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Journal of Intelligent Information Systems

28.12.2023 | Research

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

verfasst von: Vincenzo Pasquadibisceglie, Annalisa Appice, Giuseppe Ieva, Donato Malerba

Erschienen in: Journal of Intelligent Information Systems

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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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Any accuracy metric can be used in this place.

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.

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

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

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

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Titel: TSUNAMI - an explainable PPM approach for customer churn prediction in evolving retail data environments
verfasst von: Vincenzo Pasquadibisceglie
Annalisa Appice
Giuseppe Ieva
Donato Malerba
Publikationsdatum: 28.12.2023
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-023-00838-5

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