Mitchell J. Lovett

On Brands and Word-of-Mouth

Brands and word of mouth (WOM) are cornerstones of the marketing field, and yet their relationship has received relatively little attention. This study aims to enhance understanding of brand characteristics as antecedents of WOM by executing a comprehensive empirical analysis. For this purpose, the authors constructed a unique data set on online and offline WOM and characteristics for more than 600 of the most talked-about U.S. brands. To guide this empirical analysis, they present a theoretical framework arguing that consumers spread WOM on brands as a result of social, emotional, and functional drivers. Using these drivers, the authors identify a set of 13 brand characteristics that stimulate WOM, including three (level of differentiation, excitement, and complexity) that have not been studied to date as WOM antecedents. The authors find that whereas the social and functional drivers are the most important for online WOM, the emotional driver is the most important for offline WOM. These results provide an insightful perspective on WOM and have meaningful managerial implications for brand management and investment in WOM campaigns.

The Role of Paid, Earned, and Owned Media in Building Entertainment Brands: Reminding, Informing, and Enhancing Enjoyment

We study three ways firms can communicate about their brands—paid media (advertising), earned media (word of mouth and online social media), and owned media (brand websites and other owned content)—and the roles these media types play in reminding (i.e., activating memory), informing (i.e., learning their tastes for the brand), or enhancing enjoyment (e.g., gaining additional utility from socializing about the brand). We do this for a new TV show setting using a data set that contains reported viewing, exposures, expectations, and experiences. We present descriptive analyses and results from a new structural model, which indicate that earned media is more impactful than paid and owned media per exposure. However, paid media has far more exposures, so for a given percentage increase, paid media’s influence dominates earned and owned media. Earned media operate primarily through enhancing enjoyment, whereas paid media operate through reminding and owned media through reminding, but discourage live viewing. We find that media exposures help consumers learn about how well they will like the program. However, this learning can either increase or decrease the expected liking, and in our data the average audience effects are negligible. Overall, we find that earned and paid media play a central role in developing and maintaining entertainment brands.

Targeting Political Advertising on Television

We study the targeting of political advertising by congressional candidates on television. Targeting strategies for television dier from targeting strategies for direct mail advertising or get out the vote eorts because candidates cannot target voters individually. Instead, candidates must target television programs with viewers most similar to the desired target voters. Thus, for targeted advertising to have value, the audiences for television programs must dier in mean

Optimal Admission and Scholarship Decisions: Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers

Each year in the postsecondary education industry, schools offer admission to nearly 3 million new students and scholarships totaling nearly

Choosing Customized Marketing Offers to Attract a Desirable Mix of Customers

100 billion. This is a large, understudied targeted marketing and price discrimination problem. This problem falls into a broader class of configuration utility problems (CUPs), which typically require an approach tailored to exploit the particular setting. This paper provides such an approach for the admission and scholarship decisions problem. The approach accounts for the key distinguishing feature of this industry---schools value the average features of the matriculating students such as percent female, percent from different regions of the world, average test scores, and average grade point average. Thus, as in any CUP, the value of one object (i.e., student) cannot be separated from the composition of all of the objects (other students in the enrolling class). This goal of achieving a class with a desirable set of average characteristics greatly complicates the optimization problem and does not allow the application of standard approaches. We develop a new approach that solves this more complex optimization problem using an empirical system to estimate each students choice and the focal schools utility function. We test the approach in a field study of an MBA scholarship process and implement adjusted scholarship decisions. Using a holdout sample, we provide evidence that the methodology can lead to improvements over current management decisions. Finally, by comparing our solution to what management would do on its own, we provide insight into how to improve management decisions in this setting.

Marketing and Politics: Models, Behavior, and Policy Implications

This paper studies the marketing problem of a firm trying to obtain an attractive mix of customers by making customized offers. The firm values the average features of the acquired set of customers, makes decisions with uncertainty about whether and which customers will accept offers, is risk averse, and faces a resource constraint. These features do not allow the application of standard procedures, so we develop methods tailored to the stochastic nature of the problem in order to be computationally feasible. These methods include calculating probabilistic guarantees for the approximate utility function and bounds on the optimal value. Our approach overcomes both the Optimizers Curse and the computational difficulty of enumeration techniques for this problem. We apply the approach to an MBA admission process and implement adjusted scholarship decisions. We demonstrate the methodology can generate statistically significant improvements over current management decisions. By comparing our solution to what management would do on its own, we provide insight into the nature of mistakes management made in this complex decision environment. We believe that the methodology proposed here, i.e., the model, the probabilistic guarantees, and the computational approach, will prove to be valuable in a variety of different applications.