Discipline: Marketing and Information Systems. 1 Title Advanced Topics in Digital Marketing

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1 Discipline: Marketing and Information Systems 1 Title Advanced Topics in Digital Marketing 2 Lecturer Prof. Dr. Bernd Skiera (Goethe University, Frankfurt, Germany) ( 3 Date and Location September 18 21, 2017, Frankfurt am Main 4 Course Description 4.1 Abstract and Learning Objectives This course will expose participants to advanced topics in digital marketing and also force them to analyze data. The aim is to understand how to address topics in digital marketing so that they can be published in top journals. 4.2 Content The course covers the following topics, which represent a rather broad overview of research topics in digital marketing. online advertising (including real-time bidding and approaches to identify causality) social network analysis (including some simple analysis in R) customer-based enterprise valuation (which is nowadays much better feasible because of a much broader availability of data) Students will also have the opportunity to briefly describe their own research projects on day 1 so that they receive some feedback from the instructor as well as the other participants of the seminar. Students will have to submit solutions to three exercises that involve the analysis of data. One data set will be rather large. Students can analyze the data with a statistical software of their choice (e.g., Stata, R) but the instructor expects that students have access to such software and also know how to use it. 1/5

2 4.3 Schedule DAY I: Introduction to online marketing 8:30-9:00 Arrival of participants and check-in 09:00-10:30: Introduction to Digital Marketing 11:00-12:30 Shipping fee policies and return management 13:30-15:00 Introduction to case studies and description of data sets 15:30-17:30 5 minute presentation of research project of each student After 17:30: Time to solve Exercise 1 DAY II: Online Advertising 09:00-10:30: Discussion of solution of Exercise 1 11:00-12:30 Causality in Online Advertising 13:30-15:00 Search Engine Advertising 15:30-17:00 Real-Time Bidding and Value of Information in Online Advertising After 17:00: Time to solve Exercise 2 DAY III: Social Network Analysis 09:00-10:30: Discussion of solution of Exercise 2 11:00-12:30 Introduction to Social Network Analysis 13:30-15:00 Community Detection 15:30-17:00 Using social network analysis to detect competition After 17:00: Time to solve Exercise 3 2/5

3 DAY IV: Valuing companies in a digital world 09:00-10:30: Discussion of solution of Exercise 3 11:00-12:30 Customer-Based Firm Valuation (Part 1) 13:30-15:00 Customer-Based Firm Valuation (Part 2) 15:30-17:00 Visibility-at-Risk 4.4 Course format The course is a combination of lectures and hands-on exercises. In particular, the participants get a chance to directly apply the concepts taught in the lectures to datasets provided. Participants can solve these exercises in groups and they will get access to the data sets a few days before the class. Still, participants need to expect that they might have to work during the evenings on solving those exercises. I also strongly encourage the participants to familiarize themselves with the papers listed in the essential reading list. 4.5 Assignments There will be three assignments. I will provide participants with access to the assignments and the corresponding data sets a few days before the seminar. All assignments are due at 8.00 am on the following day. Case Study Star Digital: Assessing the Effectiveness of Display Advertising Case Study Bazaar.com: Measuring ROI on Sponsored Search Ads Case Study Marando: Optimal Decisions in Real-time Bidding for Online Display Advertising 5 Preparation and Literature 5.1 Prerequisites I expect that participants have basic skills in digital marketing but participants need to have a solid knowledge in statistics and multivariate data analysis techniques. In particular, they need to be able to work with a statistical software of their choice such as STATA or R. Some parts of day 3 will rely on the software R. 5.2 Essential Reading Material Day I: Introduction to online marketing Kannan, P.K. / Li, Hongshuang Alice (20), Digital marketing: A framework, review and research agenda, International Journal of Research in Marketing, Vol. 34 (1), Wedel, Michel / Kannan, P.K. (2016), Marketing Analytics for Data-Rich Environments, Journal of Marketing, Vol. 80 (November), /5

