Consumer Data Analytics (2235.YR.001417.1)

General information

Type:	OPT	Curs:	2	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 2	Ioannis Evangelidis	Marketing	ENG

Marketing Strategy: Participants in this course should be familiar with the basic concepts of marketing strategy: segmentation, targeting and positioning.

Business Statistics: Participants in this course should be familiar with the basics of statistics and hypothesis testing. This is the material that is typically covered in a business statistics course. Consumer Data Analytics is an applied course based on statistics, thus it requires a minimum of understanding of statistics.

The distribution of your workload will be (roughly) as follows:
-Lectures and work during class: 40% of your work load
-Group assignments and feedback sessions: 30% of your workload
-Individual assignment: 30% of your workload

In the core marketing management course, you learned about the importance of market segmentation, targeting and positioning in formulating marketing strategies. But as a marketing strategist (whether a management strategy consultant, a brand manager or a Chief Marketing Officer), you would be faced with the key question: how does one implement these strategies in practice?

The industrial Internet is increasingly allowing firms to measure consumer data (e.g., usage data, preference data) as part of regular business, without a study. Large amounts of data are collected, stored and organized. Such "Big Data" can be easily retrieved today, visualized in a simple manner, and made available to marketing strategists.

"Consumer Data Analytics" will equip you with practical tools to leverage consumer and market data to implement marketing strategies and to aid you in making strategic decisions.

In this course, students will develop a working knowledge of data analytics. At the end of the course, students should be able to:
1) Ask the "right? questions from the data to generate marketing insights.
2) Use market measurement data to generate actionable answers about markets. How to segment customers? Who to target? How and where to position your product? How to map product design and market structure? How to price your product?
3) Understand when and how to apply methods such as regression analytics, cluster analytics, and conjoint analytics for market segmentation, targeting, pricing, and product design.
4) Discuss data analytics questions and results with the data scientists from their organization.

Note that this is NOT a market research course. At the end of the course students will NOT learn "how to conduct? a study and collect data about consumers and markets.

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There is No Required Textbook for this Course. Any readings, notes, handouts, dataset or additional course material will be available through the course website.

However, if you are interested in learning more about the topics discussed in this class, I recommend the following materials:

General Reference Books on Data Analytics
1) Multivariate Data Analysis, 8th Edition (J. F. Hair, B. J. Babin, R. E. Anderson, W. C. Black)
2) Introduction to Machine Learning with Python: A Guide for Data Scientists (A. C. Müller, S. Guido)

Articles for Business and Management Experts
1) Lavalle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N., "Analytics: The New Path to Value? (MIT Sloan Management Review)
2) Davenport, T. H., & Harris, J. G., "What People Want (and How to Predict It)? (MIT Sloan Management Review)
3) Henke, N., Levine, J., & McInerney, P., "You Don't Have to Be a Data Scientist to Fill This Must-Have Analytics Role? (Harvard Business Review)

Scientific Articles on Big Data
1) Varan, H. R. "Big Data: New Tricks for Econometrics? (http://people.ischool.berkeley.edu/~hal/Papers/2013/ml.pdf)
2) Fan, J., Han, F., & Liu, H. "Challenges of Big Data Analysis? (National Science Review, 1: 293-314, 2014: https://academic.oup.com/nsr/article/1/2/293/1397586)

Group	Teacher	Department
Year 2	Ioannis Evangelidis	Marketing