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Generating in-store customer journeys from scratch with GPT architectures

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  • Taizo Horikomi

    (The Graduate University for Advanced Studies, SOKENDAI)

  • Takayuki Mizuno

    (The Graduate University for Advanced Studies, SOKENDAI
    National Institute of Informatics)

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. Graphic abstract

Suggested Citation

  • Taizo Horikomi & Takayuki Mizuno, 2024. "Generating in-store customer journeys from scratch with GPT architectures," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(9), pages 1-9, September.
    • Handle: RePEc:spr:eurphb:v:97:y:2024:i:9:d:10.1140_epjb_s10051-024-00778-1
    DOI: 10.1140/epjb/s10051-024-00778-1
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    References listed on IDEAS

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    1. Yutaro Ishimaru & Hiroyuki Morita & Yusuke Goto, 2021. "In-Store Journey Model with Purchasing Behavior Based on In-Store Journey Data and ID-POS Data," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 215-237, June.
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