Optimizing Adaptive Marketing Experiments with the Multi-Armed Bandit

Sequential decision making is central to a range of marketing problems. Both firms and consumers aim to maximize their objectives over time, yet they remain uncertain about the best course of action. So they allocate resources to both explore to reduce uncertainty (learning) and exploit their current information for immediate reward (earning). This explore/exploit tradeoff is best captured by the multi-armed bandit, the conceptual and methodological backbone of this dissertation. We focus on this class of marketing problems and aim to make the following substantive and methodological contributions. Our substantive contribution is that we solve an important and practical marketing problem with challenges that exceed those handled by existing multi-armed bandit methods: sequentially allocating resources for online advertising to acquire customers. Online advertisers serve millions of ad impressions to learn which ads work best on which websites. However, recognizing that ad effectiveness differs by website in unobserved ways creates a methodological challenge. Our methodological contribution is that we propose a novel bandit policy that simultaneously handles attributes of ads and how their importance differs across websites (heterogeneity) to generate recommended allocations of ad impressions. We not only test this in simulation, but we also run a live field experiment with a large retail bank to improve customer acquisition rates, lowering the firm's cost pe