4 5.2.2 Day II: Online Advertising Blake,Thomas / Nosko, Chris / Tadelis, Steve (2015), Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment, Econometrica, Vol. 83, Issue 1, Försch, Steffen / Heise, Marc / Skiera, Bernd (2017), The Impact of Floor Prices in Real-Time Online Display Advertising Auctions on Publisher s Profit, Working Paper Lewis, Randall A. / Rao, Justin M. (2015): The Unfavorable Economics of Measuring the Returns to Advertising, Quarterly Journal of Economics, Vol. 130, Issue 4, Skiera, Bernd / About Nabout, Nadia (2013), "PROSAD: A Bidding Decision Support System for Profitable Search Engine Marketing", Marketing Science, Vol. 32, Issue 2, Trusov, Michael / Ma, Liye / Jamal, Zainab (2016), Crumbs of the Cookie: User Profiling in Customer- Base Analysis and Behavioral Targeting, Marketing Science, Vol. 35, Issue 3, Day III: Social Network Analysis Hinz, Oliver / Skiera, Bernd / Barrot, Christian / Becker, Jan (2011), "An Empirical Comparison of Seeding Strategies for Viral Marketing", Journal of Marketing, 75 (November), Nitzan, Irit / Libai, Barak (2011), Social Effects on Customer Retention, Journal of Marketing, Vol. 75 (November), Ringel, Daniel M. / Skiera, Bernd (2016), "Visualizing Asymmetric Competition among More than 1,000 Products Using Big Search Data", Marketing Science, Special Issue on Big Data: Integrating Marketing, Statistics, and Computer Science, Vol. 35, Issue 3, Day IV: Valuing companies in a digital world Gupta, Sunil / Lehmann, Donald R. / Stuart, Jennifer Ames (2004), Valuing Customers, Journal of Marketing Re-search, 41, McCarthy, Daniel M. / Fader, Peter S. / Hardie, Bruce G.S. (2017), Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data, Journal of Marketing, Vol 81, Issue 1, Schulze, Christian / Skiera, Bernd / Wiesel, Thorsten (2012), "Linking Customer and Financial Metrics to Shareholder Value: The Leverage Effect in Customer-Based Valuation", Journal of Marketing, Vol. 76 (March), Additional Reading Material Day I: Introduction to online marketing Doppler, Christian / Skiera, Bernd (2017), "A Model to Determine an Optimal Shipping Fee Policy", Working Paper El Kihal, Siham / Schulze, Christian / Skiera, Bernd (2017), "Which Return Management Strategies are Most Beneficial for Online Retailers?", Working Paper Petersen, Andrew J. and Kumar, V. (2015), Perceived Risk, Product Returns, and Optimal Resource Allocation: Evidence from a Field Experiment, Journal of Marketing Research, 52(2), Day II: Online Advertising Goldfarb, Avi, / Tucker, Catharine (2014), Conducting Research with Quasi-Experiments: A Guide for Marketers, 4/5

5 5.3.2 Day II: Social Network Analysis Netzer, Oded / Feldman, Ronen / Goldenberg, Jacob / Fresko, Moshe (2012), "Mine Your Own Business: Market-Structure Surveillance Through Text Mining", Marketing Science, Vol. 31, Issue 3, Day IV: Valuing companies in a digital world Doppler, Christian / Skiera, Bernd / Alves Werb, Gabriela (2017), "An Approach to Measure a Firm s Profit-at-Risk From Losing Visibility in Organic Search Results", Working Paper McCarthy, Daniel M. / Fader, Peter S. (2017), An Indirect Inference Method for Valuing Non- Contractual Businesses: What Customer Data Summaries Should Investors Seek?, Working paper 5.4 To prepare All participants are required to read the essential reading material prior to the course and to make sure that they have access to a statistical software of their choice (e.g., Stata, R) 6 Administration 6.1 Max. number of participants The number of participants is limited to 20 in order to ensure that everyone can benefit from feedback and discussions. 6.2 Assignments The course is a combination of lectures and hands-on in-class exercises. All three exercises will be graded and account for 25% of the grade. The remaining 25% of the grade will be based upon class participation. 6.3 Credits The course (including the exam) is eligible for 6 ECTS. 6.4 Costs The course fee is EUR /5