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Domain Driven Data Mining to Improve Promotional Campaign ROI and Select Marketing Channels

Domain Driven Data Mining to Improve Promotional Campaign ROI and Select Marketing Channels . Presenter : Tsai Tzung Ruei Authors: Thomas Piton, Julien Blanchard, Henri Briand and Fabrice Guillet. 國立雲林科技大學 National Yunlin University of Science and Technology. CIKM.2009. Outline.

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Domain Driven Data Mining to Improve Promotional Campaign ROI and Select Marketing Channels

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  1. Domain Driven Data Mining to Improve Promotional Campaign ROI and Select Marketing Channels Presenter : Tsai TzungRuei Authors: Thomas Piton, Julien Blanchard, Henri Briand and FabriceGuillet 國立雲林科技大學 National Yunlin University of Science and Technology CIKM.2009

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Motivation • For each customer, there may be a large number of possible actions that can be applied . But actions, such as emailing, direct mailing and salespersons’ visits cost the company money. • To overcome this drawback, marketing managers need to acquire relevant knowledge on a one-to-one basis to decide which are the most effective communication channels to use for each customer.

  4. Objective • To propose an actionable knowledge discovery methodology, for one-to-one marketing, which allows to contact the right customer through the right communication channel. Rigrht Rigrht

  5. Methodology • THE RIGHT CUSTOMER

  6. Methodology • Naive profit curve

  7. Methodology • Choice of marketing channels

  8. Methodology • Model generation KR KI

  9. Methodology • Model interpretation

  10. Experiments • Model application

  11. Experiments • Model validation

  12. Experiments • During the previousoperation, the rate of buyers was 18 %. Whereas in the lastone, by applying our methodology, the rate of buyers raisedup to 22 %, the turnover was increased by 5 %, and 115 newcustomers participated, representing about 1 200 000 eurosof additional turnover.

  13. Conclusion • MAJOR CONTRIBUTION • To offer effective solutions to extract actionable knowledge to intelligent CRM for companies. • FUTURE WORK • To try to combineother methods of data mining to improve our methodology (associations rules for instance) with customer purchasing potential, with a view of preventing customer ”churning” during promotional campaigns, and of suggesting high profit materials to customers with the highest tendency.

  14. Comments • Advantage • To improve customer relationship • Drawback • ….. • Application • MRM, CRM

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