Understanding online purchases with explainable machine learning

João Bastos and Maria Inês Bernardes

No 2024/0313, Working Papers REM from ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa

Abstract: Customer profiling in e-commerce is a powerful tool that enables organizations to create personalized offers through direct marketing. One crucial objective of customer profiling is to predict whether a website visitor will make a purchase, thereby generating revenue. Machine learning models are the most accurate means to achieve this objective. However, the opaque nature of these models may deter companies from adopting them. Instead, they may prefer simpler models that allow for a clear understanding of the customer attributes that contribute to a purchase. In this study, we show that companies need not compromise on prediction accuracy to understand their online customers. By leveraging website data from a multinational communications service provider, we establish that the most pertinent customer attributes can be readily extracted from a black-box model. Specifically, we show that features measuring customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant non-linear relationships between customer features and the likelihood of conversion.

Keywords: Customer Profiling; Conversion; Direct marketing; Explainable artificial intelligence; SHAP value; Accumulated local effects. (search for similar items in EconPapers)
Date: 2024-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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