| Summary: | 碩士 === 國立臺灣大學 === 統計碩士學位學程 === 105 === Managers can measure customers'' churn rate and activation by Inter-Purchase time for CRM. Current studies mostly focus on single category behavior. In our study, we introduce copula and construct a multivariate model of the Multi-Category Inter-Purchase Time, which may help us make better predictions or better understand customer behavior, providing three prediction architectures and four models. We can update the predictive value as we observe a purchasing record of other category. When not happening purchasing records of other categories or the customers just buy the category, the predictive result will return to single category model. Finally, our model performs better than single and weighted category architecture when number of purchase times is less, correlation coefficient is different from 0, purchase interval is longer, and interval of the given category is shorter. Moreover, our model provides better explanation of the customer purchasing behavior with correlation matrix.
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