Product Data Is Sales Data
In e-commerce, your product data is your storefront. A customer who can't find a product because it's miscategorized will never see it. A shopper who bounces because the description is vague or the dimensions are missing has effectively been turned away at the door.
Most e-commerce store owners focus their conversion optimization on ads, landing pages, and checkout flow. Product data quality is the conversion problem that's hiding in plain sight.
The Six Attributes That Drive Conversions
Research consistently shows that customers abandon product pages primarily due to:
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Missing or vague product titles: Titles should include brand, product type, key differentiators, and key specs. "Blue Shirt" loses to "Men's Oxford Button-Down, Slim Fit, 100% Cotton."
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Incomplete specifications: For any product with measurable attributes (dimensions, weight, capacity, compatibility), missing specs drive customers to competitor sites that have them.
Stop bad product data from reaching customers — Sohovi flags quality issues before they ship — try Sohovi free.
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Poor or missing images: The primary image must be clear, on white background or consistent background, and show the product accurately. Missing images are a trust-killer.
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Inaccurate pricing or stock status: A product listed as In Stock that's actually backordered creates returns and refund requests. An "On Sale" badge on a product at full price feels deceptive.
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Missing or thin descriptions: Search engines and customers both need enough text to understand what the product does and why they should buy it. Three sentences isn't enough.
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Miscategorized products: If your navigation and category pages don't surface a product where customers expect to find it, it effectively doesn't exist in your catalog.
The E-Commerce Product Data Audit
Run this audit quarterly or before any major catalog change:
Step 1: Export your full catalog to CSV Most e-commerce platforms (Shopify, WooCommerce, BigCommerce) allow full catalog exports. This is your raw data — every product, every attribute.
Step 2: Check completeness by attribute For each key attribute (title, description, primary image URL, price, stock status, category, key specs), count how many products have it populated. A product catalog where 15% of items are missing weight is a 15% data quality problem.
Sohovi profiles every column in your dataset for completeness and flags the exact rows where values are missing — free to try.
Step 3: Check consistency Are category names spelled consistently? Are size values standardized (S, M, L vs. Small, Medium, Large)? Are brand names capitalized uniformly? Inconsistency breaks filters and search.
Step 4: Spot-check accuracy For 20 random products, verify on-page data against the physical product or your source of truth. Catch the price mismatches, wrong SKUs, and outdated descriptions before a customer does.
Prioritizing Fixes
Fix in this order:
- Products that appear in ads or promotions (highest traffic, highest cost of errors)
- Best-sellers (highest impact per fix)
- New arrivals (set the right standard from the start)
- Long-tail catalog items (lowest ROI per fix, but they add up)
Sohovi gives you the data quality picture you need to make the case for fixing it — and to track improvement over time.
