The nature of statistical learning theory
The nature of statistical learning theory
Linear Programming Boosting via Column Generation
Machine Learning
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Structural Modelling with Sparse Kernels
Machine Learning
Customer Lifetime Value Models for Decision Support
Data Mining and Knowledge Discovery
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Marketing Models of Service and Relationships
Marketing Science
Artificial Intelligence in Medicine
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
A modified Pareto/NBD approach for predicting customer lifetime value
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Customer Lifetime Value Measurement
Management Science
A Hidden Markov Model of Customer Relationship Dynamics
Marketing Science
Expert Systems with Applications: An International Journal
Dynamic Customer Management and the Value of One-to-One Marketing
Marketing Science
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Exploring the preference of customers between financial companies and agents based on TCA
Knowledge-Based Systems
Consistency of support vector machines using additive kernels for additive models
Computational Statistics & Data Analysis
An experimental bias-variance analysis of SVM ensembles based on resampling techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
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Customer lifetime value (CLV), as an important metric in customer relationship management (CRM), has attracted widespread attention over the last decade. Most CLV prediction models do not take into consideration the dynamics of the customer purchase behavior and changes of the marketing environment such as the adoption of different promotion policies. In this study, a framework for the dynamic CLV prediction using longitudinal data is presented. In the framework, both the dynamic customer purchase behavior and customized promotions are considered. An improved multiple kernel support vector regression (MK-SVR) approach is developed to predict the future CLV and select the best promotion using both the customer behavioral variables and controlled variable about multiple promotions. Computational experiments using two databases show that the MK-SVR exhibits good prediction performance and the usage of longitudinal data in the MK-SVR facilitate the dynamic prediction and promotion optimization.