Research Seminars

Homophily and Influence: Pricing to Harness Word-of-Mouth on Social Networks

Marketing

Speaker: Peter Zubcsek
Assistant Professor of Marketing , University of Florida

27 November 2015 - Building T, Room T201 - From 1:30 pm to 3:00 pm


Large-scale social platforms have enabled marketers to obtain rich data on the structure of word-of-mouth (WOM) networks and the correlation of friends’ preferences (network assortativity). We study how the similarity or difference of friends’ reservation prices for a product should affect the optimal price and advertising levels for that product. To this end, we build an analytical model of informative advertising and pricing over a social network. Connections between consumers are added in a way that allows neighbors’ preferences to be positively or negatively correlated, thereby introducing homophily or heterophily in the model. Consumers may learn about products either directly via advertising, or via WOM spread by their peers who have adopted a product. We find that in the typical scenario when blanket advertising is not affordable, firms set a price lower than the naïve optimum in order to leverage the social value of more price-sensitive customers. We also characterize the relationship between assortativity and the marketing instruments (price and advertising) of the firm, to find that either instrument may be substitutes or complements with assortativity depending on the cost of advertising relative to the market’s valuation for the product, the overall connectivity, and the assortativity of the network.

The Halo Effect of Product Color Brightness on Hedonic Food Consumption

Marketing

Speaker: Suresh Ramanathan
Marketing Professor , Mays Business School, Texas A&M University

5 November 2015 - Room T015 - From 1:30 pm to 3:00 pm


The Halo Effect of Product Color Brightness on Hedonic Food Consumption

We present an in-depth exploration of consumers’ evaluative and behavioral responses to foods that vary in color brightness, defined as the degree of lightness and darkness of the color. Over a series of five studies, we present evidence that the color brightness of a food serves as an automatic evaluative cue about its taste and healthiness that ultimately biases the volume of food consumed. We identify food type (hedonic vs. healthy) as a boundary condition for this effect and we find that light colored hedonic but not healthy foods are seen as healthier and tastier, and are consumed more than dark colored hedonic foods. We show that light colored hedonic foods evoke lower decision conflict compared to dark colored hedonic foods because they are enveloped by a positive halo of chronically accessible and automatically activated taste and health associations.

Elimination by Aspects as a Logit Model

Marketing

Speaker: Kamel Jedidi
Marketing Professor , Columbia Business School

13 October 2015 - Room T015 - From 1:30 pm to 3:00 pm


Elimination by Aspects as a Logit Model

Rajeev Kohli & Kamel Jedidi Graduate School of Business, Columbia University,
New York, NY 10027

Elimination by aspects (EBA) is a random utility model that is considered to represent the choice process used by consumers more faithfully than logit and probit models. One limitation of the model is that it does not have a known error theory. We show that EBA can be derived by assuming that aspects have random utilities with independent, extreme value distributions and that it generalizes the multinomial logit and rank-ordered logit models. We also show that it generalizes nested logit and cross-nested logit models, which are equivalent to a special case of EBA called preference trees. If the utilities of the alternatives are functions of covariates, then (1) a nested logit model correspond to a preference tree in which the aspect utilities are functions of the covariates, and (2) a preference tree correspond to a nested logit model in which the nesting parameters and inclusive values are functions of the covariates. We estimate and compare the fits and predictions of the latter two models for a problem concerning transportation choices by travelers in Australia.

Marketing Experiments: Using experimental methods to assess marketing tactics - One-day workshop

Marketing

Speaker: Elea McDonnell Feit
Marketing Professor , LeBow College of Business - Drexel University

18 September 2015 - Building T, room T307 - From 8:30 am to 6:00 pm


Marketing Experiments: Using experimental methods to assess marketing tactics

In the past decade, the shift of advertising dollars to measurable digital marketing channels has suddenly made experiments an economically feasible way to inform marketing decisions such as how advertising should be designed and targeted, what types of promotions are most effective, what products should be offered, how sales staff should be compensated, which sales channels should be emphasized, etc. Many marketers engaged in online retailing, direct marketing, online advertising, media management, and service operations are rapidly embracing a “test and learn” philosophy and a number of platforms such as Adobe Target, Optimizelyi, APT and Google Content Experiments, have been developed to facilitate rigorous field experiments in the online environment. The rapid rise of the “test and learn” philosophy in marketing has created a huge demand for those who can design, field, and analyze experiments. Through this course, you will learn about, discuss and practice a wide range of critical skills for experimentation, from the statistical methods used to design and analyze experiments to the management and strategy required to execute an experiment and act on the results. Although our cases and examples will focus on marketing problems, the material covered can be applied in a number of other domains particularly operations, management and product design.

Measuring Multi-Channel Advertising Response

Marketing

Speaker: Elea McDonnell Feit
Marketing Professor , Drexel University - LeBow College of Business

17 September 2015 - Building T – Room T203 - From 10:30 am to 12:00 pm


Measuring Multi-Channel Advertising Response

Advances in data collection have made it increasingly easy to collect data on consumer-level purchases that are linked to those same customers’ advertising exposures. However, advances in advertising response modeling (i.e., marketing mix modeling) have lagged behind the availability of this granular data. Extending extant models to multi-channel consumer-level data, we develop a Bayesian Tobit model that can be used to measure the effectiveness of advertising exposures. Building on the traditional ad-stock framework, we are able to differentially determine separate decay rates for each advertising medium. This allows us to estimate channel-specific short- and long-term effects of advertising and illustrate how the model can be used to inform advertising strategies. We demonstrate the model using data on catalog and email advertising, where customers were randomly held out from each campaign, creating a sequence of randomized field experiments that mitigate potential endogeneity problems. We find that catalogs have a substantially longer-lasting impact on customer purchases than emails, and that there are is a significant interaction (i.e., synergy) between channels. We illustrate how the model can be used to score and target individual customers on the basis of their advertising responsiveness, and find that targeting the most responsive customers substantially increases returns to advertising versus targeting heavy purchasers. In the conclusion, we discuss the potential for using the model in other contexts where individual-level advertising response data is readily available and randomized holdouts are possible, including online display advertising and addressable television.


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