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Functional Regression: A New Model for Predicting Market Penetration of New Products

Goizueta School of Business, Emory University, Atlanta, Georgia 30322
Marshall School of Business, University of Southern California, Los Angeles, California 90089
Marshall School of Business, University of Southern California, Los Angeles, California 90089
ashish_sood{at}bus.emory.edu
gareth{at}usc.edu
tellis{at}usc.edu

The Bass model has been a standard for analyzing and predicting the market penetration of new products. We demonstrate the insights to be gained and predictive performance of functional data analysis (FDA), a new class of nonparametric techniques that has shown impressive results within the statistics community, on the market penetration of 760 categories drawn from 21 products and 70 countries. We propose a new model called Functional Regression and compare its performance to several models, including the Classic Bass model, Estimated Means, Last Observation Projection, a Meta-Bass model, and an Augmented Meta-Bass model for predicting eight aspects of market penetration. Results (a) validate the logic of FDA in integrating information across categories, (b) show that Augmented Functional Regression is superior to the above models, and (c) product-specific effects are more important than country-specific effects when predicting penetration of an evolving new product.

Key Words: predicting market penetration; global diffusion; Bass model; functional data analysis; functional principal components; generalized additive models; functional clustering; spline regression; new products
History: Received: October 1, 2007; accepted: January 10, 2008.

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