Correcting for CBC model bias. A hybrid scanner data - conjoint model.

Martin Natter, Markus Feurstein

Publikation: Working/Discussion PaperWU Working Paper

27 Downloads (Pure)

Abstract

Choice-Based Conjoint (CBC) models are often used for pricing decisions, especially when scanner data models cannot be applied. Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting real-world shop data. In this contribution, we measure the performance of a Latent Class CBC model not by means of an experimental hold-out sample but via aggregate scanner data. We find that the CBC model does not accurately predict real-world market shares, thus leading to wrong pricing decisions. In order to improve its forecasting performance, we propose a correction scheme based on scanner data. Our empirical analysis shows that the hybrid method improves the performance measures considerably. (author's abstract)

Publikationsreihe

ReiheReport Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
Nummer57

WU Working Paper Reihe

  • Report Series SFB \Adaptive Information Systems and Modelling in Economics and Management Science\

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