Download Master`s Programme in Big Data Systems

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%