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arXiv · 2407.11081

Generating In-store Customer Journeys from Scratch with GPT Architectures

Taizo Horikomi·Takayuki Mizuno
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Abstract

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

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Taizo Horikomi, Takayuki Mizuno. 2024-07-13. Generating In-store Customer Journeys from Scratch with GPT Architectures. https://arxiv.org/abs/2407.11081

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