C4.5: programs for machine learning
C4.5: programs for machine learning
The Decomposition of Promotional Response: An Empirical Generalization
Marketing Science
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Using sub-sequence information with kNN for classification of sequential data
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
Ensemble learning for customers targeting
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Expert Systems with Applications: An International Journal
Technology classification with latent semantic indexing
Expert Systems with Applications: An International Journal
Protecting research and technology from espionage
Expert Systems with Applications: An International Journal
Weak signal identification with semantic web mining
Expert Systems with Applications: An International Journal
Semantic compared cross impact analysis
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The management of coupon promotions is an important issue for marketing managers since it still is the major promotion medium. However, the distribution of coupons does not go without problems. Although manufacturers and retailers are investing heavily in the attempt to convince as many customers as possible, overall coupon redemption rate is low. This study improves the strategy of retailers and manufacturers concerning their target selection since both parties often end up in a battle for customers. Two separate models are built: one model makes predictions concerning redemption behavior of coupons that are distributed by the retailer while another model does the same for coupons handed out by manufacturers. By means of the feature-selection technique 'Relief-F' the dimensionality of the models is reduced, since it searches for the variables that are relevant for predicting the outcome. In this way, redundant variables are not used in the model-building process. The model is evaluated on real-life data provided by a retailer in Fast Moving Consumer Goods (FMCG). The contributions of this study for retailers as well as manufacturers are three-fold. First, the possibility to classify customers concerning their coupon usage is shown. In addition, it is demonstrated that retailers and manufacturers can stay clear of each other in their marketing campaigns. Finally, the feature-selection technique Relief-F proves to facilitate and optimize the performance of the models.