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The purpose of this study was to build a customer selection model based on 20dimensions, including customer codes, total contribution, assets, deposit,profit, profit rate, trading volume, trading amount, turnover rate, orderamount, withdraw amount, withdraw rate, process fee, process fee submitted,process fee retained, net process fee retained, interest revenue, interestreturn, exchange house return I and exchange house return II to group and rankcustomers. The traditional way to group customers in securities or futurescompanies is simply based on their assets. However, grouping customers withrespect to only one dimension cannot give us a full picture about customers'attributions. It is hard to group customers' with similar attributions orvalues into one group if we just consider assets as the only groupingcriterion. Nowadays, securities or futures companies usually group customersbased on managers' experience with lack of quantitative analysis, which is noteffective. Therefore, we use kmeans unsupervised learning methods to groupcustomers with respect to significant dimensions so as to cluster customerswith similar attributions together. Grouping is our first step. It is thehorizontal analysis in customer study. The next step is customer ranking. It isthe longitudinal analysis. It ranks customers by assigning each customer with acertain score given by our weighted customer value calculation formula.Therefore, by grouping and ranking customers, we can differentiate ourcustomers and rank them based on values instead of blindly reaching everyone.

Customer Selection Model with Grouping and Hierarchical Ranking Analysis