Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

Hyunchul Ahn, Jae Joon Ahn, Kyong Joo Oh, Dong Ha Kim

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)5005-5012
Number of pages8
JournalExpert Systems with Applications
Issue number5
Publication statusPublished - 2011 May

Bibliographical note

Funding Information:
This work was supported (in part) by the new faculty research program 2009 of Kookmin University in Korea.

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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