One catalogue, many outputs
Attributes developed once against a written model and mapped out to each channel's requirements, instead of maintained separately per marketplace until they disagree.
Product Data & Catalog Management
Catalogue data that holds up everywhere you sell it
How the catalogue is run
Every channel wants the same product described differently. Amazon wants its category's valid values, Wayfair wants its taxonomy, the trade customer wants your spec sheet, and your own site wants something readable. Most catalogues end up maintained separately for each, which is how the same product ends up with three weights and two colours.
StaffCloud treats the catalogue as one asset and the channels as outputs of it. Attributes are developed once against a written model, matched and deduplicated against a written rule, and syndicated out — rather than re-typed per channel by whoever is closest to the deadline.
The pod works inside your systems: your PIM if you have one, your spreadsheets and channel back-ends if you do not. The five capabilities below are the shape that work takes. Most clients start with a pilot batch, because catalogue work is easy to describe and hard to scope until someone has actually looked at the data.
Capabilities
Rules written before staffing
Matching thresholds, normalisation conventions and what counts as a duplicate are agreed and documented up front. That is what makes the output consistent across a team and reviewable afterwards.
Sampled QA on every batch
Work is checked against the rules on a sampled basis, with the defect types fed back rather than silently corrected — because a recurring defect is a rule problem, not an operator problem.
Handover as a deliverable
The mapping, the conventions and the exception list are yours at the end. A catalogue project that leaves no documentation has to be repeated.
How the engagement runs
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01
Data audit
A look at what you actually have: coverage per attribute, duplicate rate, where the channel data and the source data disagree. You get the audit whether or not you engage us.
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02
Model and rules
The attribute model, the normalisation conventions and the matching rules agreed in writing before any volume work starts.
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03
Pilot batch
A defined slice run end to end so both sides can see the output quality and the real throughput before the scope is set.
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04
Run and govern
Ongoing enrichment, matching and syndication on an agreed cadence, with the rules revised as the range and the channels change.
What clients are actually buying
Nobody enjoys procuring catalogue work. They come to it because the range has outgrown the spreadsheet and the errors have started reaching customers.
- Product data that is the same product everywhere it appears
- A written model, so the next channel is a mapping rather than a rebuild
- Capacity that does not disappear when one person leaves
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.
- AkeneoPIM modelling and enrichment
- SalsifyCatalogue syndication
- inRiverProduct information management
- Excel / Google SheetsMapping, QA and exception tracking
- Channel flat filesMarketplace templates and bulk upload
- ShopifyStorefront catalogue
- Seller CentralAmazon catalogue
- SQLMatching, deduplication and reconciliation
- Your ERPSource-of-truth extracts
- Your DAMImage and asset references
What sits under Product Data & Catalog Management
5 capabilities, each staffed and documented separately. Pick the one closest to the work you need covered.
Product Data Enrichment & Attribute Development
Product data enrichment service with attribute development, taxonomy mapping, spec normalization and image sourcing at scale.
Explore this serviceProduct Data Matching & IDQ Auditing
Product data matching service with catalog deduplication audit, product identity review and IDQ auditing to keep your item data clean.
Explore this serviceMAP Monitoring & Price Compliance
MAP monitoring service with daily violation tracking, seller identification, evidence capture and enforcement outreach across channels.
Explore this serviceBundle & Kit Creation Services
Product bundle creation service and kit building: component mapping, new SKU setup, pricing logic and channel-ready listings per bundle.
Explore this servicePIM & Catalog Syndication Management
PIM managed services covering data modeling, catalog syndication, channel templates and ongoing catalog governance for growing ranges.
Explore this serviceQuestions buyers ask
The things worth knowing before the first call.
Not sure this is the right scope?
Describe the workload in a sentence and we will point you at the right team — or tell you honestly if it is not something we should take on.
Ask usNo. Plenty of brands run substantial catalogues on spreadsheets and channel back-ends, and StaffCloud works the same way in either setup. Our team builds the attribute model, the valid values and the rules first, then applies them wherever your data actually lives.
If a PIM is on your roadmap, doing the modelling first makes the implementation much cheaper: the expensive part of a PIM project is deciding what the data should look like, not installing the software.
From a pilot batch rather than an estimate. StaffCloud runs a defined slice of your catalogue end to end — the same steps the full project would use — and measures how long it took and how many records needed correction.
That gives both sides a real throughput figure and a real defect rate before anything larger is agreed. You get a scope and a price based on your actual data, not on how tidy someone assumed it would be.
You do, with our recommendation. Deciding what counts as the same product is a commercial judgement — whether a different pack size is a match depends entirely on what you are using the match for. StaffCloud proposes the rules, you sign them off, and they are written down.
Because the rules are documented rather than held in someone's head, you can review the output against them and challenge anything that looks wrong.
It is flagged as a gap and returned to you, with the source we would need in order to fill it. StaffCloud does not infer a value to complete a record.
An invented attribute is worse than a missing one: it passes your completeness check and then fails the customer who filtered on it. The exception list is part of what you receive, so you can decide which gaps are worth closing.
Yes, and it is usually the better option. Our team works directly in your PIM, your spreadsheets or your channel back-ends, using access you grant and control.
Working in place removes the export-and-reimport step, which is where catalogue data most often goes stale — two versions of the truth, and nobody sure which one the channels are reading.
The same model, and often the same people for brands who take both. The difference is scope: Amazon-specific work such as browse nodes and category valid values sits on the Amazon pages.
This service covers the catalogue underneath — the product data itself — for brands selling across several channels. If Amazon is your only channel, start there; if it is one of several, start here.
Against the agreed rules, on a sample, with the defect types reported back to you. StaffCloud reports what was wrong and how often, not just how much was done.
A sampled error rate you can see is far more useful than a completeness percentage, which counts filled cells regardless of whether the values in them are correct.