Nuo (Norah) Xu. July Goizueta Business School Mobile: +1 (770) Clifton Road Website: xu.com Atlanta, GA 30322

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1 Nuo (Norah) Xu July 2015 Goizueta Business School Mobile: +1 (770) Clifton Road Website: xu.com Atlanta, GA Education Ph.D. in Marketing (Expected 2016) Goizueta Business School,, Atlanta, GA, USA Dissertation: Uncovering Customer Dynamics in a Data- Rich Environment Chair: Jagdish Sheth Committee Members: Douglas Bowman, Ramnath Chellappa, Yi Zhao Dissertation proposal defended on June 1, 2015 Bachelor of Management, 2008 School of Management, Fudan University, Shanghai, China Research Interests Empirical Modeling in a Data- Rich Environment Big data Customer intelligence Statistical models of customer behaviors Bayesian modeling and estimation Under Review Xu, Nuo, Yi Zhao, and Yingge Qu, Early Detection of Customers Life Changes: A Control Chart Approach, under review at Marketing Science Work in Progress Xu, Nuo and Yi Zhao, Quick Detection of Changes in Market Response (Data analysis in progress) Xu, Nuo and Jagdish Sheth, Analyzing Synergies among Partners in a Coalition Loyalty Program Using Factorial Hidden Markov Model (Data analysis in progress) Liu- Thompkins, Yuping, Leona Tam, and Nuo Xu, Habit Disruption and Consumer Attrition (Data analysis in progress) 1

2 Dissertation Research Essay 1: Early Detection of Customers Life Changes using Big Data: A Control Chart Approach (job market paper) Life changes have great impact on customers consumption behaviors, thus creating new demand for products and services. However, it is difficult for managers to conduct targeted direct marketing based on life changes because they generally lack sufficient information on life changes at the individual level. This paper provides managers with an efficient solution to detect customers life changes using rich observations collected through customer management systems. Drawing from the statistical process control literature, we develop a sequential hypothesis test of customers life changes. The design of the test is based on mechanisms from the Cusum control chart, which is shown to be effective in detecting small shifts in sequential data. The test statistic is cleverly altered to accommodate characteristics of customer data and the context of customer management. We demonstrate the applicability of our method using data from a Fortune 500 financial services company. Using information about customers financial activities, their communications with the firm and their individual characteristics, we help managers detect a specific type of major life change. We compare the ability of the proposed model to identify life changes with the hidden Markov model using both empirical data and simulation experiments. In both settings, the proposed solution performs better than the hidden Markov model. We also demonstrate how managers can identify the optimal trade- off point of true positive and false positive rates under a given structure of cost and revenue when using the proposed solution. Essay 2: Quick Detection of Changes in Market Response In today s information age, managers often develop a new type of myopia. When numerous incidents that could potentially influence the performance of products and brands are taking place every day, managers simply cannot evaluate the impact of each one of them. To help managers monitor potential threats and opportunities in the market place, we develop a solution based on the Cusum control chart to continuously monitor aggregate market data for signs of systematic changes in market responses. The application of the Cusum control chart ensures the efficiency of the detection by dynamically identifying the optimal change point as new data are observed. Beyond efficiency, the solution also allows managers to trace the initial time point when the new pattern of market response emerges. This time point is a valuable piece of information because it facilitates narrowing down possible causes of the detected change to a few events that occur adjacent to the identified time point. We design the solution to detect systematic deviances from previous market behaviors in any direction, whether it is an increased sensitivity to price promotion or decreased interest in a certain product feature. In this way, the proposed solution largely reduces managers burden in judging the 2

