Correcting for CBC model bias: a hybrid scanner data – conjoint model
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.
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Document Type: Research Article
Publication date: 2001-07-01