Prof. Dr. Dr. h.c. Sönke Albers

To optimally set marketing communication (“marcom”) budgets, reliable estimates of short-term elasticities and carryover effects are required. Empirical generalizations from meta-analyses of prior field studies can help guide these decisions. However, the last such meta-analysis of marcom carryover effects was performed on Koyck model–based estimates collected before 1984 and was confined to mass media advertising. The authors update and extend extant empirical generalizations via two meta-analyses of carryover estimates compiled from studies encompassing personal selling, targeted advertising, and mass media advertising, using diverse model forms, until 2015. The first is focused on and utilizes 918 estimates of the carryover proportion of the total effect, termed long-term share of the total effect, and the second focuses on 863 derivable estimates of 90% implied duration intervals. The authors find the mean long-term shares of the total effect for personal selling (.687) and targeted advertising (.650) are distinctly larger than that for mass media advertising (.523) and the corresponding median 90% implied duration intervals are 12.6, 2, and 3.4 months, respectively. The authors conclude by discussing differences by model type and the implications for marcom budget-setting and analyses.

Previous research on marketing budget decisions has shown that profit improvement from better allocation across products or regions is much higher than from improving the overall budget. However, despite its high managerial relevance, contributions by marketing scholars are rare.

In this paper, we introduce an innovative and feasible solution to the dynamic marketing budget allocation problem for multiproduct, multicountry firms. Specifically, our decision support model allows determining near-optimal marketing budgets at the country--product--marketing--activity level in an Excel-supported environment each year. The model accounts for marketing dynamics and a product's growth potential as well as for trade-offs with respect to marketing effectiveness and profit contribution. The model has been successfully implemented at Bayer, one the world's largest pharmaceutical and chemical firms. The profit improvement potential is more than 50% and worth nearly €500 million in incremental discounted cash flows.

This article presents a meta-analysis of prior econometric estimates of personal selling elasticity—that is, the ratio of the percentage change in an objective, ratio-scaled measure of sales output (e.g., dollar or unit purchases) to the corresponding percentage change in an objective, ratio-scaled measure of personal selling input (e.g., dollar expenditures). The authors conduct a meta-analysis of 506 personal selling elasticity estimates drawn from analyses of 88 empirical data sets across 75 previous articles. They find a mean estimate of current-period personal selling elasticity of .34. They also find that elasticity estimates are higher for early life-cycle-stage offerings, higher from studies set in Europe than from those set in the United States, and smaller in more recent years. In addition, elasticity estimates are affected significantly by analysts' use of relative rather than absolute sales output measures, by cross-sectional rather than panel data, by omission of promotions, by lagged effects, by marketing interaction effects, and by the neglect of endogeneity in model estimation. The method bias–corrected mean personal selling elasticity is approximately .31. The authors discuss the implications of their results for sales managers and researchers.

The authors analyze primary demand effects of marketing efforts directed at the physician (detailing and professional journal advertising) versus marketing efforts directed at the patient (direct-to-consumer advertising). The analysis covers 86 categories, or approximately 85% of the U.S. pharmaceutical market, during the 2001–2005 period. Primary demand effects are rather small, in contrast with the estimated sales effects for individual brands. By using a new brand-level method to estimate primary demand effects with aggregate data, the authors show that the small effects are due to intense competitive interactions during the observation period but not necessarily to low primary demand responsiveness. In contrast with previous studies, the authors also find that detailing is more effective in driving primary demand than direct-to-consumer advertising. A category sales model cannot provide such insights. In addition, a category sales model likely produces biased predictions about period-by-period changes in primary demand. The suggested brand-level method does not suffer from these limitations.