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TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments

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Abstract

Retail companies are greatly interested in performing continuous monitoring of purchase traces of customers, to identify weak customers and take the necessary actions to improve customer satisfaction and ensure their revenues remain unaffected. In this paper, we formulate the customer churn prediction problem as a Predictive Process Monitoring (PPM) problem to be addressed under possible dynamic conditions of evolving retail data environments. To this aim, we propose TSUNAMI as a PPM approach to monitor the customer loyalty in the retail sector. It processes online the sale receipt stream produced by customers of a retail business company and learns a deep neural model to early detect possible purchase customer traces that will outcome in future churners. In addition, the proposed approach integrates a mechanism to detect concept drifts in customer purchase traces and adapts the deep neural model to concept drifts. Finally, to make decisions of customer purchase monitoring explainable to potential stakeholders, we analyse Shapley values of decisions, to explain which characteristics of the customer purchase traces are the most relevant for disentangling churners from non-churners and how these characteristics have possibly changed over time. Experiments with two benchmark retail data sets explore the effectiveness of the proposed approach.

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Data, Material, and/or Code Availability

Code that support the findings of this study and data extracted for training the classification algorithms are available at https://github.com/vinspdb/TSUNAMI.

Notes

  1. Any accuracy metric can be used in this place.

  2. As an additional constraint, let us consider that retails data are commonly recorded fro 18 months, hence serialized values of \(\textbf{T}\) older than 18 months can be also removed from the disk.

  3. https://archive.ics.uci.edu/dataset/502/online+retail+ii

  4. https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce

  5. The source code is available online at https://github.com/vinspdb/TSUNAMI

  6. https://riverml.xyz/dev/api/drift/ADWIN/

  7. https://shap-lrjball.readthedocs.io/en/latest/index.html

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Acknowledgements

The work of Vincenzo Pasquadibisceglie was supported by the project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI, under the NRRP MUR program funded by the NextGenerationEU. The work of Annalisa Appice, Donato Malerba and Giuseppe Ieva was in partial fulfilment of the research objectives of the Research Contract “LUTECH DIGITALE 4.0: Progetto di Tecniche di Machine Learning predittivo per la piattaforma di loyalty Management” within the project “LUTECH DIGITALE 4.0”. We thank reviewers for useful suggestions provided to improve the manuscript.

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Authors and Affiliations

  1. Department of Informatics, Università degli Studi di Bari Aldo Moro, via Orabona, 4, 70125, Bari, Italy

    Vincenzo Pasquadibisceglie, Annalisa Appice & Donato Malerba

  2. Consorzio Interuniversitario Nazionale per l’Informatica - CINI, Bari, Italy

    Vincenzo Pasquadibisceglie, Annalisa Appice & Donato Malerba

  3. Lutech Group, Cinisello Balsamo (MI), 20092, Italy

    Giuseppe Ieva

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  1. Vincenzo Pasquadibisceglie
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  2. Annalisa Appice
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Contributions

V. P.: Conceptualization, Methodology, Software, Validation, Investigation, Writing - original draft, Writing - review & editing A. A.: Conceptualization, Methodology, Validation, Investigation, Writing - original draft, Writing - review & editing, Supervision, Project administration. G. I.: Conceptualization, Investigation, Writing - review & editing. D. M.: Conceptualization, Methodology, Writing - original draft, Writing - review & editing

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Correspondence to Vincenzo Pasquadibisceglie.

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Pasquadibisceglie, V., Appice, A., Ieva, G. et al. TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00838-5

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