We examine the situation where hourly data are available to design advertising-response models, whereas managerial decision making can concern hourly, daily or weekly intervals. The key question is how models for hourly data compare to models based on weekly data with respect to forecasting accuracy and with respect to assessing advertising impact. Simulation experiments suggest that the strategy, which entails modeling the least aggregated data and forecasting more aggregate data, yields better forecasts, provided that one has a correct model specification for the higher frequency data. A detailed analysis of three actual data sets confirms this conclusion. A key feature of this confirmation is that aggregation affects data transformation to dampen the variance. The estimated advertising impact is sensitive to the appropriate transformation. Our conclusion is that disaggregated models are preferable also when decision have to be made at lower frequencies.

Additional Metadata
Keywords advertising effectiveness, advertising response, aggregation, normative and predictive validity
JEL Statistical Decision Theory; Operations Research (jel C44), Business Administration and Business Economics; Marketing; Accounting (jel M), Marketing (jel M31)
Publisher Erasmus Research Institute of Management
Persistent URL hdl.handle.net/1765/22614
Series ERIM Report Series Research in Management
Journal ERIM report series research in management Erasmus Research Institute of Management
Citation
Kiygi Calli, M, Weverbergh, M, & Franses, Ph.H.B.F. (2010). To Aggregate or Not to Aggregate: Should decisions and models have the same frequency? (No. ERS-2010-046-MKT). ERIM report series research in management Erasmus Research Institute of Management. Erasmus Research Institute of Management. Retrieved from http://hdl.handle.net/1765/22614