Comparing performance of feedforward neural nets and K-means for cluster-based market segmentation
Article Abstract:
A study was conducted on cluster-based market segmentation by comparing the performance of the K-means clustering approach and feedforward artificial neural network with one layer of hidden units. The real life data used showed that the K-means approach failed to identify other stronger cluster structure, while the feedforward neural network model secured a two-segment solution. Combining the two methods, however, was suggested to obtain more accurate data.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 1999
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Market segmentation by maximum likelihood clustering using choice elasticities
Article Abstract:
A study focuses on the determination of market segments by clustering households on the basis of choice elasticities across purchases and brands with respect to price, sales promotion and brand loyalty. The cluster analysis technique used is a maximum likelihood method that allows varying size and orientation and assumes constant volume.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 2004
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An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications
Article Abstract:
The application of choice models to brand purchase data is examined. The neural net-based multinomial logit choice model is compared to its alternative models. The purchase data for six largest brands in terms of market share is analyzed.
Publication Name: European Journal of Operational Research
Subject: Business, international
ISSN: 0377-2217
Year: 2004
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