Case Study

Leading Office Desk Furniture Brand – Repeat Purchase AI Audiences

A desk furniture seller wanted a more precise way to bring previous customers back into the purchase cycle without treating all prior users as having an equal probability of buying again.

CHALLENGE:

Improve Repeat Purchase Performance

Re-targeting audiences typically treat all prior purchasers as equally valuable, regardless of purchasing patterns, product ownership, or future buying intent. The brand needed a more intelligent approach that could identify the customers most likely to buy again and improve the effectiveness of repeat-purchase campaigns.

SOLUTION:

Repeat Purchaser Audience Studio Model

WBX leveraged its Repeat Purchaser Audience Studio Model to score previous customers based on their likelihood of making another purchase. The model identified high-propensity repeat purchasers and was benchmarked against a traditional past-purchaser retargeting audience to measure performance.

METHODOLOGY:

WBX Audience Studio leveraged a closed-loop AI system and self-improving models focused on repeat purchase probability, scoring previous customers based on their likelihood to purchase again. The custom audience was activated via Amazon DSP campaigns and benchmarked against a traditional past-purchaser retargeting audience. Conversion rates were measured across both audiences.

RESULTS:

The model converted at 28.30% — 16.73x higher than a traditional retargeting audience.