Working with Panel Data: Extracting Value from Multiple Customer Observations

Many retail and consumer packaged goods (CPG) companies are now keeping track of what their customers purchased in the past, often through some form of loyalty program. This record keeping is one example of how modern corporations are building data sets that have a panel structure, a data structure that is also pervasive in insurance and finance organizations. Panel data (sometimes called longitudinal data) can be thought of as the joining of cross-sectional and time series data. Panel data enable analysts to control for factors that cannot be considered by simple cross-sectional regression models that ignore the time dimension. These factors, which are unobserved by the modeler, might bias regression coefficients if they are ignored. This paper compares several methods of working with panel data in the PANEL procedure and discusses how you might benefit from using multiple observations for each customer. Sample code is available.