The Impact of Soda Taxes on Nutritional Intake and Welfare

Stephan Seiler, Anna Tuchman, Song Yao

Price-based interventions are widely considered by policy makers as a tool to shift customers’ behavior. This paper investigates one such policy intervention where a local government imposed a tax on sweetened beverages in order to discourage unhealthy food consumption and fight obesity and diet-related diseases. Through an extensive set of analyses, we document the effect of the tax on retailers’ pricing decisions and market demand for taxed products and substitutes. We show that the tax on sweetened beverages has had limited effects in reducing total consumption or leading to a shift in consumption towards healthier products. Furthermore, the financial burden is the highest for low income households, while higher income households avoid the tax by driving to stores outside the taxed zone.

Scott Neslin

Scott A. Neslin is the Albert Wesley Frey Professor of Marketing at the Tuck School of Business, Dartmouth College.  He has been a visiting scholar at the Yale School of Management, the Fuqua School of Business, and Columbia Business School. Professor Neslin’s expertise is in the fields of customer relationship management, measurement of marketing effectiveness, sales promotion, and advertising.  He has published several papers on these topics in leading academic journals.  He is co-author with Robert C. Blattberg and Byung-Do Kim of the book, Database Marketing:  Analyzing and Managing Customers, co-authors with Robert C. Blattberg of the book, Sales Promotion: Concepts, Methods, and Strategies, and author of the Marketing Science Institute monograph, Sales Promotion. Professor Neslin has served as President of the INFORMS Society for Marketing Science (ISMS) and is an ISMS Fellow.

The Role of the Physical Store: Developing Customer Value through ‘Fit Product’ Purchases

Chun-wei Chang, Jonathan Z. Zhang, Scott Neslin

Recent trends suggest retailers are ambivalent regarding the contribution of the physical retail store.  Ironically, several traditionally offline retailers are closing stores, while some traditionally online retailers are opening them.  This raises the question, what is the role of the physical retail store in today’s multichannel environment?  We posit that the type of product purchased, “fit” or “non-fit”, impacts subsequent customer value, and that purchasing fit products offline is especially effective at creating high value customers. We formulate a multivariate hidden Markov model (HMM) to investigate how customers make product and channel decisions. The HMM identifies two dynamic states – low-value and high-value. We hypothesize and find that fit-product purchases accelerate customer migration to the high-value state, especially if those purchases are made in the physical store. We theorize this occurs because buying fit products requires customer engagement, the physical store excels at providing this engagement, and engagement leads to higher customer satisfaction and hence value.  In addition, we find that offline marketing communication, specifically direct mail, enhances the likelihood the customer buys fit products offline and hence migrates customers to the high-value state, or keeps them at high value if they are already there. Our findings identify a strategic role that fit products and retail stores play in customer development, and show that marketing can help implement this strategy.

Ryan Dew

Ryan Dew is an Assistant Professor of Marketing at the Wharton School of the University of Pennsylvania. He received his B.A. in Mathematics from the University of Pennsylvania, and his M.Phil. and Ph.D. in Marketing from Columbia University. His research explores how machine learning and Bayesian statistical methodologies can solve real world marketing problems, with a particular interest in the domains of customer relationship management, preference dynamics and estimation, and data-driven design. His recent work, Bayesian Nonparametric Customer Base Analysis with Model-based Visualizations, has been published in Marketing Science. His on-going research focuses on understanding and predicting how consumer preferences change over time, through novel hierarchical nonparametric models, and on understanding the underpinnings of effective logo design from a data-driven perspective, utilizing image processing and machine learning techniques. His dissertation, Machine Learning Methods for Data-driven Decisions, was a winner of the ISMS Doctoral Dissertation Proposal Competition, the Marketing Section of the American Statistical Association’s Doctoral Research Award, and was an honorable mention in the Marketing Science Institute’s Alden G. Clayton Doctoral Dissertation Proposal Competition.

Letting Logos Speak: A Machine Learning Approach to Data-Driven Logo Design

Ryan Dew, Asim Ansari, Olivier Toubia

Logos serve a fundamental role in branding as the visual figurehead of the brand. Yet, due to the difficulty of using unstructured image data, prior research on logo design has been largely limited to non-quantitative studies. In this work, we explore logo design from a data-driven perspective. In particular, we aim to answer several key questions: first, to what degree can logos represent a brand’s personality? Second, what are the key visual elements in logos that elicit brand and firm relevant associations, such as brand personality traits? Finally, given text describing a firm’s brand or function, can we suggest features of a logo that elicit the firm’s desired image? To answer these questions, we develop a novel logo feature extraction algorithm, that uses modern image processing tools to decompose unstructured pixel-level image data into meaningful visual features. We then analyze the links between firm identity, and the features of its logo, through both predictive modelling, and a probabilistic model which links visual features with textual descriptions of firms. We apply our modeling framework on a dataset of hundreds of logos, textual descriptions from firms’ websites, third party descriptions of firms, and consumer evaluations of brand personality to explore these questions.

