Grouping Customers Using Frequently Purchased Goods Sets
نویسنده
چکیده
Analysing customers in groups is one of the most fundamental issues in Marketing. It helps companies sufficiently learn from their customers, and rationally design their marketing strategies. Given a customer database with n records (customers) and m attributes (one’s characteristics) stored, different approaches can be applied to automatically segment (cluster) records in divisions. In this paper, we propose an Association Rule Mining (ARM) based approach, which introduces transaction (market-basket) data into current customer segmentation task, and automatically recognises customers in groups (possibly overlapping) using their frequently purchased goods sets (noticed as frequent itemsets in ARM). Furthermore, a variety of strategies and/or techniques (i.e., statistics based, data mining based, machine learning based, etc.) that are found in current customer segmentation can be applied to deeply extract the similarities in customer characteristics within each pre-generated group, which indicates why these particular goods are purchased simultaneously.
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