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Activity Number: 467
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Marketing
Abstract - #305366
Title: Copula-Based Dependence Structure Estimation for Monte Carlo Simulation in Marketing Research
Author(s): Joseph Retzer*+ and Michael Conklin
Companies: MarketTools Inc. and MarketTools Inc.
Address: 2019 E River Rd, Grafton, WI, 53024-9654, United States
Keywords: copula ; Monte Carlo ; Shapley

Cumulative distribution functions of key driver attribute variables can be written in terms of marginal distribution functions and a copula. The marginals describe the distribution function of each driver while the copula describes the dependence structure between them. This paper will explore various market research applications of copula based density dependence structure approximation for purposes of prediction. Specifically, using this information, Monte Carlo simulation will be employed for predicting product adoption in both pharma and technology sectors. We will also explore the use of copulas in evaluating market potential and reach in CPG markets via Shapley Value model averaged reach measurement. The use of copulas will allow modeling and estimation of the distribution of drivers of product adoption and reach by separately estimating marginals and the associated copula.

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