Norris Bruce

Discovering How Advertising Grows Sales and Builds Brands

Advertising nudges consumers along the think–feel–do hierarchy of intermediate effects of advertising to induce sales. Because intermediate effects—cognition, affect, and experience—are unobservable constructs, brand managers use a battery of mind-set metrics to assess how advertising builds brands. However, extant sales response models explain how advertising grows sales but ignore the role of intermediate effects in building brands. To link these dual contributions of advertising, the authors propose an integrated framework that augments the dynamic advertising–sales response model by integrating the hierarchy, dynamic evolution, and purchase reinforcement of intermediate effects. Methodologically, the new approach incorporates the intermediate effects as factors from mind-set metrics while filtering out measurement noise, extracts the factor loadings, estimates the dynamic evolution of the factors, and infers their sequence in any hypothesized hierarchy by embedding their impact in a dynamic advertising–sales response model. The authors apply the proposed model and associated method to a major brand to discover the brands operating hierarchy (advertising → experience → cognition → affect ↔ sales). The results provide the first empirical evidence that intermediate effects are indeed dynamic constructs, that purchase reinforcement effects exist not only for experience but also for other intermediate effects, and that advertising simultaneously contributes to both sales growth and brand building. Thus, both researchers and managers should consider using the proposed framework to capture advertisings dual contributions of building brands and growing sales.

The Better They Are, the More They Give: Trade Promotions of Consumer Durables

The authors study trade promotions for durable goods, such as automobiles, for which manufacturers provide special incentives to dealers for exceeding specific sales targets. They develop a theoretical model of consumer, retailer, and manufacturer behavior based on observations about key aspects of the automobile market. The analysis provides important insights about the intertemporal effects of trade promotions and the effect of product durability on the promotion strategies of manufacturers. For example, manufacturers of more durable products benefit more from running trade promotions and give deeper discounts. The authors find empirical support when they test the theoretical results.

Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters

Firms often use a pool or series of advertising themes in their campaigns. Thus, for example, a firm may employ some of its advertising to promote price-related themes or messages and other of its advertising to promote product-related themes. This study examines the interdependence that can occur between pairs of themes in a pool i.e., pooling effects, the impact of these pooling effects on the allocation of advertising expenditures, and the factors that can affect forgetting rates or, conversely, carry-over rates in a multitheme advertising environment. The study measures pooling, wear out, and forgetting carry-over effects for a campaign that uses five different advertising themes. To obtain these measures, I extend the linear Nerlove-Arrow NA 1962 model to a nonlinear model of advertising theme quality and goodwill and estimate the extended model using Markov chain Monte Carlo MCMC and particle filtering ideas. Particle filtering belongs to a class of sequential Monte Carlo SMC methods designed to estimate nonlinear/nonnormal state space models. Results show that forgetting or carry-over rates may be time varying and a function of prior goodwill past advertising and other advertising variables. Results show, moreover, that pooling effects can reduce theme wear out and, in turn, significantly improve advertising efficiency.

A Dynamic Model for Digital Advertising: The Effects of Creative Format, Message Content, and Targeting on Engagement

The authors study the joint effects of creative format, message content, and targeting on the performance of digital ads over time. Specifically, they present a dynamic model to measure the effects of various sizes of static (GIF) and animated (Flash) display ad formats and consider whether different ad contents, related to the brand or a price offer, are more or less effective for different ad formats and targeted or retargeted customer segments. To this end, the authors obtain six months of data on daily impressions, clicks, targeting, and ad creative content from a major U.S. retailer, and they develop a dynamic zero-inflated count model. Given the sparse, nonlinear, and non-Gaussian nature of the data, the study designs a particle filter/Markov chain Monte Carlo scheme for estimation. Results show that carry-over rates for dynamic formats are greater than those for static formats; however, static formats can still be effective for price ads and retargeting. Most notably, results also show that retargeted ads are effective only if they offer price incentives. The study then considers the import of these results for the retailers media schedules.

Social Learning and Diffusion of Pervasive Goods: An Empirical Study of an African App Store

In this study, the authors develop a structural model that combines a macro diffusion model with a micro choice model to control for the effect of social influence on the mobile app choices of customers over app stores. Social influence refers to the density of adopters within the proximity of other customers. Using a large data set from an African app store and Bayesian estimation methods, the authors quantify the effect of social influence and investigate the impact of ignoring this process in estimating customer choices. The findings show that customer choices in the app store are explained better by offline than online density of adopters and that ignoring social influence in estimations results in biased estimates. Furthermore, the findings show that the mobile app adoption process is similar to adoption of music CDs, among all other classic economy goods. A counterfactual analysis shows that the app store can increase its revenue by 13.6% through a viral marketing policy (e.g., a sharing with friends and family button).