Mailing decisions in the catalog sales industry
Management Science
The Decomposition of Promotional Response: An Empirical Generalization
Marketing Science
Bayesian Statistics and Marketing
Marketing Science
The Effects of Effort and Intrinsic Motivation on Risky Choice
Marketing Science
Editorial: Who Is Afraid to Give Freedom of Speech to Marketing Folks?
Marketing Science
Marketing Models of Service and Relationships
Marketing Science
EditorialAre Consumers Rational? Experimental Evidence?
Marketing Science
Editorial: Who Is Afraid to Give Freedom of Speech to Marketing Folks?
Marketing Science
Optimizing marketing planning and budgeting using Markov decision processes: an airline case study
IBM Journal of Research and Development - Business optimization
Firm-Created Word-of-Mouth Communication: Evidence from a Field Test
Marketing Science
Telecommunications Policy
Up close and personalized: a marketing view of recommendation systems
Proceedings of the third ACM conference on Recommender systems
Dynamic Customer Management and the Value of One-to-One Marketing
Marketing Science
Construction of classification models for credit policies in banks
International Journal of Electronic Finance
Determining Optimal CRM Implementation Strategies
Information Systems Research
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
We provide a fully personalized model for optimizing multiple marketing interventions in intermediate-term customer relationship management (CRM). We derive theoretically based propositions on the moderating effects of past customer behavior and conduct a longitudinal validation test to compare the performance of our model with that of commonly used segmentation models in predicting intermediate-term, customer-specific gross profit change. Our findings show that response to marketing interventions is highly heterogeneous, that heterogeneity of response varies across different marketing interventions, and that the heterogeneity of response to marketing interventions may be partially explained by customer-specific variables related to customer characteristics and the customer's past interactions with the company. One important result from these moderating effects is that relationship-oriented interventions are more effective with loyal customers, while action-oriented interventions are more effective with nonloyal customers. We show that our proposed model outperformed models based on demographics, recency-frequency-monetary value (RFM), or finite mixture segmentation in predicting the effectiveness of intermediate-term CRM. The empirical results project a significant increase in intermediate-term profitability over all of the competing segmentation approaches and a significant increase in intermediate-term profitability over current practice.