Modeling Co-Existing Business Scenarios with Time Series Panel Data

Abstract

Due to customer segmentation, multiple types of dynamic business scenarios (business-as-usual, escalation, hysteresis, and evolving business practice; Dekimpe and Hanssens 1999) may co-exist within a single product market. The authors develop an approach to model this phenomenon with time series panel data. Unit-root tests are used to group panelists by whether or not outcome (e.g., sales) and marketing activity (e.g., advertising, promotion) variables are stationary or evolving. This produces four clusters corresponding to each business scenario. Next, panel-data vector autoregressive models appropriate for each panelist cluster are estimated to assess the dynamics and the magnitude of the response to marketing effort. The approach is applied to physician panel data on drug prescriptions and direct-to-physician promotions. Estimation results show markedly different response dynamics (as captured by impulse response functions) and elasticities across the physician groups. The approach also produces better in-sample and holdout fits than pooled data models. For firms that track customer-level marketing activity and response over time, a segmentation based on dynamic business scenarios provides a new tool for targeting and efficient marketing resource allocation.