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作者 : E.W.T. Ngai 、 Li Xiu 、 D.C.K. Chau 指導老師 : 詹智強 李英聯 學生 : 黃俊杰

Application of data mining techniques in customer relationship management A literature review and classification. 作者 : E.W.T. Ngai 、 Li Xiu 、 D.C.K. Chau 指導老師 : 詹智強 李英聯 學生 : 黃俊杰 學號 : 9715632 2008/12/21. Introduction(1/3).

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作者 : E.W.T. Ngai 、 Li Xiu 、 D.C.K. Chau 指導老師 : 詹智強 李英聯 學生 : 黃俊杰

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  1. Application of data mining techniques in customer relationship managementA literature review and classification 作者: E.W.T. Ngai、Li Xiu、 D.C.K. Chau 指導老師: 詹智強 李英聯 學生: 黃俊杰 學號: 9715632 2008/12/21

  2. Introduction(1/3) • CRM：Customer relationship management. • Customer data and information technology (IT) tools form the foundation upon which any successful CRM strategy is built. • Business intelligence.

  3. Introduction(2/3) • CRM framework can be classified into operational and analytical. • The hidden knowledge. • Uses statistical, mathematical, artificial intelligence、machine-learning techniques.

  4. Introduction(3/3) • First, research methodology used in the study is described. • Second, method for classifying data mining articles in CRM. • Third, articles about data mining in CRM are analysed and the classification. • Finally, conclusions, limitations and implications of the study are discussed.

  5. Classification method • (1) Customer Identification. • (2) Customer Attraction. • (3) Customer Retention. • (4) Customer Development.

  6. Data mining algorithms • (1) Association rule. • (2) Decision tree. • (3) Genetic algorithm. • (4) Neural networks. • (5) K-Nearest neighbour. • (6) Linear/logistic regression.

  7. Classification framework for data mining techniques in CRM

  8. Classification framework data mining models • (1) Association. • (2) Classification. • (3) Clustering. • (4) Forecasting. • (5) Regression. • (6) Sequence discovery. • (7) Visualization.

  9. Classification process • (1) Online database search. • (2) Initial classification by first researcher. • (3) Independent verification of classification results by second researcher. • (4) Final verification of classification results by third researcher.

  10. Selection criteria and evaluation framework

  11. Distribution of articles by CRM and data mining model

  12. Distribution of articles by data mining techniques

  13. Distribution of articles by year of publication

  14. Conclusion(1/3) • Application of data mining techniques in CRM is an emerging trend in the industry. • Provide insight to organization policy makers on the common data mining practices used in retaining customers.

  15. Conclusion(2/3) • Data mining techniques could be applied to discover unseen patterns of complaints from a company’s database. • Neural networks and decision trees, could be used to seek the profitable segments of customers through analysis of customers’ underlying characteristics.

  16. Conclusion(3/3) • Policy makers have to both retain valuable customers and increase the lifetime value of the customer. • Customer retention and development are both important to maintaining a long term and pleasant relationship with customers.

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