• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.535-546
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• 2010

There are two methods in grouping customers for micro marketing promotion. The one is based on how much they paid and the other is based on how many times they purchased. In this study we are interested in the repurchase probability of customers. By analysing the customer's transaction data and demographic data, we develop a forecasting model of repurchase and make epurchase indexes of them. As a modeling tool we use the logistic regression model. Finally we categorize the customers into five groups in according to their repurchase indexes so that we can control customers effectively and get higher profit.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.615-627
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• 2009

In micro marketing promotion, it is important to know the behavior of customers. In this study we are interested in the forecasting of repurchase of customers from customers' behavior. By analyzing the cosmetic transaction data we derive some variables which play an important role in the knowledge of the customers' behavior and in the modeling of repurchase. As modeling tools we use the decision tree, logistic regression and neural network model. Finally we decide to use the decision tree as a final model since it yields the smallest RASE (root average squared error) and the greatest correct classification rate.

• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.291-300
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• 2019

This study investigates the characteristics of evaluation measures for classification models on a binary response variable in order to evaluate their suitability for use. Six measures are considered: Accuracy, Sensitivity, Specificity, Precision, F-measure, and the Heidke's skill score (HSS). Evaluation measures are reformulated using x(ratio of actually 1), y(ratio predicted by 1), z(ratio of both actual and predicted by 1) from the confusion matrix. We suggest two necessary conditions to assess the suitability of the evaluation measures. The first condition is that the measure function is constant for x and y in the case of a random model. The second condition is that the measure function is increasing for z and decreasing for x and y. Since only HSS satisfies the two conditions, that is always appropriate as an evaluation measure for the classification model on the binary response variable, and the other measures should be used within a limited range.