Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales
Predicting the class of customer profiles is a key task in marketing, which enables businesses to approach the customers in a right way to satisfy the customer’s evolving needs. However, due to costs, privacy and/or data protection, only the business’ owned transactional data is typically available for constructing customer profiles. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved.
KeywordsCritical Number Food Retail Customer Lifetime Transactional Data Experimental Case Study
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