Your catalog was accurate once. Products kept changing anyway.
Formulations change, packaging changes, specs change, and the supplier knows first. Clover keeps product data current by collecting changes and running refreshes from the suppliers who own the data, validated at entry and tracked through every version.
Suppliers managed in Clover include
Where product data breaks down
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Item data is a snapshot from setup day.
The item was set up right. Then the supplier changed the pack size, reformulated, or updated the spec, and told no one. Your systems keep serving the old answer to every team and customer that asks.
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Updates arrive as attachments, if they arrive at all.
A new spec sheet lands in one buyer's inbox. Maybe it gets forwarded. Maybe someone re-keys part of it. There's no process that moves a supplier's product change into your systems reliably, so accuracy depends on luck and heroics.
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Wrong data costs real money downstream.
Stale specs turn into mispicks, mislabeled products, wrong shelf data, chargebacks, and compliance exposure. By the time bad data is discovered, it has already been copied everywhere.
Current data, from the source, on a system.
Clover treats product data as living information with an owner: the supplier. The people who know about a change first are the ones who submit it, so accuracy scales with your catalog, not your headcount.
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Changes flow in as structured updates
When something changes, the supplier submits it through a guided task: the field, the new value, the supporting document. Validation checks it at entry, and your team only reviews what needs a decision.
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Refresh campaigns confirm what didn't change
On your cadence, or ahead of a season or a reset, send a refresh across any slice of your catalog. Suppliers confirm or correct their items, and you get a current-as-of date on every record.
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Every attribute has a history
Who changed the spec, when, from what, and why: each product's data carries its versions. When a discrepancy surfaces downstream, the answer is a lookup, not an investigation.
From stale catalog to living catalog
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Define what current means
The attributes, documents, and formats each product category requires, and the refresh cadence each deserves.
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Suppliers submit changes and confirmations
Change tasks and refresh campaigns collect updates from the source, validated at entry, with reminders driving completion.
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Clean data flows downstream
Approved changes sync to your ERP, PIM, and eCommerce systems, each with a version history that makes every value explainable.
Every catalog drifts. The question is whether anything corrects it.
Frequently asked questions
It's keeping product data current after setup: specs, descriptions, attributes, and supporting documents, collected and maintained by the suppliers who own them. Changes arrive as validated, structured updates, refresh campaigns confirm whole categories on a cadence, and every attribute keeps its version history.
New item setup gets an item into your catalog complete and validated. Product data management keeps it that way as the product changes over time. Same engine, same supplier experience: setup is the intake, management is the upkeep.
No. Clover is the supplier-facing collection and validation layer: it gets accurate, current data from suppliers and syncs it to your PIM, ERP, and eCommerce systems. Your PIM stays the internal home for product content; Clover keeps what flows into it accurate.
Alongside it. Your largest suppliers keep publishing through GDSN data pools. Clover covers what GDSN doesn't: the long-tail suppliers who never join, the documents and attributes standards don't carry, and the validation and history around every change.
Yes. A refresh campaign targets any slice of your catalog, every supplier gets a task to confirm or correct their items, reminders chase the stragglers, and you end with a current-as-of date on every record instead of hoping the data held.
See your catalog stay current
We'll run a live product data refresh on items like yours.
- We start with your problems, then show you how Clover would solve them. A real walkthrough with someone who knows product data.
- Typical deployments cost less than a single data coordinator.
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