Back-to-School
How Long Can the Good Times Last in China?
In the past thirty years, China’s economy has been growing at the pace of 9.88% per year, doubling its GNP about every seven years. As a result of the rapid growth, China today is drastically different in countless ways from what it was, and every corner of the world has felt its impact. However, going forward, how long could the rapid growth rate last? In this session, we will explore the answers to that question and discuss the possible constraints facing China to maintain a high growth rate.
Z. John Zhang
Professor of Marketing and Murrel J. Ades Professor, The Wharton School, University of Pennsylvania
Dr. Z. John Zhang is a Professor of Marketing and Murrel J. Ades Professor at The Wharton School of the University of Pennsylvania. He earned a Bachelors degree in Engineering Automation and Philosophy of Science from Huazhong University of Science and Technology (China), a Ph.D. in History and Sociology of Science from the University of Pennsylvania, and also a Ph.D. in economics from the University of Michigan.
Prior to joining Wharton in 2002, Dr. Zhang taught pricing and marketing management at the Olin School of Business of Washington University in St. Louis for three years and at Columbia Business School for five years. Dr. Zhang’s research focuses primarily on competitive pricing strategies, the design of pricing structures, and channel management. He has published numerous articles in top marketing and management journals on various pricing issues such as measuring consumer reservation prices, price-matching guarantees, targeted pricing, access service pricing, the choice of price promotion vehicles, channel pricing, price wars in China, and the pricing implications of advertising. He has also developed an interest in the movie and telecom industries.
He currently serves as Associate Editor for the Quantitative Economics and Marketing. He is also an area editor for Marketing Science. He won the 2001 John D.C. Little Best Paper Award and 2001 Frank Bass Best Dissertation Award, along with his co-authors, for his contribution to the understanding of targeted pricing with imperfect targetability.
Marshall W. Meyer
Richard A. Sapp Professor of Management, The Wharton School, University of Pennsylvania
Marshall W. Meyer is the Richard A. Sapp Professor of Management in the Wharton School, Professor of Sociology, and Associate Member of the Center for East Asian Studies at the University of Pennsylvania. Meyer has taught at Harvard University, Cornell University, the Riverside, Irvine, and Los Angeles campuses of the University of California, and Yale University, and has been a visiting professor in the Faculty of Business Administration at the Chinese University of Hong Kong, the School of Economics and Management at Tsinghua University, the School of Business and Management at the Hong Kong University of Science and Technology, and Singapore Management University. Meyer was also a Visiting Scholar at the Russell Sage Foundation in 1993-94.
Meyer is a senior member of the editorial team of a new journal on Chinese management studies, Management and Organization Review, sponsored by the Guanghua School of Management of Peking University and the Hong Kong University of Science and Technology. He is also an advisory editor of Harvard Business Review—China.
Meyer is a vice president and a member of the executive committee of the Chamber Orchestra of Philadelphia and a member of the advisory board of Anvil Global Partners. Meyer also serves on the boards of the Chief Executive Leadership Institute of Yale University, Knowledge@Wharton, the SEI Center for Advanced Studies in Management of the Wharton School, and the Wharton Global Family Alliance. He also heads the three Wharton School faculty research initiatives in Greater China: the Chinatrust Research Fund on Greater China, the CEIBS-Wharton Joint Research Initiative, and the Guanghua-Wharton Joint Research Initiative on Firms and Markets in China.
Network-based Marketing: Identifying Likely Adopters Via Consumer Networks
This session will focus on the topic of Network-based marketing. Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) “Network neighbors”—those consumers linked to a prior customer—adopt the service at a rate 3–5 times greater than baseline groups selected by the best practices of the firm’s marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.
Shawndra Hill
Assistant Professor of Operations and Information Management Department, The Wharton School, University of Pennsylvania
Shawndra Hill is an Assistant Professor of Operations and Information Management Department at the Wharton School of the University of Pennsylvania. Her research and teaching focuses on data mining, machine learning and statistical relational learning and their alignment with business problems. Shawndra has won awards for her research including the Herman E. Kroos Award from NYU's Stern School of Business and an award in the ACM’s KDDCUP data mining competition. Her past and present industry partners include AT&T Labs Research, ClearForest, and Siemens Energy & Automation. Dr. Hill has applied machine learning techniques to a number of complex business problems, including fraud detection, targeted marketing, social-network marketing and process automation. Her recent work appears in IEEE Transactions on Data and Knowledge Engineering, Journal of Computational and Graphical Statistics, SIGKDD Explorations, and Statistical Science.
| Platinum | Gold | Silver | Ruby | Partner |
 
|
  
|
  
|
|
|