This subscription box brand was manually assigning monthly shipments using hand-written rules, which introduced bias and limited personalization at scale.
A leading subscription E-commerce company replaced manual product assignments with Faraday’s algorithmic matching—lifting revenue and retention through smarter personalization.
To improve performance, they tested Faraday’s AI-powered recommendation engine, which uses a two-sided matching approach—scoring both customer traits and product attributes—to predict high-likelihood pairings and deliver the most relevant and value-driving recommendations.
A rigorous A/B test showed Faraday’s algorithm drove a ~5% lift in revenue per customer compared to the rule-based logic.
The algorithm also reduced churn by ~3% and generated an over 5x monthly return on investment.
Faraday’s recommendations outperformed manual rules for four consecutive months—and the brand continues to experiment with new strategies to drive further gains.
