Product Feed Optimisation
From raw ERP to rich listing, automatically.
Data enrichment, field mapping, validation, translation, and output to every channel your products sell on. One source of truth in your ERP — richly-populated listings on every marketplace.
Feed pipeline · SKU TK-047
Processing
ERP
Raw data
Enrich
Rules applied
Validate
Channel-ready
Field
Raw
Enriched
title
socks
brand
—
gtin
—
category
apparel
material
—
images
1
attributes
0
Feed quality score
18/100
Five dimensions of feed quality
Every field, assessed on all five.
Completeness
Are all required fields populated? Are recommended fields populated for bonus visibility?
Accuracy
Does the data match reality? GTIN matches product, weight matches shipped unit, material matches composition label.
Richness
Beyond required — enough detail to win the listing? 6+ images vs 1, 23 attributes vs 3, 2000-char descriptions vs 200.
Freshness
Price, stock, availability updated within hours not days. Stale feeds lose trust with channels and customers.
Consistency
Same product, same data across all channels. No conflicting brand names, inconsistent categories, divergent prices.
The enrichment layer, explained
What a feed rule actually looks like.
Not "AI" — concrete transformation rules we design per client. Deterministic, traceable, version-controlled. If a rule misfires, we can always find why.
Title construction
Input
brand, product type, key attribute, size, variant
Output
Trailkit Merino Runner Sock · Cushioned · Size M
Amazon allows 200 chars, Google Shopping 150. Per-channel title templates applied.
Category mapping
Input
internal category 'SOCKS_RUN'
Output
Google: 'Apparel & Accessories > Clothing > Socks' · Amazon: 'Clothing > Men > Socks > Running'
Channel-specific taxonomies, mapped from single internal category.
Image rules
Input
array of raw media URLs
Output
Hero with white BG first, 5 lifestyle/detail shots after, min 1000px, format WebP/JPEG
Auto-rejects non-compliant images, flags missing hero, orders for optimal display.
Availability sync
Input
ERP stock level (every 15 min)
Output
In stock >5 units · Low stock 1-5 · Out of stock 0 · Hidden from feed if <0
Thresholds client-defined, preventing oversell while maintaining listing visibility.
One feed engine, many outputs
Channels we transform feeds for.
Amazon (all EU marketplaces)
Flat files · Excel · S3 FTP
Per-marketplace translation, category-specific templates, GTIN validation
Google Merchant Center
XML · Content API
Product-level attributes, shipping rules per country, daily refresh
Meta Commerce (Facebook + Instagram)
CSV · XML · API
Dynamic product ads dataset, Collection Ads-compatible
TikTok Shop + eBay + Etsy
Varies per platform
Platform-specific item specifics, shipping policies, return rules
Google Manufacturer Center
XML · Content API
Authoritative brand data, feeds into Knowledge Graph
Your own Shopify/WooCommerce
REST API · Webhooks
Store catalogue kept in sync, no double data entry
Why feed quality is a ranking factor
Rich feeds outrank competitors with poor feeds.
On Amazon, Google Shopping, Meta — all of them — the platform uses feed completeness and accuracy as a ranking signal. Missing attributes demote you even when you have the better product.
2–4×
Visibility lift typically seen when moving from 50% to 90% attribute completion
~35%
Of Amazon suppression cases caused by missing or mismatched data attributes
20+ hrs
Per product per month saved for in-house teams vs. manual listing management
Frequently asked
Feed optimisation questions.
No. We build the enrichment layer around whatever you have — SAP, NetSuite, Odoo, a spreadsheet, or a Shopify admin. The point is to leave the source of truth untouched and do transformations downstream.
Share your feed — get a quality score.
Send us a current feed (any format) and we'll come back with a per-field completeness score, ranking gaps across channels, and a 90-day improvement roadmap. Free diagnostic.