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The authors investigate how heuristics and analytics contribute to the advertising budget decision by decomposing it into four components: (1) baseline spending, (2) adaptive experimentation, (3) advertising-to-sales ratio, and (4) competitive parity. They propose a methodology to estimate and infer the weights of these four components. Applying this methodology to sales and advertising data across eight brands from three categories substantiates for the first time, and uniformly across all brands, that managers depart from optimality through adaptive experimentation, which is in line with dual control theory that suggests they do so to learn about advertising effectiveness. The adaptive experimentation finding, combined with evidence on the use of heuristic methods, suggests that budget decision making is characterized by bounded rationality. Furthermore, budgeting decisions are brand-specific, reflecting the considerations of a brand’s market position and performance. Finally, simulation studies show that brands from categories with high uncertainty in advertising effectiveness can benefit from double-digit revenue lifts by placing higher emphasis on adaptive experimentation.
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Kolsarici et al. (2020) studied this question.
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