Improving the prediction performance of customer behavior through multiple imputation

Various predictive modeling approaches based on the customers’ information may be used for selecting proper targets for a promoted product to entice customers into purchasers. However, there is a fundamental problem, the incomplete data which can yield biased results and deteriorate the accuracy of those approaches. So far, several methods such as case deletion and mean substitution are applied to handle the incomplete dataset in various domains. Those approaches are simple and easy to implement but may also provide biased results. Recently multiple imputation is suggested as a method to overcome the flaws in traditional treatments through reflecting the uncertainty of missing values in the incomplete dataset. This study is designed to introduce the multiple imputation technique and show two experimental works of several imputation methods applied to the real cases in electronic customer relationship management domain, the first with missing covariates and the second with missing targets. According to the results of the experimental works, the multiple-imputation based approaches produced the better performance than the traditional approaches in both of two case studies. Especially, the multiple imputation technique proved to be more effective in the dataset with a high missing rate than the one with a low missing rate.