3 significance of a pattern change in market responses. We demonstrate the applicability of our solution using the Dominick s dataset. During the observation period of the data set, several new products and brands entered the market, and previous studies have shown significant impact of these new product entries to incumbents in the market. We use these incidents to test whether the proposed solution can effectively detect changes in market response due to new product entry and correctly identify the time of entries. Essay 3: Analyzing Synergies among Partners in a Coalition Loyalty Program Using Factorial Hidden Markov Model A coalition loyalty program is a loyalty card platform formed by two or more merchants. In a typical coalition loyalty program, customers can collect loyalty points by shopping within the coalition network and redeem points at all partner stores. While this type of program quickly gains popularity among customers because of its convenience, debates exists on whether participating partners can benefit from the coalition. Central to this debate is whether a positive network effect exists among the partners. In this study, we propose a factorial HMM model to examine synergies among partners through customers cross- buying behaviors. Preliminary results show that not all partners can benefit when customers actively engage with other partners in the coalition. Interestingly, results show that competitors can benefit by joining the coalition and driving growth in customer value together. Externally Funded Projects MSI research grant (#4-1936) Detecting Customers Life Changes in Big Data, Marketing Science Institute, 2015 Conference Presentations & Talks Detecting Customers Life Changes in Real Time: A Complete Solution using the Cusum Control Chart, Presenter, Federal Home Loan Bank, June 2015 Change Detection using Sequential Likelihood Ratio Test, Presenter, Marketing Science Conference, Johns Hopkins University, Baltimore, June 2015 Habit Disruption and Consumer Attrition, Co- author, Marketing Science Conference, Johns Hopkins University, Baltimore, June 2015 Detecting Customers Life Changes in Real Time, Presenter, Wharton Customer Analytics Initiative Research Symposium, University of Pennsylvania, Philadelphia, March 2015 In- network and Out- of- network Shopping in a Loyalty Program, Presenter, Marketing Science Conference,, Atlanta, June

4 Awards and Honors Wharton Customer Analytics Initiative Predicting Life Changes Opportunity Winner, 2014 Wharton Customer Analytics Initiative Coalition Loyalty Program Research Opportunity Winner, 2013 Piedmont Teaching Assistant Training and Teaching Opportunity Fellowship on Sustainability, Teaching, and Curriculum,, 2015 ISMS Doctoral Consortium Fellow, Johns Hopkins University, 2015 ISMS Doctoral Consortium Fellow,, 2014 Sheth Fellowship,, 2014 Goizueta Business School Doctoral Fellowship,, Academic Scholarship, School of Management, Fudan University, Teaching Experience Teaching Interests Marketing Research Advanced Analytics in Marketing Customer Intelligence Product Management Teaching Associate Product & Brand Management (BBA class), Guest Lecturer for Professor Douglas Bowman, , Goizueta Business School, Teaching evaluation: 4.4/5 Lecture topics: Survey Method, Max- Diff, and Targeting using Simple Map Implemented problem- based teaching method to increase students hands- on experience in marketing analysis Teaching Assistant Marketing Management (MBA class), TA for Professor Ryan Hamilton, Fall 2013, Goizueta Business School, Marketing Strategy & Customer Relationship Management (BBA & MBA class), TA for Professor Michael Lewis, Fall 2013, Goizueta Business School, Marketing Principles (BBA class), TA for Professor Mohammed Abdur Razzaque, University of New South Wales, Australia Teaching Assistant, Pricing (BBA class), TA for Professor Yiping Song, Fudan University, China 4

5 Teaching Training Piedmont Teaching Assistant Training and Teaching Opportunity workshop, 2015, Professional Opportunity Development Sessions, 2014, Master Teacher Workshop, 2012, Goizueta Business School, Teaching Assistant Training and Teaching Opportunity Program, 2011, Emory University Other Work & Research Experience Graduate Student, University of New South Wales, Sydney, Australia, 2009 Research Intern, Bain&Company, 2008 Research Assistant for Small Business in China Project, Fudan University, 2008 Research Assistant for Swarovski in China Project, Fudan University, 2007 Professional Affiliations INFORMS Society on Marketing Science (ISMS) American Marketing Association (AMA) Selected Graduate Courses Marketing Marketing Strategy Consumer Behavior Empirical Models in Marketing Marketing Topics Survey of Business Research Methods Economics Probability and Statistics for Economists Econometrics I Econometrics II (Georgia State University) Advanced Econometrics (Georgia State University) Microeconomic Theory I Microeconomic Theory II Statistics Longitudinal Data Categorical Data Generalized Linear Models Bayesian & Empirical Bayes Method Statistical Computing Optimization Sandy Jap Ryan Hamilton Douglas Bowman Mike Lewis Anand Swaminathan Esfandiar Maasoumi Esfandiar Maasoumi Judex Hyppolite Shiferaw Gurmu Andrew Francis- Tan Hugo Mialon Jose Binongo Ying Guo & Qi Long Qi Long Lance Waller George Cotsonis Valerie Miller 5

6 References Jagdish Sheth (Chair) Charles H. Kellstadt Professor of Marketing Goizueta Business School 1300 Clifton Road, Atlanta, GA (404) Douglas Bowman Professor of Marketing Goizueta Business School 1300 Clifton Road, Atlanta, GA (404) Yi Zhao Assistant Professor of Marketing Robinson College of Business Georgia State University 35 Broad Street NW, Atlanta, GA (404)

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