Bankscope in the trial


Bankscope combines widely-sourced data with flexible software for searching and analysing banks. Bankscope contains comprehensive information on banks across the globe. You can use it to research individual banks and find banks with specific profiles and analyse them. Bankscope has up to 16 years of detailed accounts for each bank. Bankscope recently added the Fitch Bank Credit Model to Bankscope. This is a statistical model that produces a financial implied rating and daily implied CDS spread for over 11,000 banks across the globe. This model helps you evaluate banks that aren’t traditionally assessed by rating agencies and validate and benchmark your own credit opinions. 


Language:  English

Trial Period: Until Dec 31, 2014

Access: (On campus)


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Oriana- Company information across the Asia-Pacific region in the trial


ORIANA is a comprehensive database containing financial information on over 20 million public and private companies in 46 countries including the Middle East and Asia-Pacific regions. Each company is part of a default peer group based on its activity codes; integral graphs and a specific report demonstrate its position in this peer group. A company tree diagram instantly illustrates the structure of the group.


Language  Chinese/English

Trial Period  Until Dec 31, 2014

Access: (On campus)

Update:  Daily


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YAO Song

Song Yao is an Associate Professor of Marketing at the Carlson School of Management, University of Minnesota. Professor Yao has won the 2012  Paul Green Best Paper Award and the 2009 John Howard Dissertation Award, both of which are sponsored by the American Marketing Association. He was the finalist for INFORMS Long Term Impact Award in 2017, the Frank Bass Outstanding Dissertation Award in 2011 and 2012, the John Little Best Paper Award in 2009 and 2011. He has also been selected by the Marketing Science Institute (MSI) as one of the MSI Young Scholars of 2017. He serves on the Editorial Boards of the Journal of Marketing Research, Marketing Science, and Quantitative Marketing and Economics. Professor Yao’s research interests include quantitative marketing, online marketing, advertising, pricing, and customer management. His publications appear in leading academic journals, including Management Science, Marketing Science, the Journal of Marketing Research, and Quantitative Marketing and Economics. Professor Yao received his Ph.D. in Business Administration from Duke University, M.A. in Economics from the University of California, Los Angeles, and B.A. in Economics from the Renmin University of China.

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.