| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 100 === In this study, using Internet banking member’s transaction database, the use of customers on the Internet banking transaction details, calculated relative and absolute activity indicators to distinguish each customer changes in activity on the Internet banking. This study expected to more precise distinction between customer behavior, planning appropriate marketing projects, to improve the effectiveness and efficiency of the use of marketing resources.
First use of transaction data to calculate the interpurchase time, using interpurchase time to measure customer activity, further calculate average interpurchase time and weighted average interpurchase time. Comparison the difference between average interpurchase time and weighted average interpurchase time can be calculated the Customer Activity Index (CAI), and CAI relatively active indicators can judge the trend of customer activity.
Estimated customer activity, another method is hierarchical Bayesian estimation. Hierarchical Bayesian estimation can solve individual data scarcity problem and correct individual differences at the same time. Coupled with the use of Markov chain Monte Carlo (MCMC) methods to simulate the distribution of each customer’s interpurchase time, in addition to calculate each customer’s average interpurchase time, can still be learned every customer’s interpurchase time variability. The application of the results of this estimate and the aforementioned average interpurchase time, weighted average interpurchase time, and CAI, can produce customer segmentation. Then get the marketing implications, provide important reference information for marketing management.
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