Enriching Scanner Panel Models with Choice Experiments

Abstract

This research examines the methods, viability, and benefits of pooling scanner panel choice data with compatible preference data from designed choice experiments. The fact that different choice data sources have diverse strengths and weaknesses suggests it might be possible to pool multiple sources to achieve improved models, due to offsetting advantages and disadvantages. For example, new attributes and attribute levels not included in the scanner panel data can be introduced via the choice experiment, while the scanner panel data captures preference dynamics, which is, at best, difficult with experimental data. Our application, involving liquid laundry detergent, establishes the feasibility and desirability of doing such augmentations of scanner panel data: The joint scanner panel/choice experiment model has significantly better prediction performance on a holdout data set than does a pure scanner panel model. Thus, we extend the concept of choice data enrichment into another domain and demonstrate that data enrichment can add significantly to one's understanding of preferences reflected in scanner panel data.