Firms in many industries release new products in sequential stages. They also launch separate advertising campaigns at each distribution stage. Thus, communication mix elements—advertising and word of mouth (WOM)—can play important, distinct, and yet interdependent roles in stimulating new product demand. Their effectiveness may fluctuate within and across stages and spill over from earlier to later stages. Thus, the authors construct a dynamic linear model to study the dynamic effects of advertising and WOM on demand for heterogeneous products across stages. They further apply the model to examine a canonical example, the theater-then-video sequential distribution of motion pictures, and estimate the parameters using Kalman filtering/smoothing and Markov chain Monte Carlo methods. The results show that advertising and WOM exert dynamic, yet diverse, influences on demand for new products. For example, while increased ad spending is more effective at an earlier stage due to repetition wear-in and synergy with WOM, increased WOM activities at a later stage could become more powerful in driving demand. Subsequent optimization exercises suggest that films of varied characteristics can potentially re-allocate their advertising budgets and reap additional revenues.

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The authors examine the dynamic effects of category- and brand-level advertising for a new pharmaceutical in a market in which regulations require that the content of these two types of advertising be mutually exclusive. Specifically, category, or generic, messages should communicate information only about the disease without promoting any brand, whereas brand-level messages should be void of any therapeutic information. This brings up two questions of great managerial importance: Which type of message is generally more effective (category or brand level), and when is one type more effective than the other? The authors pursue these questions by analyzing the effects of advertising on new and refill prescriptions through the use of an augmented Kalman filter with continuous state and discrete observations. The findings suggest the presence of complex dynamics for both types of regulation-induced advertising messages. In general, brand advertising is more effective, especially after competitive entry. Extensive validation tests confirm the superiority of the modeling approach. The authors discuss implications for managers and regulators.

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.

A daptive estimation methods have become a popular tool for capturing and forecasting changing conditions in dynamic environments. Although adaptive models can provide superior one-step-ahead forecasts, their application to multiperiod forecasting is challenging when the underlying parameter variation process is not correctly specified. The authors propose a methodology based on the Chebyshev approximation method (CAM), which provides a parsimonious substitute for the measurement updating process in the forecasting period, to help forecasters improve multiperiod accuracy in the case of parameter variation misspecification. In two empirical applications concerning the sales growth of new brands, CAM exhibits superior forecasting performance compared to a variety of benchmarks. CAM's properties are further explored through extensive simulations, which suggest that the proposed method is more likely to increase forecast accuracy when parameter variation is more systematic but misspecified because of uncertainty regarding its exact functional form.