Modern mathematical statistics
Modern mathematical statistics
Using the data warehouse
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
A comparative study of neural network based feature extraction paradigms
Pattern Recognition Letters
Measuring lift quality in database marketing
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Neural Networks: A Comprehensive Foundation
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Introduction to Bayesian Networks
Introduction to Bayesian Networks
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Relational differential prediction
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A joint optimization of incrementality and revenue to satisfy both advertiser and publisher
Proceedings of the 22nd international conference on World Wide Web companion
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In database marketing, data mining has been used extensively to find the optimal customer targets so as to maximize return on investment. In particular, using marketing campaign data, models are typically developed to identify characteristics of customers who are most likely to respond. While these models are helpful in identifying the likely responders, they may be targeting customers who have decided to take the desirable action or not regardless of whether they receive the campaign contact (e.g. mail, call). Based on many years of business experience, we identify the appropriate business objective and its associated mathematical objective function. We point out that the current approach is not directly designed to solve the appropriate business objective. We then propose a new methodology to identify the customers whose decisions will be positively influenced by campaigns. The proposed methodology is easy to implement and can be used in conjunction with most commonly used supervised learning algorithms. An example using simulated data is used to illustrate the proposed methodology. This paper may provide the database marketing industry with a simple but significant methodological improvement and open a new area for further research and development.