Using neural networks to aid the diagnosis of breast implant rupture
Computers and Operations Research
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Segmentation-based competitive analysis with MULTICLUS and topology representing networks
Computers and Operations Research - Neural networks in business
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Computers and Operations Research
Neural Networks in Business Forecasting
Neural Networks in Business Forecasting
Simultaneous optimization of neural network function and architecture algorithm
Decision Support Systems
The exploration of consumers' behavior in choosing hospital by the application of neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Towards optimal use of incomplete classification data
Computers and Operations Research
Electronic promotion to new customers using mkNN learning
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Assessing the severity of phishing attacks: A hybrid data mining approach
Decision Support Systems
Development and performance evaluation of neural network classifiers for Indian internet shoppers
Expert Systems with Applications: An International Journal
Improved response modeling based on clustering, under-sampling, and ensemble
Expert Systems with Applications: An International Journal
Audience targeting by B-to-B advertisement classification: A neural network approach
Expert Systems with Applications: An International Journal
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This article develops an alternative estimation approach for classifying new prospective consumers as "good" or "bad" prospects for direct marketing purposes. We show that the traditional approach of using demographics alone to profile non-active consumers (those who have yet to buy in the category) can be improved by waiting to observe their initial and limited number of sequential purchases in the category. We call this method the early purchase classification (EPC) approach. We make use of two established classification models, a multinomial logit model (MNL) and a neural network model (NN), and show that the classification accuracy of both models using our EPC approach outperforms the traditional approach of classifying non-active prospects using demographics only. Furthermore, we find that the NN model consistently outperforms the MNL model at this task. This research uses the best aspects of each model by utilizing the MNL model to determine which variables are most relevant to the classification and then using those variables for classification in the NN model. Using the complementary features of the MNL and NN models, managers can use the EPC approach to determine the most profitable time in a purchasing history to classify and target prospective consumers new to their categories.