Product Launches with New Attributes : A Hybrid Conjoint-Consumer Panel Technique for Estimating Demand

We propose and empirically evaluate a new hybrid estimation approach that integrates choice-based conjoint with repeated purchase data for a dense consumer panel, and show that it increases the accuracy of conjoint predictions for actual purchases observed months later. Our key innovation lies in combining conjoint data with a long and detailed panel of actual choices, both before and after the product line introduction, for both survey respondents and a random sample of the target population. By linking the actual purchase and conjoint data, we can estimate preferences for attributes not yet present in the marketplace, while also addressing many of the key limitations of conjoint analysis, including sample selection and contextual differences. A counterfactual product and pricing exercise illustrates its managerial relevance