Enhancing Consumer Store Choice and Cross-Purchase Modelling through Graph Machine Learning
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Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Li, XuliangAbstract
This thesis advances consumer behavior research by integrating computational techniques to model complex retail decision-making. It bridges marketing theory and practice, focusing on basket selection and store choice, while enhancing econometric models with machine learning.
Motivated ...
See moreThis thesis advances consumer behavior research by integrating computational techniques to model complex retail decision-making. It bridges marketing theory and practice, focusing on basket selection and store choice, while enhancing econometric models with machine learning. Motivated by challenges in capturing product interdependencies and spatial influences, traditional models often overlook nuanced patterns. This work employs Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) to address these gaps. Study 1: Basket Selection Uses GNNs for graph embeddings of product relationships, integrated into a conditional logit model to predict basket composition more accurately. Study 2: Store Choice Combines GNNs with LSTM attention to derive location features from purchase sequences, improving predictions of store preferences. These innovations offer methodological advancements and practical tools for retailers to boost engagement and performance. The research enriches marketing science, paving the way for future explorations in data-driven consumer analysis.
See less
See moreThis thesis advances consumer behavior research by integrating computational techniques to model complex retail decision-making. It bridges marketing theory and practice, focusing on basket selection and store choice, while enhancing econometric models with machine learning. Motivated by challenges in capturing product interdependencies and spatial influences, traditional models often overlook nuanced patterns. This work employs Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) to address these gaps. Study 1: Basket Selection Uses GNNs for graph embeddings of product relationships, integrated into a conditional logit model to predict basket composition more accurately. Study 2: Store Choice Combines GNNs with LSTM attention to derive location features from purchase sequences, improving predictions of store preferences. These innovations offer methodological advancements and practical tools for retailers to boost engagement and performance. The research enriches marketing science, paving the way for future explorations in data-driven consumer analysis.
See less
Date
2026Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
The University of Sydney Business School, Discipline of MarketingAwarding institution
The University of SydneyShare