Case Study

Leading Desk Furniture Brand – New-to-Brand AI Audiences

A desk furniture brand wanted a more effective way to identify and convert prospective new customers through Amazon DSP. The goal was to improve customer acquisition performance while reducing reliance on broad interest-based targeting.

CHALLENGE:

Find Higher-Propensity New Customers

In-market audiences are built around category browsing and purchase intent signals, but they do not always identify the shoppers most likely to become new customers for a specific brand. The brand needed to find users most likely to become new-to-brand purchasers instead of relying only on broad in-market segments.

SOLUTION:

NTB Purchaser Audience Studio Model

WBX leveraged its New-to-Brand Audience Studio Model to score shoppers based on their likelihood of becoming new-to-brand purchasers. The model was activated through Amazon DSP and evaluated against available Amazon in-market audiences.

METHODOLOGY:

WBX Audience Studio leveraged a closed-loop AI system and self-improving models focused on new purchase probability, scoring users based on their likelihood to purchase from this brand. The custom audience was activated via Amazon DSP campaigns and benchmarked against strong-performing Amazon in-market audiences.

RESULTS:

The model converted at 0.55% — 1.39x higher than the best Amazon in-market audience.