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

Abstract

The Bass (1969) model has been a standard for analyzing and predicting the market penetration of new products. The authors demonstrate the insights to be gained and predictive performance of Functional Data Analysis (FDA), a new class of non-parametric techniques that has shown impressive results within the statistics community, on the market penetration of 760 products drawn from 21 products and 70 countries. The authors propose a new model called Functional Regression and compare its performance to over 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 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.

Keywords: Global Market Penetration, Diffusion, Bass Model, Functional Data Analysis, Functional Principal Components, Generalized Additive Models, Functional Clustering, Spline Regression, New Products