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"Norris, Bruce"
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Communicating Brands in Television Advertising
Many studies have quantified the effects of TV ad spendingor gross rating points on brand sales. Yet this effect is likelymoderated by the different types of brand-related messages or cues (e.g., logo, brand attributes) embedded in the ads and by the ways (e.g., explicitly or implicitly) these cues are conveyed to TV audiences. The authors thusmeasure 17 cues often usedwithin ads to build brand awareness (or salience) and brand image and investigate their influence on ad effectiveness. Technically, the study builds a dynamic model to quantify the effects of advertising on sales; builds a robust and interpretable (i.e., nonparametric and sparse) factor model that integrates correlated, left-censored branding cues; and thenmodels the effects of advertising as a function of the factors identified by these cues. An analysis of 177 campaigns aired by 62 brands finds that salience cues (e.g., logo) and benefit and attributemessagesmoderate ad effectiveness. It also finds that explicit cues aremore effective than implicitones; nonetheless, the primary drivers of ad effectiveness are visual salience cues: the duration and frequencywith which the logo and the duration with which the product are displayed. The study can thus suggest ways brand and ad agency managers can improve the effects of creative ad content on sales.
Journal Article
Bayesian Nonparametric Dynamic Methods
Bayesian methods for dynamic models in marketing have so far been parametric. For instance, it is invariably assumed that model errors emerge from normal distributions. Yet using arbitrary distributional assumptions can result in false inference, which in turn misleads managers. The author therefore presents a set of flexible Bayesian nonparametric (NP) dynamic models that treat error densities as unknown but assume that they emerge from Dirichlet process mixtures. Although the methods address misspecification in dynamic linear models, the main innovation is a particle filter algorithm for nonlinear state-space models. The author used two advertising studies to confirm the benefits of the methods when strict error assumptions are untenable. In both studies, NP models markedly outperformed benchmarks in terms of fit and forecast results. In the first study, the benchmarks understated the effects of competitive advertising on own brand awareness. In the second study, the benchmark inflated ad quality, and consequently, the effects of past advertising appeared 36% higher than that predicted by the NP model. In general, these methods should be valuable wherever state-space models appear (e.g., brand and advertising dynamics, diffusion of innovation, dynamic discrete choice).
Journal Article
Discovering heterogeneous consumer journeys in online platforms: implications for networking investment
We model consumer journeys for user-created programs published in an online programming platform (OPP) and uncover factors that predict their occurrence. We build our model on a theoretical framework where consumer journeys involve three latent stages (Learn, Feel, Do), in which users gather information about, express fondness toward, and try the published items, respectively. Using a dataset from an OPP where users publish multimedia items and follow other users, we find that there is no one dominant consumer journey; instead, the sequences of stages in a journey (e.g., Learn → Feel → Do) vary across published items. Furthermore, we find that the social capital (i.e., social network) of a publisher influences the occurrence of spillover effects between latent stages (the phenomenon that one stage in a period triggers another stage in the next period) for the items posted by the publisher. We also find that a publisher’s social capital has only a transient impact on the consumer journeys for the publisher’s projects, underlining the importance of consistently making new network connections in order to promote the growth of user activities surrounding the publisher’s projects. We apply our findings to the publishers’ networking investment decisions to show that publishers’ networking investment would be severely suboptimal if journey heterogeneity is not considered.
Journal Article
Hydrate Formation from Joule Thomson Expansion Using a Single Pass Flowloop
Hydrate risk management is critically important for an energy industry that continues to see increasing demand. Hydrate formation in production lines is a potential threat under low temperature and high-pressure conditions where water and light gas molecules are present. Here, we introduce a 1-inch OD single-pass flow loop and demonstrate the Joule-Thomson (JT) expansion of a methane-ethane mixture. Initially, dry gas flowed through the apparatus at a variable pressure-differential. Larger pressure differentials resulted in more cooling, as predicted by standard thermodynamic models. A systematic deviation noted at higher pressure differentials was partially rectified through corrections incorporating heat transfer, thermal mass and kinetic energy effects. A wet gas system was then investigated with varying degrees of water injection. At the lowest rate, hydrate plugging occurred close to the expansion point and faster than for higher injection rates. This immediate and severe hydrate plugging has important implications for the design of safety relief systems in particular. Furthermore, this rate of plugging could not be predicted by existing software tools, suggesting that the atomization of liquids over an expansion valve is a critical missing component that must be incorporated for accurate predictions of hydrate plug formation severity.
Journal Article
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 retailer's media schedules.
Journal Article
Dynamic Effectiveness of Advertising and Word of Mouth in Sequential Distribution of New Products
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.
Journal Article
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 brand's 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 advertising's dual contributions of building brands and growing sales.
Journal Article
Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship
Models of advertising response implicitly assume that the entire advertising budget is spent on disseminating one message. In practice, managers use different themes of advertising (for example, price advertisements versus product advertisements) and within each theme they employ different versions of an advertisement. In this study, we evaluate the dynamic effects of different themes of advertising that have been employed in a campaign. We develop a model that jointly considers the effects of wearout as well as that of forgetting in the context of an advertising campaign that employs five different advertising themes. We quantify the differential wearout effects across the different themes of advertising and examine the interaction effects between the different themes using a Bayesian dynamic linear model (DLM). Such a response model can help managers decide on the optimal allocation of resources across the portfolio of ads as well as better manage their scheduling. We develop a model to show how our response model parameters can be used to improve the effectiveness of advertising budget allocation across different themes. We find that a reallocation of resources across different themes according to our model results in a significant improvement in demand.
Journal Article
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.
Journal Article