The challenge
The problem
APS Industrial were building a new ecommerce platform — but the product data needed to power it wasn't ready. The bulk of their catalogue existed in ETIM format: highly structured for technical classification, but not ecommerce-ready. Descriptions were absent or deeply technical, attributes were inconsistent across suppliers, and the team had no scalable process to transform raw ETIM data into content that would actually perform online.
For industrial distributors launching into ecommerce, product data is the foundation everything else is built on. Without complete attributes, products won't surface in technical search or parametric filtering. Without clear, engaging descriptions, trade buyers won't convert. And with catalogues running to tens of thousands of SKUs across electrical, mechanical, and safety categories, doing this manually simply isn't viable.
The challenge for APS Industrial was threefold: decoding and extracting usable structured data from ETIM classification files, supplementing that with additional supplier source data to fill gaps, and generating the product descriptions and enriched content needed to bring a new ecommerce experience to life — all at a scale and pace that matched the platform launch timeline.
The solution
The approach
Rather than treating ETIM data as a starting point requiring heavy manual work, APS Industrial used SKULaunch's ETIM decoder to unlock the structured data already embedded in their files — then layered AI enrichment on top to fill gaps, standardise attributes, and generate ecommerce-ready content automatically.
The process began with SKULaunch's ETIM decoder, which ingested the existing ETIM classification files and mapped the embedded technical attributes into a clean, structured product record for each SKU. Class codes, feature values, and unit data were automatically extracted and organised — giving the team a far richer starting point than any manual import process could have achieved.
Where ETIM data alone wasn't sufficient, SKULaunch's multi-source enrichment tools took over. Supplier data feeds and product documentation were pulled in and reconciled against the decoded ETIM attributes, with conflicts flagged for review and gaps filled automatically. Web-sourced enrichment was used to supplement records further — drawing on manufacturer sites and technical reference sources to ensure every product record was as complete as possible before content generation began.
With attributes structured and validated, SKULaunch's AI content generation tools produced ecommerce-ready product descriptions at scale — converting technical classification data into clear, searchable, on-brand copy suited to trade buyers. The result was a catalogue that was not just data-complete, but commercially ready for the new platform from day one.
The results
By the time the new ecommerce platform launched, APS Industrial had processed thousands of SKUs through the SKULaunch enrichment pipeline — with product records that were structured, attributed, and content-ready across every category. What would have taken months of manual work was completed in a fraction of the time, without sacrificing quality or consistency.
We had the ETIM data but it wasn't doing anything useful for us commercially. SKULaunch decoded it, filled the gaps from our supplier sources, and generated content we could actually put in front of customers. We launched our ecommerce platform with product data we were genuinely proud of.
Programme Manager
APS Industrial
What’s next
With the ecommerce platform now live on a foundation of enriched, structured product data, the focus turns to maintaining and extending that quality as the catalogue grows. APS Industrial are moving to SKULaunch's supplier portal — bringing new product submissions directly into the same structured, validated workflow that powered the launch, ensuring every new SKU enters the catalogue already enriched and ecommerce-ready from day one.
SKUs with ecommerce-ready descriptions
New capability
Data sources reconciled per product
Fully automated
Avg. time to produce a publish-ready record
↓ ~90%
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