Integration of heterogeneous models to predict consumer behavior

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as association rule, frequency matrix, and tree-based models (CHAID, CART, QUEST, C5.0), this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or does not buy a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as association rule, frequency matrix, and tree-based models.

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