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Modeled scenarioHome & Furniture

An 8,500-SKU home retailer improves weak discovery

Weak search responses made best-selling pieces harder to find. Continuous re-ranking improved discovery and lifted collection conversion.

Catalog
8,500 SKUs
Platform
BigCommerce
Starting point
Weak search and click-through signals
$128,900

modeled revenue recovered per quarter

MetricBeforeAfter
Collection click-through rate3.1%4.1%
Zero-result search rate11%4%
Products rewritten citation-ready06,200
Revenue per 1,000 sessions$420$511
The challenge

Large catalogs and static, hand-curated collections produced weak search and click-through signals for high-margin best-sellers. Merchandising reviews happened weekly, far slower than demand actually shifted.

What Regor did

Search

Regor repaired zero-result and low-CTR searches across a wide long tail of room, material, and style queries, connecting shoppers to inventory they could not previously find.

Ranking

It continuously rebalanced collections using trusted search and click-through signals, promoting products with rising engagement and demoting stale SKUs without waiting for the weekly manual pass. High-impact ranking changes were held for review.

Product data

More than 6,000 products were rewritten into structured, citation-ready copy with materials, dimensions, and use-case attributes filled in, improving both on-site search and visibility with AI assistants.

The outcome

The modeled result was a 31% lift in collection click-through and an estimated $128,900 in recovered revenue per quarter, with every change logged and supported mutations carrying rollback information.

"Our best products were always there. Shoppers just struggled to find them. Having something rebalance the catalog every hour instead of every week is the difference."

Illustrative persona: Merchandising Director, home goods retailer

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 one day of an Adobe Analytics feed from a major US retailer, representing 15,968 unique visitors, showed that 73.5% of purchasers searched before buying. Actual results vary by catalog, traffic, and configuration.

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