Applications of machine learning and rule induction
Communications of the ACM
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Data mining: concepts and techniques
Data mining: concepts and techniques
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Expert Systems with Applications: An International Journal
Application of wrapper approach and composite classifier to the stock trend prediction
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Selection of mobile value-added services for system operators using fuzzy synthetic evaluation
Expert Systems with Applications: An International Journal
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
Selecting prospects for cross-selling financial products using multivariate credibility
Expert Systems with Applications: An International Journal
A two-phase case-based distance approach for multiple-group classification problems
Computers and Industrial Engineering
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Mobile phone customer retention strategies and Chinese e-commerce
Electronic Commerce Research and Applications
| Hi-index | 12.05 |
As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company's case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.