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What does fixing your product data cost — and when does it pay back?
Everyone agrees incomplete product data is a problem. Nobody can tell you what it's worth to fix, and we won't pretend to: the uplift below is your assumption, clearly marked. What this does is the arithmetic nobody does — how many SKUs, how many hours, what that costs, and how long the payback takes.
Payback period
2.4 months
Fixing 1,125 SKUs costs about $3,938 in effort. At the 5% uplift you assumed, it pays back in 2.4 months.
Pays for itself inside two quarters even on your own conservative framing. The usual blocker here isn't the business case, it's that nobody owns the work.
How we got the number
Exposed = $180,000/mo × 40% discovery × 45% gaps = $32,400/mo
Upside = exposed × 5% (your assumption) = $1,620/mo
Effort = 1,125 SKUs × 6 min = 113 h × $35 = $3,938
Payback = $3,938 ÷ $1,620/mo = 2.4 months
We supply no uplift figure of our own, because none would be honest for your catalog. Halve your assumption and the payback doubles — which is exactly why the payback period is the number to argue about, not the upside.
How this works — and what we're deliberately not claiming
- What is catalog debt?
- The accumulated backlog of missing, inconsistent or prose-trapped product data in a catalog. It behaves like debt because it compounds: every new SKU added to a weak schema widens the gap, and every surface that reads your catalog — site search, marketplace search, AI shopping assistants — reads it through the same missing fields. The interest is paid in recommendations you never appear in.
- What conversion uplift should I expect from enriching product data?
- We deliberately don't supply a figure, and you should be sceptical of anyone who does. The honest answer is that it depends on your category, how bad the gaps are, and how much of your demand is discovery-driven — and no industry average tells you anything about your catalog. That's why the uplift here is your input. Enter a conservative number, then a generous one, and see how far the payback period moves.
- How long does catalog enrichment take per SKU?
- With a defined attribute schema and templated values, a few minutes per SKU is a reasonable planning figure — the work is mostly mapping and filling, not research. Without a schema it's several times that, because every SKU becomes a decision about what fields even matter. Building the schema first is what makes the per-SKU number small.
- Should I enrich my whole catalog?
- Usually not, and the calculator will often tell you so. Cost scales with SKU count while upside concentrates in the SKUs that actually sell, so remediating everything is frequently the worst version of this project. Sort by revenue, fix the top decile, measure, then decide whether to continue.
- How do I find my real attribute-gap percentage?
- Audit it rather than estimate it — a guess here makes every number below it a guess. Our free catalog coverage audit template is a per-SKU scoring grid for exactly this: identifier presence, attribute coverage, schema validity and server-side rendering, scored 1/0 so you can average a column and get a genuine catalog-level figure.
Get the real gap number first.
A guessed gap percentage makes every figure above it a guess. The free catalog coverage audit template scores your catalog SKU by SKU so you can run this on facts.