Abstract
Developing mathematical models does convey an impression or perception to the average business person that these tools are highly accurate due to the complexity in creating these tools. This article attempts to dispel this notion by conveying to the reader the accuracy limitations in developing predictive models. At the same time, the author discusses some of the rationale as to why these limitations exist. Yet, even with model accuracy being a limiting factor, these tools still yield tremendous business benefits that accrue right to the bottom line.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Lobo, J.M., Jiménez-Valverde, A. and Real, R. (2008) AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography 17 (2): 145–151.
Stockwell, D.R.B. and Peterson, T.P.A. (2002) Effect of sample size on accuracy of species distribution models. Ecological Modelling 148 (1): 1–13.
Zou, K.H., O’Malley, A.J. and Mauri, L. (2007) Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation 115 (5): 654–657.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Boire, R. Is predictive analytics for marketers really that accurate?. J Market Anal 1, 118–123 (2013). https://doi.org/10.1057/jma.2013.8
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1057/jma.2013.8