Enhancing customer engagement in e-commerce: improving e-marketing open rates through model-free reinforcement learning.

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dc.contributor.advisorEngelbrecht, Hermanen_ZA
dc.contributor.authorGrobler, Abrahamen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.en_ZA
dc.date.accessioned2024-03-04T12:38:37Zen_ZA
dc.date.accessioned2024-04-26T18:20:39Zen_ZA
dc.date.available2024-03-04T12:38:37Zen_ZA
dc.date.available2024-04-26T18:20:39Zen_ZA
dc.date.issued2024-03en_ZA
dc.descriptionThesis (PhD)--Stellenbosch University, 2024.en_ZA
dc.description.abstractENGLISH 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.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Hierdie tesis het ten doel om die aflewertempo van e-bemarkingsmetodes soos e-posse en stootkennisgewings te optimaliseer, met die voorneme om klieëntebetrokkenheid met ’n e-handelsplatform te verhoog. Hierdie optimalisering kan uitgevoer word deur model-vrye versterkingsleermetodes (RL). Eerstens streef ons daarna om ’n statistiese, nie-stasioneêre model van ’n klieënt se waarskynlikheid te ontwikkel om op verskillende tye van die dag met e-bemarking te kommunikeer. Die model is gebou met behulp van ’n klein steekproef van anonieme, werklike klieëntdata. Uit hierdie steekproef lei ons ’n Gaussiese Mengsel Model af, wat ons in staat stel om ’n groot sintetiese klieëntbasis te genereer. Hierdie klieëntmodel dien as die omgewing van die RL-eksperimente. Ons ontwikkel dan verskeie verskillende RL-agente, wat algoritmes soos Q-leer en DQN gebruik, om te probeer en die beste tyd te vind om e-bemarkingsboodskappe aan elke klieënt af te lewer. Ons vergelyk dan die verskillende agente in terme van leertempo, aanpasbaarheid ii https://scholar.sun.ac.za ABSTRACT iii en stabiliteit. ’n Nuwe metode vir epsilon-gulsige verkenning, wat spesifiek vir elke klieënt aangepas is deur ’n parameter-spesifieke benadering, word ook voorgestel en getoets. Ons eksperimente toon dat hierdie metode beter presteer as tradisionele verkenningstegnieke in die konteks van ons eksperimente. Ons bevindings toon dat RL-gebaseerde optimalisering van aflewertempo die oopmaak tempo en klieëntebetrokkenheid doeltreffend verhoog, wat waardevolle insig bied vir e-handelsplatforms.af_ZA
dc.format.extentxi, 91 pages : illustrations.en_ZA
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/130458en_ZA
dc.language.isoen_ZAen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subject.lcshReinforcement learningen_ZA
dc.subject.lcshElectronic commerceen_ZA
dc.subject.lcshCustomer relationsen_ZA
dc.subject.lcshGaussian Mixture Model,en_ZA
dc.subject.lcshMachine learningen_ZA
dc.subject.lcshNeural networks (Computer science)en_ZA
dc.subject.lcshUCTDen_ZA
dc.titleEnhancing customer engagement in e-commerce: improving e-marketing open rates through model-free reinforcement learning.en_ZA
dc.typeThesisen_ZA
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Doctoral Degrees (Electrical and Electronic Engineering)