For managers, setting an advertising budget is often both a science and an art. Even in the digital age where advertising effectiveness can be measured with greater precision than ever before, managers frequently struggle to determine an optimal advertising budget. However, insights can be gleaned from companies’ annual CMO surveys, such as IBM and Deloitte, which provide information about how managers use combinations of analytical and heuristic rules in their budgeting processes. But which of these combinations works best? New research by Kolsarici, Vakratsas, and Naik (2020) addresses this question and offers a framework to enhance our understanding of complex advertising budget decisions.
Kolsarici and colleagues’ framework shows how ad effectiveness and its uncertainty vary over time and allows managers to decompose advertising budgets into two categories of commonly used rules: analytical and heuristic rules. Analytical rules focus on maximizing profit and include baseline advertising spending and adaptive experimentation. Specifically, baseline advertising is the optimal advertising level, which is contingent on a manager’s knowledge of advertising effectiveness and complete certainty of its effectiveness. Since ad effectiveness is sometimes unknown, managers may use adaptive experimentation by conducting experiments through intentional manipulation of advertising levels to learn about ad effectiveness and the time-varying uncertainty in advertising effectiveness.
Heuristics, on the other hand, involve managerial judgment and past performance to determine advertising budgets. Managers commonly employ heuristics, such as advertising-to-sales ratio (A/S ratio) and competitive parity, when making advertising budget decisions. The A/S ratio is considered a “responsive” budgeting rule, as ad budgets are based on either past sales or projected future sales. Thus, when managers utilize this rule, they set their advertising budget based on previous performance and fail to consider the competitive environment. Competitive parity, the other heuristic-based rule, involves setting the ad budget using competition-based metrics and commonly takes one of two forms – matching a competitor’s ad budget or setting share-of-voice equal to share-of-market.
Using the “multiple stakes” paradigm, Kolsarici et al. formulate a dynamic model of advertising spending which allows for a combination of the above analytical and heuristic-based rules. Their model was tested on eight brands from three categories (i.e., hybrid cars, beer, and yogurt) in the U.S. market. These product categories were chosen because they vary among the following criteria: product types (i.e., durable for hybrid cars and nondurable for consumer goods), market maturity (i.e., new markets for hybrids and established ones for consumer goods), level of fragmentation (i.e., consolidated for hybrids, medium fragmentation for yogurt, and high for beer), and frequency of purchase. Therefore, their framework should be applicable to various industries and brands.
Kolsarici, Vakratsas, and Naik’s study reveals that ad budgeting is a complex process, requiring managers to make decisions under uncertainty of ad effectiveness. For example, managers of hybrid car brands put more emphasis on adaptive experimentation because the hybrid car market is an emerging market, and managers have limited knowledge of consumer response to new technology and brand advertising. Yogurt brands use almost exclusively heuristics-based methods and maintain a similar balance between A/S ratio and competitive parity heuristics. Finally, beer brands use different combinations of rules in their approaches: the first brand relies mainly on baseline spending, the second brand uses a balance of analytic and heuristics methods, and the remaining brands rely on different combinations of heuristics (i.e., A/S, competitive parity).
The authors’ framework can be utilized to generate new insights for firms’ internal audits of their own brands and for business intelligence on competing brands. An important takeaway from their research is that budgeting decisions are brand-specific, and managers should try to understand how market-related conditions (e.g., market leadership, market maturity, category growth) influence advertising budget approaches. Adaptive experimentation, a technique that departs from profit maximization, is not a suboptimal decision and should be considered when greater uncertainty in ad effectiveness exists. Thus, larger budgets for experimentation should be considered when there is more uncertainty in ad effectiveness (i.e., when entering new markets).
As more consumers are utilizing social media platforms, such as Facebook, WeChat, and YouTube, future research could examine whether the authors’ framework extends to channel-specific budgets. When we posed this question to the authors, they expected that managers would do more experimentation for channels that have higher uncertainty in ad effectiveness and stated that their developed framework is flexible and can be tailored to gain insights from such platforms by using data at a more granular level and accounting for the relatively short consumer reaction times.
Finally, as the world is currently experiencing the global effects of COVID-19, there are many changes to market environments that may affect managers’ knowledge of their environment and the degree of certainty of advertising effectiveness. Many companies have already altered their ad content and messaging to acknowledge COVID-19 and its effects on the industries and the world, but it will take time for companies to learn the effects of their ad efforts. Future research could examine how COVID-19 changes the advertising budget process and whether these changes are temporary or become the new normal.
Read the full article:
Kolsarici, Ceren, Demetrios Vakratsas, and Prasad A. Naik (2020), “The Anatomy of the Advertising Budget Decision: How Analytics and Heuristics Drive Sales Performance,” Journal of Marketing Research, 57(3), 468–88.