← Monday, October 5, 2026Models & trainingCase study
A contextual bandit produced a high-single-digit conversion lift for one population
In a seven-week online A/B test, Amazon Payments reports a high-single-digit relative lift in final-funnel conversion for one population and no improvement for another. The result and the finding that content, not the model, was the issue offer practical lessons for applied ML experimentation.
- What
- A multi-objective contextual bandit personalizes a product acquisition funnel by selecting content based on signals about each visit.
- How
- UCB selects content using estimated reward plus an uncertainty bonus, balancing exploitation with continued exploration.
- Why it matters
- Bandits can keep learning as new content variants arrive without waiting for a test to conclude, which suits the growing volume of generative-AI options.
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