Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We examine the situation in which hourly data are available for designing advertising-response models, whereas managerial decision-making can concern hourly, daily or weekly intervals. A key notion is that models for higher frequency data require the intra-seasonal heterogeneity to be addressed, while models for lower frequency data are much simpler. We use three large, actual real-life datasets to analyze the relevance of these additional efforts for managerial interpretation and for the out-of-sample forecast accuracy at various frequencies.