Machine Learning
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
A Web Services Application for the Data Quality Management in the B2B Networked Environment
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
Cross-validated bagged learning
Journal of Multivariate Analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Out-of-bag estimation of the optimal sample size in bagging
Pattern Recognition
Web Semantics: Science, Services and Agents on the World Wide Web
Expert Systems with Applications: An International Journal
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Protecting research and technology from espionage
Expert Systems with Applications: An International Journal
Web mining based extraction of problem solution ideas
Expert Systems with Applications: An International Journal
Weak signal identification with semantic web mining
Expert Systems with Applications: An International Journal
Quantitative cross impact analysis with latent semantic indexing
Expert Systems with Applications: An International Journal
Semantic compared cross impact analysis
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The customer acquisition process is generally a stressful undertaking for sales representatives. Luckily there are models that assist them in selecting the 'right' leads to pursue. Two factors play a role in this process: the probability of converting into a customer and the profitability once the lead is in fact a customer. This paper focuses on the latter. It makes two main contributions to the existing literature. Firstly, it investigates the predictive performance of two types of data: web data and commercially available data. The aim is to find out which of these two have the highest accuracy as input predictor for profitability and to research if they improve accuracy even more when combined. Secondly, the predictive performance of different data mining techniques is investigated. Results show that bagged decision trees are consistently higher in accuracy. Web data is better in predicting profitability than commercial data, but combining both is even better. The added value of commercial data is, although statistically significant, fairly limited.