Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Technical Note: \cal Q-Learning
Machine Learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multi-criteria Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Reinforcement Learning with Bounded Risk
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cross channel optimized marketing by reinforcement learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Risk-sensitive reinforcement learning applied to control under constraints
Journal of Artificial Intelligence Research
Agent-based analysis and simulation of the consumer airline market share for Frontier Airlines
Knowledge-Based Systems
| Hi-index | 0.00 |
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.