Forecasting New Product Adoption with Probabilistic Neural Networks

Abstract

Forecasting the adoption of innovative products is an important managerial task. In this paper we examine the usefulness of a probabilistic neural network (PNN) algorithm for forecasting new product adoption. We compare this approach with one widely accepted forecasting procedure, the binomial logit model, and two other neural network algorithms: a feed-forward neural network model estimated with backward propagation (NNBP), and a feed-forward neural network model estimated with a genetic algorithm (NNGA). To test the relative forecasting accuracy of these algorithms, we examine the first-time adoption of DVD players. Our analysis is based on longitudinal consumer data collected between March 2000 and March 2001. We find that the PNN algorithm significantly outperforms the logit model and the two remaining neural network algorithms.