Product Data Enrichment & Attribute Development

Attributes developed once, used on every channel

Enrichment is not typing missing cells. It is deciding what the attribute means, where the truth for it lives, and how it is expressed — then applying that same decision across tens of thousands of SKUs without drifting.

Attribute development
Taxonomy mapping
Spec normalisation

Talk to us See what it covers

What this covers

A catalogue arrives incomplete in predictable ways: attributes that exist for the newest range and not the oldest, specs pasted from supplier PDFs in three different units, colours described by the manufacturer's marketing name rather than anything a filter can use.

StaffCloud develops the attribute model first — which attributes matter for your categories, what their valid values are, and which source wins when two disagree — then enriches against it. The model is the deliverable as much as the data, because it is what makes the next range cheap.

Images, where in scope, are sourced and named against the same conventions: a consistent reference so a product's assets can be found by rule rather than by memory.

What the team does

Attribute model design

Which attributes your categories actually need, their valid values and units, and the source hierarchy for when the ERP and the supplier sheet disagree.

Attribute development and backfill

Values developed from your own sources — spec sheets, supplier data, packaging, technical documentation — and applied consistently across the range rather than per batch.

Taxonomy mapping

Your internal category structure mapped to each channel's taxonomy once, so a new channel is a mapping exercise rather than a re-classification of the catalogue.

Spec normalisation

Units, formats and conventions standardised so the same measurement reads the same way everywhere, and so filters and comparisons actually work.

Image sourcing and naming

Assets located, matched to the right SKU and named to a convention, so the image a listing needs can be found by rule.

Sampled QA

Output checked against the model on a sample, with defect types reported back so a recurring error becomes a rule change rather than a correction habit.

How the engagement runs

01
Coverage audit
Attribute coverage measured per category against what the channels require, so the gap is a number rather than an impression.
02
Model and sources
The attribute model agreed, with the source hierarchy written down — this is the step that stops two operators making different reasonable choices.
03
Pilot batch
A defined slice enriched end to end, reviewed together, and the model revised where the data argues with it.
04
Volume run and QA
The range worked through on an agreed cadence, sampled against the model, with exceptions listed rather than guessed.

Why the model matters more than the cells

Anyone can fill a spreadsheet. The value is in filling it the same way twice.

Attributes that survive being sent to a new channel
A source hierarchy, so disagreements resolve the same way every time
Gaps listed as gaps instead of quietly invented

Platforms and tools we work in

The pod works inside your stack. These are the systems this team uses day to day — if yours is not listed, we will learn it.

AkeneoAttribute modelling and enrichment
SalsifyAttribute governance
Excel / Google SheetsWorking files, mapping and QA
Your ERPSource-of-truth extracts
Channel flat filesOutput templates per marketplace

Questions buyers ask

The things worth knowing before the first call.

They are flagged as gaps and returned to you, naming the source we would need to fill them. StaffCloud does not infer a value simply to complete a row.

A wrong attribute passes your completeness check and then fails the shopper who filtered on it — and neither failure is visible in a report. You get a clear list of what is missing and where it would have to come from.

Yes, and it is common. Extracting attributes from supplier spec sheets, manuals and packaging is part of this service, and our team works through them against your agreed attribute model.

Where a document is ambiguous, the value goes on the exception list rather than into your catalogue. That keeps the unclear cases visible instead of burying a guess among thousands of correct values.

Honest scoping. Catalogue work is easy to describe and hard to estimate until someone has looked at the actual data, so StaffCloud runs a defined batch through the full process first.

You get a real throughput rate and a real defect rate before agreeing the full scope, which means the price and the timeline are based on your catalogue rather than on an optimistic assumption about it.

Not unless you ask us to. Taxonomy mapping translates between your structure and each channel's requirements, leaving your own structure exactly as it is.

Restructuring your internal taxonomy is a separate and much larger conversation, with consequences across reporting and merchandising. We will tell you if we think it needs doing, but we will not do it quietly as part of another job.

The attribute model, the valid-value lists and the source hierarchy are documented before anyone is staffed on the work, and our QA samples output against them.

Consistency is a documentation problem before it is a training problem. Because the rules are written down, the tenth person to join the team produces the same output as the first, and you can audit either against the same standard.

Sourcing, matching images to the right products and naming them to a convention, yes — that is part of getting the catalogue record complete.

Image editing and production is a separate service under our Creative line. We will tell you when a job needs that rather than quietly taking it on and stretching this scope.