Cross-Selling Through Database Marketing: A Mixed Data Factor Analyzer for Data Augmentation and Prediction

An important aspect of the new orientation on customer relationship marketing is the use of customer transaction databases for the cross-selling of new services and products. In this study, we propose a mixed data factor analyzer that combines information from a survey with data from the customer database on service usage and transaction volume, to make probabilistic predictions of ownership of services with the service provider and with competitors. This data-augmentation tool is more flexible in dealing with the type of data that are usually present in transaction databases. We test the proposed model using survey and transaction data from a large commercial bank. We assume four different types of distributions for the data: Bernoulli for binary service usage items, rank-order binomial for satisfaction rankings, Poisson for service usage frequency, and normal for transaction volumes. We estimate the model using simulated likelihood (SML). The graphical representation of the weights produced by the model provides managers with the opportunity to quickly identify cross-selling opportunities. We exemplify this and show the predictive validity of the model on a hold-out sample of customers, where survey data on service usage with competitors is lacking. We use Gini concentration coefficients to summarize power curves of prediction, which reveals that our model outperforms a competing latent trait model on the majority of service predictions.