Enriching Scanner Panel Models with Choice Experiments

Authors:
Joffre Swait;Rick L. Andrews
Affiliations:
-;-
Venue:
Marketing Science
Year:
2003

Citing 0
Cited 8

Invited Commentary---Structural Modeling in Marketing: Some Future Possibilities

Marketing Science
How Dynamic Consumer Response, Competitor Response, Company Support, and Company Inertia Shape Long-Term Marketing Effectiveness

Marketing Science
The Impact of Utility Balance and Endogeneity in Conjoint Analysis

Marketing Science
Handling class imbalance in customer churn prediction

Expert Systems with Applications: An International Journal
Commentary---Discussion of “Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test”

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

Management Science
Investigating the Drivers of Consumer Cross-Category Learning for New Products Using Multiple Data Sets

Marketing Science
Optimizing Retail Assortments

Marketing Science

Quantified Score

Hi-index	0.00

Visualization

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 choicedata enrichment into another domain and demonstrate that data enrichment can add significantly to one's understanding of preferences reflected in scanner panel data.