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A data‐driven modeling approach to product level decision support

A data‐driven modeling approach to product level decision support The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Product & Brand Management Emerald Publishing

A data‐driven modeling approach to product level decision support

Giles D’Souza
Journal of Product & Brand Management , Volume 6 (2): 13 – Apr 1, 1997
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References (29)

Publisher
Emerald Publishing
Copyright
Copyright © 1997 MCB UP Ltd. All rights reserved.
ISSN
1061-0421
DOI
10.1108/10610429710175664
Publisher site
See Article on Publisher Site

Abstract

The marriage of new scanner‐type data sources and new computing and analysis methods is allowing a new approach to the development and use of models for decision support and product line management. Data‐driven modeling describes a process of model‐building wherein models are created that fit the dynamics of the data rather than assuming a priori relationships among brands and their marketing mix elements. Based on a combination of time‐series and econometric modeling methods, these models can significantly improve a modeler’s ability to capture marketplace structure and dynamics. Although more complex than their predecessors, the capabilities of these new data‐driven decision support models make them potentially very powerful tools, improving intuition and managerial understanding while suggesting improved decision alternatives. Develops such a model using detailed multiproduct retail data and demonstrates its capabilities.

Journal

Journal of Product & Brand Management – Emerald Publishing

Published: Apr 1, 1997

Keywords: Decision‐support systems; Econometrics; Modelling; Retailing; Time series analysis

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