Browsing by Author "Grobler, Abraham"

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    Enhancing customer engagement in e-commerce: improving e-marketing open rates through model-free reinforcement learning.
    (Stellenbosch : Stellenbosch University, 2024-03) Grobler, Abraham; Engelbrecht, Herman; Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
    ENGLISH ABSTRACT: This thesis aims to optimise the delivery time of e-marketing methods such as emails and push notifications, with the intention of increasing customer engagement with an e-commerce platform. This optimisation can be performed using model-free reinforcement learning (RL) methods. First, we aim to develop a statistical, non-stationary model of a customer’s probability to interact with e-marketing at different hours of the day. The model is built using a small sample of anonymous, real customer data. From this sample, we train a Gaussian Mixture Model, which allows us to generate a large synthetic customer base. This customer model acts as the environment of the RL experiments. We then develop several different RL agents, employing algorithms such as Q-learning and DQN, to try and find the best time to deliver e-marketing messages to each customer. We then compare the different agents in terms of learning rate, adaptability and stability. A novel method for epsilon-greedy exploration, tailored to each customer through a parameter-specific approach, is also proposed and tested. Our experiments demonstrate that this method outperforms traditional exploration techniques in the context of our experiments. Our findings demonstrate that RL-based optimisation of delivery time provides a promising method of potentially increasing the open rate and customer engagement, providing valuable insights for e-commerce platforms.