This study explores online insurance ventures by customers' behavioral profiles from data mining and visualization. First, we employ a simple decision-tree statistical learning method on unique hand-collected and processed data in Taiwan. This method considers first-time online insurance subscribers as new ones without a proper marketing connection after six months. Second, using advanced clustering technique and decision tree statistical learning on first-time purchasing customers, we find they repurchase online travel insurance for different purposes varying periods. Finally, we get robust results engaging different segmentations of customer data. In these ways, we enable marketing strategies to work with generating decision rules.
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