Dynamic Multilevel Covariance Structure Models for Analyzing Rolling Cross-Sectional Tracking Surveys
40 Pages Posted: 22 Feb 2018
Date Written: Fall 2017
Appendix is available at: https://ssrn.com/abstract=3127752
Modeling customer satisfaction tracking data presents some unique challenges. Customer satisfaction tracking data are neither strictly cross-sectional nor strictly longitudinal in a panel sense. We refer to such data as nested repeated (rolling) cross-sectional, reflecting the fact that respondents are nested within brands and that the data are composed of a series of repeated cross-sections. In this study, we develop a new methodology that extends multilevel modeling techniques by considering both temporal variation and individual-level heterogeneity simultaneously in the presence of nested repeated cross-sectional samples. Our modeling approach essentially uses a Dynamic Linear Model (DLM) form and a multilevel structural equation form to “marry” the time series and cross-sectional sources of variation. Our empirical analysis explores the long-term effects of overall customer satisfaction and feature-level satisfaction on brand sales and the salutary benefits, if any, of controlling for individual-level response heterogeneity.
Keywords: Satisfaction Tracking, Performance Metrics, Dynamic Linear Model, Hierarchical Time-Series Cross-Sectional Data
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