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Erschienen in:
2015 | OriginalPaper | Buchkapitel
A Two Tiered Finite Mixture Modelling Framework to Cluster Customers on EFTPOS Network
verfasst von : Yuan Jin, Grace Rumantir
Erschienen in: AI 2015: Advances in Artificial Intelligence
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Abstract
This paper proposes a framework to build a clustering model of customers of the retailers on the EFTPOS network of a major bank in Australia. The framework consists of two clustering tiers using Finite Mixture Modelling (FMM) that segments customers based on their probabilities of generating transactions of different categories. The first tier generates the transaction categories and the second tier segments the customers, each with a vector of the fractions of their transaction categories as parameters. For each tier, we determine the optimal number of clusters based on the Minimum Message Length (MML) criterion. With the premise that the most valuable customer segment is one that is most likely to generate the most valuable transaction category, we rank the customer segments based on their respective joint probabilities with the most valuable transaction category. By doing so, we are able to reveal the relative value of each customer segment.