Understanding Online Purchases with Explainable Machine Learning
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...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/10/587 |
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| author | João A. Bastos Maria Inês Bernardes |
| author_facet | João A. Bastos Maria Inês Bernardes |
| author_sort | João A. Bastos |
| collection | DOAJ |
| description | 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 the features that measure customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant nonlinear relationships between customer features and the likelihood of conversion. |
| format | Article |
| id | doaj-art-6569d7aeecf74228987896a7ae9800aa |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-6569d7aeecf74228987896a7ae9800aa2025-08-20T02:11:04ZengMDPI AGInformation2078-24892024-09-01151058710.3390/info15100587Understanding Online Purchases with Explainable Machine LearningJoão A. Bastos0Maria Inês Bernardes1Lisbon School of Economics and Management (ISEG) and REM, Universidade de Lisboa, 1649-004 Lisboa, PortugalLisbon School of Economics and Management (ISEG) and REM, Universidade de Lisboa, 1649-004 Lisboa, PortugalCustomer 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 the features that measure customer activity within the e-commerce platform are the most reliable predictors of conversions. Moreover, we uncover significant nonlinear relationships between customer features and the likelihood of conversion.https://www.mdpi.com/2078-2489/15/10/587customer profilingconversiondirect marketingexplainable artificial intelligenceSHAP valueaccumulated local effects |
| spellingShingle | João A. Bastos Maria Inês Bernardes Understanding Online Purchases with Explainable Machine Learning Information customer profiling conversion direct marketing explainable artificial intelligence SHAP value accumulated local effects |
| title | Understanding Online Purchases with Explainable Machine Learning |
| title_full | Understanding Online Purchases with Explainable Machine Learning |
| title_fullStr | Understanding Online Purchases with Explainable Machine Learning |
| title_full_unstemmed | Understanding Online Purchases with Explainable Machine Learning |
| title_short | Understanding Online Purchases with Explainable Machine Learning |
| title_sort | understanding online purchases with explainable machine learning |
| topic | customer profiling conversion direct marketing explainable artificial intelligence SHAP value accumulated local effects |
| url | https://www.mdpi.com/2078-2489/15/10/587 |
| work_keys_str_mv | AT joaoabastos understandingonlinepurchaseswithexplainablemachinelearning AT mariainesbernardes understandingonlinepurchaseswithexplainablemachinelearning |