Simulating and Forecasting the Demand for New Consumer Durables

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http://urn.fi/URN:ISBN:951-555-825-5
Title: Simulating and Forecasting the Demand for New Consumer Durables
Author: Lerviks, Alf-Erik
Contributor: Swedish School of Economics and Business Administration, Department of Marketing and Corporate Geography, Marketing
Publisher: Svenska handelshögskolan
Date: 2004-05-28
Language: en
Belongs to series: Research Reports - 59
ISBN: 951-555-825-5
ISSN: 0357-5764
URI: http://hdl.handle.net/10227/239
http://urn.fi/URN:ISBN:951-555-825-5
Abstract: A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.
Subject: consumer durables
diffusion of innovations
adoption behaviour
interpersonal communication
word of mouth
diffusion models
growth curve models
simulation models
forecasting
calibration
replacement demand
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