged as potentially counterfeit or misleading.
- 2Duplicate or boilerplate descriptions across many products — indicates low effort and makes Google question data integrity.
- 3Keyword stuffing in titles — excessive repetition ('Running Shoes Men Sport Running Sport Shoes Men') flags as spam-like.
- 4Attribute-image mismatches — your 'color' attribute says one thing, but the product image shows something different.
- 5Unusual attribute values — quantities like 9999 for inventory (used as 'unlimited'), 'N/A' placeholder text in required fields, or obvious test data ('Test Product', 'Sample SKU').
Fix by Platform
- 1Review GMC Diagnostics for specific data quality warnings. Each one lists the exact issue.
- 2For pricing issues: ensure your feed prices reflect actual sale prices. If you're intentionally priced very low (genuine liquidation or overstock), provide context via the description and avoid sensationalized pricing language.
- 3For duplicate descriptions: use Shopify's metafields or product descriptions to write unique content per product. Avoid template-generated descriptions that vary only by product name.
- 4For keyword stuffing in titles: rewrite titles following the formula [Brand] [Type] [Feature] [Variant] [Color] [Size] without repeating keywords.
- 5For attribute-image mismatches: audit your product images to confirm they show the specific variant (correct color, correct angle). Use Shopify's variant-level images to ensure each variant's image matches its attributes.
When This Doesn't Apply
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Frequently Asked Questions
What specific things does Google consider 'data quality issues'?+
Common patterns: (1) Pricing anomalies — significantly below or above market, (2) Duplicate descriptions — same text across multiple products, (3) Title keyword stuffing — repetitive keywords packed into titles, (4) Attribute-image mismatches — color/size/material doesn't match what's shown, (5) Placeholder text in required fields — 'N/A', 'Unknown', 'TBD', (6) Unrealistic inventory — 9999 units as 'always in stock', (7) Inconsistent data — product_type says one thing, google_product_category says another, (8) Fake urgency — 'Only 1 left!' always.
How many data quality warnings trigger an account review?+
Google doesn't publish exact thresholds, but patterns matter more than counts. A few warnings on specific products are normal; hundreds of warnings across the catalog triggers review. More critically: repeated warnings of the SAME issue after being flagged (indicating you didn't fix it) escalates the review priority. Always address specific warnings promptly rather than letting them accumulate.
My prices really are that low — how do I avoid pricing flags?+
Legitimately low prices (clearance, overstock, close-out) aren't flagged incorrectly if: (a) you provide context in the description ('End of season clearance', 'Overstock sale', 'Limited quantity, final price'), (b) the product availability is consistent with genuine clearance (limited inventory, not endless stock), (c) your account history shows normal pricing patterns with occasional deep discounts — not perpetual 80% off everything. Google's flags are for suspicious patterns, not genuine discounts.
How do I check my data quality proactively?+
Use GMC's Diagnostics regularly — it shows specific issues. Monitor the 'Data quality' tab in Products → Diagnostics. For more comprehensive checks, use a feed audit tool that compares your feed against best practices: title length/keyword density, description uniqueness (run through a duplicate detection tool), pricing against category averages, image-attribute alignment. GMCCheck runs these checks automatically and alerts you before Google does.