| Summary: | 碩士 === 國立臺北大學 === 企業管理學系 === 101 === The main focus of this research is to discuss how industries can utilize basic information and transactional data of customers by applying data mining technique to further isolate those of who with hidden or inconspicuous data connections in order to achieve efficient customer relationship management.This study uses online transactions information for the past three years of the cast study company as samples for analysis. Recency (R), Frequency (F), Monetary (M) and Length (L) are the four variables chosen to be analyzed in the Analytic Hierarchy Process (AHP) and expert modeling to determine the weight and further evaluate Customer Lifetime Value (CLV) through those weighed variables.Using the above four variables as indexes for separating customers and by using data mining technique to analyze expectation- maximization algorithm, we can successfully segment customers into six groups- New Customers, Scratch the Surface Customers, Potential Customers, Cautious Customers, Adhesive Customers and Loyal Customers.Next, by applying Association Rules Apriori and Sequence Cluster Analysis we can determine purchase connections and sequences of each group. Finally through filters and domain know- how, we are able to customize cross and vertical sales marketing strategies which in turn provides valuable suggestions to the upper management marketing decisions.
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