A 60,000-SKU marketplace tames the long tail
Inconsistent vendor data created tens of thousands of dead-end searches. Autonomous enrichment and search repair recovered the long tail at scale.
modeled revenue recovered per quarter
| Metric | Before | After |
|---|---|---|
| Long-tail zero-result searches | Baseline | down 58% |
| Products enriched to citation-ready | 0 | 41,000 |
| Manual merchandising hours / week | 30+ | under 8 |
| Search-driven revenue | Baseline | +19% |
Thousands of vendors meant thousands of naming conventions. The same product might be listed three ways, and shoppers searching a fourth way found nothing. At 60,000 SKUs, no team could fix this by hand.
Search
Cortex worked the long tail continuously, learning the intent behind sparse and inconsistent queries and mapping them to the right listings across vendors.
Rank
It ordered results by what actually converted rather than by whichever vendor uploaded the cleanest feed, surfacing the best product regardless of who listed it.
Enrich
Roughly 41,000 products were standardized and rewritten into structured, citation-ready copy, normalizing inconsistent vendor data into a coherent, discoverable catalog.
The modeled outcome was a 58% reduction in long-tail dead ends, an estimated $340,000 in recovered revenue per quarter, and a collapse in manual merchandising hours, all governed by autonomy levels the team set per agent.
"At our catalog size, fixing discovery by hand was never going to happen. Autonomy was the only path that scaled with us."
Modeled scenario. The figures above are illustrative projections from Regor's recovery model and published discovery benchmarks, not results from a named customer. Search shoppers convert roughly 2 to 3 times higher than browsers (Forrester), and in our analysis of nearly 178,000 shopper sessions, 73.5% of purchasers searched before buying. Actual results vary by catalog, traffic, and configuration.
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