Marketing Analytics in the Digital Era (2235.YR.006336.1)

General information

Type:	OPT	Curs:	1	Period:	S semester
ECTS Credits:	3 ECTS

Teaching Staff:

Group	Teacher	Department	Language
Year 1	Ioannis Evangelidis	Marketing	ENG

Participants in this course should be familiar with the basic concepts of Marketing Strategy, such as segmentation, targeting, positioning, pricing, advertising, product development, etc.

Participants in this course should be familiar with the basic concepts of Marketing Strategy, such as segmentation, targeting, positioning, pricing, advertising, product development, etc..

Further, it is recommended that participants in this course are familiar with the basics of measurement and hypothesis testing. This refers to the material that is typically covered in a Business Statistics course or in a Marketing Research Course. If possible, please refresh your knowledge on this content prior to the start of the course.

You should also have JASP installed on your computer. You can download JASP here: https://jasp-stats.org/download

The course will combine lectures on "what" Marketing Analytics techniques are and "when" to apply them, with hands on JASP sessions on "how" to perform the analyses and interpret the output to take decisions.

Additionally, the course will feature sessions on data-based behavioral insights, during which we will discuss state-of-the-art empirical research on how consumers make decisions, and how you can anticipate and predict those decisions based on marketing research and data analytics.

In the core courses, you learned about the importance of Market Segmentation, Targeting and Positioning in formulating marketing strategies. But as a marketing analyst (whether you are working in management strategy consulting or in brand management), you would be faced with the key question: How does one implement these strategies in practice?

Firms increasingly have access to consumer data (e.g., usage data, perceptions data and preference data) as part of regular business. Large amounts of data are collected, stored and organized. Such "Big Data" can be today retrieved easily, visualized in a simple manner, and made available to marketing strategists. "Marketing Analytics in the Digital Era" 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, you will develop a working knowledge of market data analytics:

1) You develop fluency with regression analytics and machine learning. By the end of this class, you will be able to analyze data and make predictions about market outcomes (e.g., sales, customer preferences, etc.) based on key marketing variables such as price and advertising.

2) You will learn how to think with data and understand the difference between correlation and causation and why it matters for data driven decision making. You will understand how critical it is to eliminate alternative explanations when trying to use data to causally link the decisions you make and the market outcomes you seek.

3) You will learn how to use your market measurement data to generate actionable answers about your markets:

- How to segment customers? Who to target?

- How to identify the customers that you are targeting?

- How to optimize the design of your products?

4) You will learn why and how methods such as regression analysis, cluster analysis and conjoint analysis are useful in market segmentation, in targeting, and in mapping market structure and product design.

5) You will develop an understanding as to which method and approach is best suited to leverage the market measurement data available.

6) You will learn how consumers make decisions on the marketplace.

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.

1. COURSE INTRODUCTION, BIG DATA, & ANALYTICS

2. BUILDING PREDICTIVE MODELS

3. CLUSTER ANALYTICS WITH MACHINE LEARNING

4. CONJOINT ANALYTICS

5. INSIGHTS FROM MARKETING DATA ON CONSUMERS

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 1	Ioannis Evangelidis	Marketing