Reality Check: Combining Choice Experiments with Market Data to Estimate the Importance of Product Attributes

Discrete choice models estimated using hypothetical choices made in a survey setting (i.e., choice experiments) are widely used to estimate the importance of product attributes in order to make product design and marketing mix decisions. Choice experiments allow the researcher to estimate preferences for product features that do not yet exist in the market. However, parameters estimated from experimental data often show marked inconsistencies with those inferred from the market, reducing their usefulness in forecasting and decision making. We propose an approach for combining choice-based conjoint data with individual-level purchase data to produce estimates that are more consistent with the market. Unlike prior approaches for calibrating conjoint models so that they correctly predict aggregate market shares for a baseline market, the proposed approach is designed to produce parameters that are more consistent with those that can be inferred from individual-level market data. The proposed method relies on a new general framework for combining two or more sources of individual-level choice data to estimate a hierarchical discrete choice model. Past approaches to combining choice data assume that the population mean for the parameters is the same across both data sets and require that data sets are sampled from the same population. In contrast, we incorporate in the model individual characteristic variables, and assert only that the mapping between individuals' characteristics and their preferences is the same across the data sets. This allows the model to be applied even if the sample of individuals observed in each data set is not representative of the population as a whole, so long as appropriate product-use variables are collected that can explain the systematic deviations between them. The framework also explicitly incorporates a model for the individual characteristics, which allows us to use Bayesian missing-data techniques to handle the situation where each data set contains different demographic variables. This makes the method useful in practice for a wide range of existing market and conjoint data sets. We apply the method to a set of conjoint and market data for minivan choice and find that the proposed method predicts holdout market choices better than a model estimated from conjoint data alone or a model that does not include demographic variables.