Product Data Enrichment
Incomplete product data doesn't just hurt your Shopping campaigns — it stops customers from finding your products, understanding what they're buying, and converting. We fix that at catalogue scale, automatically.
Strategically — Live
Attribute gap analysis
Estimated impact of full enrichment:
+28% impression share
The problem
When a customer searches for "air filter for a 2019 Toyota Tacoma", they need a product page that tells them clearly — this fits your vehicle. If that fitment data isn't there, the search engine can't surface it. The customer can't verify it. The sale doesn't happen.
This plays out across every category. A fashion customer needs sleeve length, neckline, and material to make a purchase decision. A shopper researching garden tools needs technical dimensions. The data that closes the sale is the same data that helps search engines rank your products.
Incomplete product data is simultaneously a CRO problem, an SEO problem, and a paid search problem — with one fix.
Customers can't make purchase decisions
Without the right attributes — fitment, dimensions, materials, specifications — customers can't confirm a product is right for them. They leave and buy from someone who made it clear.
Search engines can't understand your products
AI crawlers and search engines need structured, complete data to understand what a product is and who it's for. Generic titles and missing schema mean your products get categorised incorrectly or not at all.
Site search returns irrelevant results
Shopify's search engine can only surface what's in the metafields. Without structured attributes, customers searching your store for specific specs — colour, size, fitment — get back poor results.
Manual enrichment breaks above 500 products
Getting this right manually for 50 products is hard. At 500, it's impractical. Above that, it's essentially impossible to keep pace with catalogue changes, new arrivals, and seasonal updates.
How it works
01
We look at your products, your imagery, and your product categories. For each category — a sweater, an automotive part, a garden tool — we define the exact set of attributes required: material, fitment, dimensions, style details, technical specs. The data structure is built before any enrichment starts.
02
Strategically analyses each product against the defined attribute set — reading existing data, imagery, and design details. We identify what's present, what's missing, and what's incorrect. You get a clear picture of the gap before we fill it.
03
Missing attributes are sourced and written. For fashion: sleeve length, neckline, material, trend language. For automotive: year/make/model fitment, vehicle placement, technical specs. For any category: the data a customer needs to buy and a crawler needs to rank.
04
Enriched data is written back into Shopify metafields — making it available for site search, product recommendations, and storefront display. It's also structured as schema.org markup ready for your front-end team to present to AI crawlers and search engines.
What's included
Every category has a different set of required attributes. We define the right attribute set for each product type in your catalogue, then build and fill it. The enriched data serves three purposes simultaneously: customer buying decisions, search engine understanding, and paid campaign eligibility.
Book a free auditMaterial, brand, MPN, EAN/GTIN, colour, size, weight, dimensions — the baseline attributes every product needs regardless of category.
Year, make, model, trim, engine — the data that tells a customer whether a part fits their specific vehicle, and tells a search engine what vehicle queries to surface it for.
Sleeve length, neckline, fit type, fabric composition, care instructions, trend and style language — written to match how customers actually search for garments.
For tools, appliances, electronics, sports equipment — technical specifications written at the attribute level, not buried in a description.
Enriched data written directly into Shopify metafields — enabling better site search, product filtering, personalised recommendations, and storefront display.
Product data structured as schema.org markup, ready to be published on product pages so AI crawlers and search engines can correctly classify and surface your products.
Written in the language your customers use — not manufacturer spec copy. For fashion, that means trend and style language. For automotive, it means fitment and compatibility language.
The full set of attributes Google Shopping and Performance Max require for auction eligibility — product type hierarchy, custom labels, and category-specific fields.
How it compares
| Option | Cost | What you actually get | Scales with catalogue | Schema + metafields |
|---|---|---|---|---|
| In-house team (manual) | Hours per product | Works at 50 products. Breaks at 500. Impossible at 5,000. | ✗ | ✗ |
| Agency enrichment | $1,000–$5,000/mo | Human-delivered, slow turnaround, generic output — not category-specific | ✗ | ✗ |
| Generic AI writing tools | $50–$200/mo | Bulk descriptions with no structure, no metafields, no schema output | ✗ | ✗ |
| Strategically (pay-as-you-go credits) | $0.20/attribute | Category-specific attributes, metafield output, schema-ready — on demand | ✓ | ✓ |
| Strategically MC Optimization | From $499/mo | Ongoing enrichment + feed management + reporting — credits included monthly | ✓ | ✓ |
Results
Fashion retailer
~4,500 SKUs
Full attribute enrichment — size, colour, material, GTIN resolution, product type correction.
Shopping impression share
+31%
Attribute completeness
94%
Achieved in 45 days
Home & garden retailer
~8,000 SKUs
GTIN resolution and structured attribute enrichment across full catalogue before campaign rebuild.
ROAS
1.8x → 4.2x
Products fully attributed
96%
Achieved in 30 days
Previous
← Feed managementWe'll audit your product data and show you exactly which attributes are incomplete, what that's costing you in search visibility, and what a full enrichment would look like for your catalogue.
Book a free audit →No commitment. Written report within five working days.