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National Research University Higher School of Economics Master’s Programme in Big Data Systems Big Data Based Marketing Analytics Course Instructor: Artyom Korkhov Contacts: [email protected] Moscow, 2015 COURSE DESCRIPTION The course “Big Data Based Marketing Analytics” provides an overview of modern technologies and approaches to marketing analytics with extensive usage of tools, designed for analysis and processing of extremely large and complex datasets. Objective of the course The main objective is to teach students how to structure and organise modern marketing analysis workflow, how to integrate and use big data tools. Goals of the Course As a result of study, the student should know how to: • Define what data need to be collected; • Define what tools to use to collect the data; • Select an appropriate method of marketing analysis depending on goals of a marketing department; • Build both personalized and non-personalized marketing campaigns The course is aimed at developing the following competences of the students: • ability to structure marketing analysis workflow; • ability to organize individual and group research work; • ability to select appropriate tools for certain goals; • ability to present and to interpret results of analysis; • ability to use predictive and descriptive models to interpret and forecast different marketing phenomena. COURSE CONTENT Theme 1. Big data and marketing. • What is Big data • Place of Big data in modern marketing: acquisition, conversion, retention Readings 1. Siegel, Eric. Predictive analytics: the power to predict who will click, buy, lie, or die. Wiley, 2013 2. Arthur, Lisa. Big Data Marketing: Engage Your Customers More Effectively and Drive Value. Wiley, 2013 Theme 2. Multichannel marketing. What is multichannel marketing? Connection between online and offline channels Marketing channels Readings 1. Arikan, Akin. Multichannel marketing. Metrics and methods for On and Offline Success. Wiley, 2008 Theme 3. Data collection. Where is the data? Types of data How to collect data from different channels and integrate it together What to collect Readings 1. Kaushik, Avinash. Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex, 1st edition, 2009 Theme 4. CRM Customer Relationship Management - definition, goals, approaches Analytical CRM Segmentation techniques Readings 1. Kumar, V., Reinartz, Werner. Customer Relationship Management. Concept, Strategy and Tools. Springer, second edition, 2012 Theme 5. Personalized marketing What is personalized marketing Usage of Big data in personalized marketing communications Use cases: Netflix, Amazon, Target Readings 1. Siegel, Eric. Predictive analytics: the power to predict who will click, buy, lie, or die. Wiley, 2013 2. Michael Minelli, Michele Chambers, Ambiga Dhiraj . Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses. Wiley, 1 edition, 2013 3. Chuck Hemann and Ken Burbary. Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World. Que, 2013 Theme 6. Recommender systems Overview of recommenders Connection between recommenders and marketing How to recommend: types of recommenders Pros and cons of recommendation systems Readings 1. Leskovec, Jure; Rajamaran, Aanand; Ullman, Jeffrey D. Mining Massive Datasets. Cambridge University press, 2nd edition, 2014 2. Segaran, Toby. Programming collective intelligence. O’Reilly, 2008 Control questions 1. What is CRM? Name several main indicators 2. Online marketing channels. Name and describe them 3. Define personalized marketing communications. How could we measure their effectiveness? 4. Data collection. What options do we have if we need to connect customer data from offline with online? 5. What is web analytics? 6. Define CLV. Is there any differences that could be brought by Big data to the CLV components? 7. Name types of recommender systems. What are the differences? 8. Name a few recommender systems evaluation techniques. What are their strengths and weaknesses? 9. What are the pros and cons of collaborative filtering recommenders? 10. How graph modelling can be used for marketing analytics? Grading The grade consists of the three elements: Homework (Presentation) 20% Group work (Competition) 50% Final task (60 minutes) 30%