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Modeled scenarioHome & Furniture

An 8,500-SKU home retailer resurfaces buried best-sellers

Best-selling pieces were stranded on page three of collections. Continuous re-ranking pulled them forward and lifted collection conversion.

Catalog
8,500 SKUs
Platform
BigCommerce
Starting point
Best-sellers buried below the fold
$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 meant high-margin best-sellers sat on page three while slow movers held prime positions. 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 by what was converting and trending, promoting accelerating products 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 reversible.

"Our best products were always there. They were just buried. 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 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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