How a global home furnishings retailer grew Google Shopping revenue 5.7%, while clicks went down

The shift: more spend was not the answer

The retailer already ran a large, mature Google Shopping and paid search program. The obvious levers — bids, budget, campaign structure — were already well tuned. But like most retailers, its product feed was written in the language of the business rather than the language shoppers use. The question was whether better product data alone, with no change to spend, could still move revenue at this scale.

The approach: a clean, controlled test

Lily AI ran a controlled A/B test in Google Merchant Center. The catalog was split into a matched treatment group and a control group. The control kept the retailer's existing product content. The treatment group received Lily's enrichment: richer, consumer-language product data and structured attributes that Google's ranking and matching systems actually read.

Bids, budget, and campaign structure stayed the same across both groups. The only variable was the product data. The test ran for 28 days.

What the results say

Revenue rose while clicks fell. That is the interesting part: the enriched content was surfacing products to more qualified, higher-intent shoppers, not just more of them — fewer wasted clicks, more revenue per click, better efficiency on the same spend. For a retailer of this size, the lift ran above the average Lily sees for comparably large catalogs.

What this means

At this scale, a mid-single-digit revenue lift from product data alone, with spend held flat, is a meaningful result, and the efficiency gain compounds over time. The campaign was already optimized. The product data was the lever that still had room to move.

The same enrichment layer extends beyond Google Shopping to Meta Ads and to organic and AI-driven discovery. Enrich once, perform across every surface where people search and shop.