Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Response modeling with support vector regression
Expert Systems with Applications: An International Journal
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Response modeling with support vector machines
Expert Systems with Applications: An International Journal
Exploiting randomness for feature selection in multinomial logit: a CRM cross-sell application
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Incorporating neighborhood effects in customer relationship management models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Expert Systems with Applications: An International Journal
Including spatial interdependence in customer acquisition models: A cross-category comparison
Expert Systems with Applications: An International Journal
Predicting e-commerce company success by mining the text of its publicly-accessible website
Expert Systems with Applications: An International Journal
Customer event history for churn prediction: How long is long enough?
Expert Systems with Applications: An International Journal
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
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Since customer relationship management (CRM) plays an increasingly important role in a company's marketing strategy, the database of the company can be considered as a valuable asset to compete with others. Consequently, companies constantly try to augment their database through data collection themselves, as well as through the acquisition of commercially available external data. Until now, little research has been done on the usefulness of these commercially available external databases for CRM. This study will present a methodology for such external data vendors based on random forests predictive modeling techniques to create commercial variables that solve the shortcomings of a classic transactional database. Eventually, we predicted spending pleasure variables, a composite measure of purchasing behavior and attitude, in 26 product categories for more than 3 million respondents. Enhancing a company's transactional database with these variables can significantly improve the predictive performance of existing CRM models. This has been demonstrated in a case study with a magazine publisher for which prospects needed to be identified for new customer acquisition.