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 5 Piece Queen Slat Panel Bedroom 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 Skin Oil platform are the most reliable predictors of conversions.Moreover, we uncover significant nonlinear relationships between customer features and the likelihood of conversion.