Data decay: why catalog quality erodes and what the drift costs
Product catalogs don't stay clean once they're clean. Here's how to measure catalog decay rate and what stale data actually costs in lost sales and returns.
Every catalog you launch clean starts eroding the day it goes live. Suppliers swap a resin for a cheaper alloy without telling you. A marketplace adds a required attribute mid-quarter. A category standard shifts, and last year's "complete" listing is this year's flagged one. Data decay isn't a failure of your original enrichment project — it's the default physical state of a catalog left alone. The question worth measuring isn't whether your data decays, it's how fast, and what that rate is costing you in discovery and returns.
Why catalogs decay even when nobody touches them
Four forces drive decay, and none of them require anyone on your team to make a mistake:
- SKU churn. New variants launch, old ones get discontinued or superseded, and the mapping between "what's live" and "what's documented" drifts apart within weeks.
- Supplier-side spec changes. A manufacturer reformulates, resizes, or re-sources a component and updates their spec sheet — but that change doesn't propagate to your PIM or storefront unless someone is watching for it.
- Standard and taxonomy shifts. Google Merchant Center, major marketplaces, and category-specific compliance bodies periodically add or redefine required attributes. Data that passed validation last quarter can fail it this quarter without a single field being edited.
- New channels raising the bar. Every channel you add — a new marketplace, a retail-media placement, an on-site search upgrade, an AI shopping surface — comes with its own attribute expectations. What works on your own site isn't automatically what works on a marketplace or a retail partner's feed, and industry analysis of PIM trends increasingly points to channel-specific monitoring — catching quality issues before they reach the customer, per-channel — as a requirement, not a nice-to-have. A listing that was "good enough" last quarter can be functionally incomplete the moment it needs to power a size filter, a compatibility check, or a comparison table somewhere else.
None of this is hypothetical, and it gets harder to catch as the catalog gets bigger. A small assortment can be spot-checked by a person who knows it well. A catalog running into the tens of thousands of SKUs across dozens of suppliers can't be — stale attributes hide in plain sight until a return, a support ticket, or a lost sale surfaces them.
Measuring decay rate, not just decay
Most teams that "check on data quality" do it once — a big enrichment push, a scorecard, done. That treats decay like an event instead of a rate. To manage it, you need a trend, not a snapshot.
| Signal | What it shows | How to measure it |
|---|---|---|
| Attribute completeness over time | How fast required fields are going empty or stale as SKUs are added/changed | Run the same completeness scorecard weekly or monthly against your full catalog; chart the trend, not just the current score |
| Last-verified age | How much of the catalog hasn't been re-checked against source docs recently | Tag every attribute with a last-verified timestamp; report the % of catalog older than your freshness threshold (e.g. 90 days) |
| Post-launch edit rate | How often a "live" SKU gets corrected after going public | Pull PIM/CMS audit logs for edits made after initial publish; a rising rate signals your intake process is missing errors upstream |
| Standard-compliance drift | How many SKUs newly fail a channel's schema after a standard update | Re-validate the catalog against Merchant Center/marketplace/schema requirements after every known standard change, not just at onboarding |
| Attribute-level return reasons | Which specific fields (size, material, compatibility) are driving returns | Tag returns by root cause at the attribute level, not just "customer changed mind"; roll up by SKU and category |
The point of tracking these as trends is that decay rate is diagnostic. A catalog with a slowly rising post-launch edit rate has an upstream intake problem. One where completeness holds steady but standard-compliance drift spikes has a monitoring gap, not a data-entry gap. You fix different things depending on which curve is moving.
What the drift actually costs
Decay shows up in two places: people who never find the product, and people who find it, buy it, and send it back.
On discovery, incomplete or outdated attributes quietly drop products out of facets, filters, and comparison surfaces — on-site search, marketplace category pages, organic search, and increasingly AI-driven shopping answers all lean on structured attributes to decide what to surface. A SKU with a missing spec doesn't get excluded on purpose; it just stops qualifying for the query that would have found it. That's measurable as a gap between impressions/traffic to a product's category and the SKU's own share of clicks or add-to-carts within it.
On returns, the connection to bad data is well documented and getting worse, not better. Consumer research cited in Akeneo's "Evolution of the Modern Shopper" survey coverage found that 40% of consumers say they've returned a product because of incorrect information — sizing mistakes and misleading specs chief among the causes — and 53% have abandoned a purchase outright over data they didn't trust. Both are up from prior years, which is itself evidence of decay: the bar for "acceptable" product data keeps rising even as more catalogs go stale under it. Every one of those returns carries a cost beyond the refund — reverse logistics, restocking, and a customer less likely to buy from you again.
The compounding cost is trust. A shopper who gets burned once by a mismatched spec doesn't file a bug report — they just buy the next item from a competitor, or a marketplace listing, where the data held up. That shows up downstream in repeat-purchase rate and AOV, both of which are worth pulling alongside your data-quality trend, not just your conversion rate.
Continuous, not a project
A one-time enrichment sprint treats data quality like a renovation — do it once, admire it, move on. But decay is ongoing, so the fix has to run continuously: monitoring completeness and freshness against source documents on a schedule, catching supplier spec changes and standard updates as they happen, and re-scoring the catalog rather than re-scoring it once a year when someone finally notices the returns are climbing.
This is the exact gap Anglera is built to close. Your PIM stores the data; Anglera continuously scores it, flags what's drifted, and re-enriches from supplier and source documents so the catalog doesn't quietly degrade between projects. It plugs into whatever PIM you already run — or none — without a rip-and-replace migration, because the real fix for data decay was never a bigger cleanup. It's making sure the cleanup never has to happen again.