Case Study
An electrolyte supplement brand wanted a more strategic way to re-engage existing customers through Amazon DSP. The brand theorized that not all customers have the same likelihood of repurchasing, and sought. to identify and prioritize the customers most likely to buy again to maximize campaigm efficiency.
CHALLENGE:
Retargeting often groups all prior purchasers together, regardless of purchase behavior or future buying propensity. As a result, media spend is frequently allocated to customers with vastly different likelihoods of making another purchase. The brand needed a more precise approach that could improve repeat purchase rates while making customer retention campaigns more efficient.
WBX leveraged its NTB Audience Studio Model to score shoppers based on their likelihood of becoming a repeat purchasers. The model was activated through Amazon DSP and benchmarked against a traditional retargeting audience of past purchasers.
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 to evaluate the effectiveness of predictive customer scoring.
RESULTS:
The model converted at 24.91% — 3.25x higher than retargeting past-purchasers.