A New Marketing Strategy Map for Direct Marketing

International Journal of Knowledge-Based Systems, Forthcoming

KAIST Business School Working Paper Series No. 2009-001

37 Pages Posted: 24 Jan 2009  

Young Ae Kim

KAIST Business School

H. S. Song

affiliation not provided to SSRN

Soung Hie Kim

Korea Advanced Institute of Science and Technology (KAIST) - Management Engineering

Abstract

Direct marketing is one of the most effective marketing methods with an aim to maximize the customer's lifetime value. Many cost-sensitive learning methods which identify valuable customers to maximize expected profit have been proposed. However, current cost-sensitive methods for profit maximization do not identify how to control the defection probability while maximizing total profits over the customer's lifetime. Unfortunately, optimal marketing actions to maximize profits often perform poorly in minimizing the defection probability due to a conflict between these two objectives . In this paper, we propose the sequential decision making method for profit maximization under the given defection probability in direct marketing. We adopt a Reinforcement Learning algorithm to determine the sequential optimal marketing actions. With this finding, we design a marketing strategy map which helps a marketing manager identify sequential optimal campaigns and the shortest paths toward desirable states. Ultimately, this strategy leads to the ideal design for more effective campaigns.

Keywords: Sequential decision making; Reinforcement Learning; Direct marketing strategy; Customer Relationship Management; Marketing strategy map

Suggested Citation

Kim, Young Ae and Song, H. S. and Kim, Soung Hie, A New Marketing Strategy Map for Direct Marketing. International Journal of Knowledge-Based Systems, Forthcoming; KAIST Business School Working Paper Series No. 2009-001. Available at SSRN: https://ssrn.com/abstract=1323185

Young Ae Kim (Contact Author)

KAIST Business School ( email )

85 Hoegiro Dongdaemun-Gu
Seoul 130-722
Korea, Republic of (South Korea)

HOME PAGE: http://business.kaist.ac.kr/

H. S. Song

affiliation not provided to SSRN ( email )

Soung Hie Kim

Korea Advanced Institute of Science and Technology (KAIST) - Management Engineering ( email )

207-43 Cheongryangri-Dong
Dongdaemun-Ku
Seoul 130-722
United States

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