Machine Learning
Mailing decisions in the catalog sales industry
Management Science
Direct Marketing Performance Modeling Using Genetic Algorithms
INFORMS Journal on Computing
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Genetic programming in classifying large-scale data: an ensemble method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Customer-adapted coupon targeting using feature selection
Expert Systems with Applications: An International Journal
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
Mining customer knowledge for product line and brand extension in retailing
Expert Systems with Applications: An International Journal
Mining in-depth patterns in stock market
International Journal of Intelligent Systems Technologies and Applications
Expert Systems with Applications: An International Journal
Quantifying the indirect effects of a marketing contact
Expert Systems with Applications: An International Journal
Using visual and text features for direct marketing on multimedia messaging services domain
Multimedia Tools and Applications
Ontology-based data mining approach implemented for sport marketing
Expert Systems with Applications: An International Journal
Ontology-based data mining approach implemented on exploring product and brand spectrum
Expert Systems with Applications: An International Journal
Application of data mining to the spatial heterogeneity of foreclosed mortgages
Expert Systems with Applications: An International Journal
Accounting for the long-term effects of a marketing contact
Expert Systems with Applications: An International Journal
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Ensembles of probability estimation trees for customer churn prediction
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Mining shopping behavior in the Taiwan luxury products market
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
Although most data-mining (DM) models are complex and general in nature, the implementation of such models in specific environments is often subject to practical constraints (e.g. budget constraints) or thresholds (e.g. only mail to customers with an expected profit higher than the investment cost). Typically, the DM model is calibrated neglecting those constraints/thresholds. If the implementation constraints/thresholds are known in advance, this indirect approach delivers a sub-optimal model performance. Adopting a direct approach, i.e. estimating a DM model in knowledge of the constraints/thresholds, improves model performance as the model is optimized for the given implementation environment. We illustrate the relevance of this constrained optimization of DM models on a direct-marketing case, i.e. in the field of customer relationship management. We optimize an individual-level response model for specific mailing depths (i.e. the percentage of customers of the house list that actually receives a mail given the mailing budget constraint) and compare its predictive performance with that of a traditional response model, neglecting the mailing depth during estimation. The results are in favor of the constrained-optimization approach.