LIN Song

Song Lin is an Assistant Professor of Marketing at the Department of Marketing of Hong Kong University of Science and Technology. He holds a PhD in Marketing from Massachusetts Institute of Technology. His research interests include product and pricing polices, platform design, consumer learning and search, new products, and advertising.  He has won the 2013 INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition, and the finalist for the 2015 John Little Award for the best marketing paper published in Marketing Science and Management Science.

Informational Complementarity

Tony Ke, Lin Song

Many products are correlated because they share some similar or common attributes. We show that when these attributes are uncertain to consumers, a complementarity effect can arise among competing products, in the sense that a lower price of one product may increase the demands of others. The effect occurs when consumers optimally search for information about both common and idiosyncratic product attributes prior to purchase. We characterize the optimal search strategy for the correlated search problem and provide the conditions for the existence of the complementarity effect. We further explore the implications of the effect for firm pricing. When firms compete in price, although product correlation may weaken differentiation between the firms, the complementarity effect due to correlated search may raise equilibrium price and profit.


Bing Jing

Most products comprise both digital and inspection attributes. For example, while the style of apparel and shoes can be conveyed online, assessing fit still requires personal inspection. In a discrete model of match, we examine the effects of webrooming in a duopoly. When the match probability of the digital attribute is sufficiently low, a counter-intuitive result is that webrooming increases both firms’ profits. Here the reason is that webrooming induces greater participation. This finding is thus opposite to the general impression from the popular press that webrooming intensifies competition by releasing more information. We then generalize this analysis to a model of continuous match values. Here, webrooming still increases both firms’ profits under broad conditions. The reason is that webrooming informs each consumer about her relative preference over the two firms’ digital attributes and, consequently, her optimal search sequence.


CHEN Yubo is Associate Dean, Professor, and Director of Center for Internet Development and Governance at School of Economics and Management, Tsinghua University. He received his Ph.D. in Marketing from the University of Florida, M.Eng. in Systems Engineering and B.Eng. in Industrial Management Engineering from Southeast University. Before joining Tsinghua SEM., he was a tenured professor at Eller College of Management, University of Arizona, USA. He is a recipient of the National Science Fund for Distinguished Young Scholars from the National Natural Science Foundation of China, and selected for the Top-Notch Young Professionals Program of China. Prof. Chen’s main research areas include 1) big data and business innovation in the networked world; 2) market transformation and business analytics in the mobile internet era; 3) digital transformation of Chinese economy, and 4) climate change and sustainability strategy. Prof. Chen has published many articles in top marketing and business journals such as the Journal of Marketing, the Journal of Marketing Research, Marketing Science, and Management Science, including one article been listed as “Most Cited Papers” at Management Science in the last ten years by ISI. His research has won many international awards and recognitions, including INFORMS Frank M. Bass Best Paper Finalist Award, Journal of Marketing MSI/Paul H. Root Award Finalist, Journal of Marketing Research William F. O’Dell Long-term Impact Award Finalist, Journal of Interactive Marketing Best Paper Award and Emerald Citations of Excellence.

Hybrid E-commerce Platform: When an Online Retailer Offers Own Delivery Service

Yubo Chen

The imbalanced development of logistics and payment makes it difficult for an online retailer to reduce the uncertainty of online shopping in emerging markets. In response, some online retailers establish a hybrid e-commerce platform in the hope to reduce online shopping uncertainty, and thus to establish customers’ trust in the platform and increase their purchases. Based on two natural experiments arising from’s initiative of establishing its own delivery service, we examine how the setup of a hybrid e-commerce platform influenced customers’ shopping behaviors between 2009 and 2013. We find that the founding of a hybrid e-commerce platform led customers to increase purchases by 7 to 10 percent at city level. In addition, we find that the sales boosting effect is greater for (1) cities with less mature shipping service, (2) light-buyers, (3) categories with higher uncertainty, and (4) products with fewer discounts. The results are consistent with the interpretation that the establishment of a hybrid e-commerce platform improves shoppers’ trust in the online retailer. Thus, a hybrid platform does more than to speed up the delivery, but more importantly, it helps to build customer trust in the business.