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Correcting for CBC model bias: a hybrid scanner data – conjoint model

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Abstract:

Choice-Based Conjoint (CBC) models are often used for pricing decisions, especially when scanner data models cannot be applied. To date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting real-world shop data. In this contribution, the performance of a latent class CBC model is measured not by means of an experimental hold-out sample but via aggregate scanner data. It is found 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, a correction scheme based on scanner data is proposed. Our empirical analysis shows that the hybrid method improves the performance measures considerably.

Keywords: CHOICE-BASED CONJOINT ANALYSIS; EXTERNAL VALIDITY; LATENT CLASS MODEL; PRICING; SCANNER DATA MODEL

Document Type: Research Article

DOI: https://doi.org/10.1080/713770600

Publication date: 2001-07-01

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