Optimizing Product Portfolios Using Discrete Choice Modeling and TURF

Abstract

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

Citation

Adler, T.J., Smith, C. and Dumont, J. (2010), "Optimizing Product Portfolios Using Discrete Choice Modeling and TURF", Hess, S. and Daly, A. (Ed.) Choice Modelling: The State-of-the-art and The State-of-practice, Emerald Group Publishing Limited, Leeds, pp. 483-497. https://doi.org/10.1108/9781849507738-022