C4.5: programs for machine learning
C4.5: programs for machine learning
Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
Personalization in Context: Does Context Matter When Building Personalized Customer Models?
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
International Journal of Intelligent Systems in Accounting and Finance Management
Expert Systems with Applications: An International Journal
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Human-Computer Interaction
Context as a dynamic construct
Human-Computer Interaction
Improving Personalization Solutions through Optimal Segmentation of Customer Bases
IEEE Transactions on Knowledge and Data Engineering
Dynamic micro-targeting: fitness-based approach to predicting individual preferences
Knowledge and Information Systems
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Intelligent profitable customers segmentation system based on business intelligence tools
Expert Systems with Applications: An International Journal
Visualization method for customer targeting using customer map
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Disseminating active map information to mobile hosts
IEEE Network: The Magazine of Global Internetworking
| Hi-index | 12.05 |
In e-commerce, where competition is tough and customers' preferences can change quickly, it is crucial for companies to segment customers and target marketing actions effectively. The process of segmentation and targeting is effective if the customers grouped into the same segment show the same behavior and reaction to marketing campaigns. However, the link between segmentation and targeting is often missing. Some research contributions have recently addressed this issue, by proposing approaches to build customer behavior models in each segment. However customers' behavior can change with the context, such as in many e-commerce business applications. In these cases, building contextual models of behavior would provide better predictive performance and, in turn, better targeting. However, the problem of including context in a segmentation model and building predictive behavior model of each segment consistently is still an open issue. This research aims at providing an answer to the following research issue: how to include context in a segmentation model in order to build an effective predictive model of customer behavior of each segment. To this aim we identified three different approaches and compared them by a set of experiments across several settings. The first result is that one of the three approaches dominates the others in certain conditions in our experiments. Another important result is that the most accurate approach is not always the most efficient from a managerial perspective.