Morphing for Consumer Dynamics: Bandits Meet HMM
51 Pages Posted: 16 Dec 2019 Last revised: 14 Feb 2022
Date Written: November 8, 2019
Websites are created to help visitors take an action, such as making a purchase or a donation. As visitors browse various webpages, they may take rapid steps towards the action or may bounce away. Websites that can adapt to match such consumer dynamics perform better. However, assessing visitor's changing distance to the action, at each click, and adapting to it in real time is challenging because of the sheer number of design elements that are found in websites, that combine exponentially. We solve this problem by matching latent states to webpage designs, combining recent advances in multi-armed bandit (MAB), website morphing, and hidden Markov models (HMM) literature. We develop a novel dynamic program to explicitly model the trade-off firms face between nudging a visitor to later states along the funnel, and maximizing immediate reward given current estimates of purchase probabilities. We use an HMM to assess visitors' states in real time, and couple it with an MAB model to learn the effectiveness of each design x state combination. We provide a proof-of-concept in two applications. First, we conduct a field study on the MBA website of a major university. Second, we implement our algorithm on a cloud server and test it on an experimental online store.
Keywords: Machine Learning, Electronic Commerce, Website Morphing, Multi-Armed Bandits, Hidden Markov Models (HMM), Retailing
JEL Classification: M31, M30, M37, C61
Suggested Citation: Suggested Citation