Procedures for optimization problems with a mixture of bounds and general linear constraints
ACM Transactions on Mathematical Software (TOMS)
Nine management guidelines for better cost estimating
Communications of the ACM
Empirically Guided Software Effort Guesstimation
IEEE Software
Improving Subjective Estimates Using Paired Comparisons
IEEE Software
A Causal Model for Software Cost Estimating Error
IEEE Transactions on Software Engineering
Kernel partial least squares regression in reproducing kernel hilbert space
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Agile Estimating and Planning
Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms
Management Science
Determinants of customer loyalty in the wireless telecommunications industry
Telecommunications Policy
Computer assisted customer churn management: State-of-the-art and future trends
Computers and Operations Research
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model after relative management costs of controllable marketing variables are estimated through a triangulation method. Finally, a PLS optimization model with marketing objectives and constraints are specified and solved via a sequential quadratic programming method. In our experiments, we observe that while the training and test data sets are dramatically different in terms of churner distributions (50% vs. 1.8%), four controllable variables in three marketing strategies significantly changed through optimization process while other variables only marginally changed. We also observe that the most significant variable in a PLS prediction model does not necessarily change most significantly in our PLS optimization model due to the highest management cost associated, implying differences between a prediction and an optimization model. Finally, two marketing models designed for targeting the subsets of customers based on churn probability or management costs are presented and discussed.