Excellence in
Management
Education

Title
MBA 2010 Market Intelligence
Contact hours per week
2.0
Lecturer
Jank, Wolfgang
Language
Content
The practice of marketing is changing. Due to increasing desktop computing power and companies amassing massive amounts of data, marketing decisions made by companies in practice are becoming more and more data based. This holds in many sectors, but in particular in banking, retailing, internet marketing, and direct marketing. In Finance, a similar evolution to model based decision making has taken place already, preceding that in Marketing by ten years or so. Because of this new approach to marketing, companies are in need of people with a new set of analytical marketing skills. These skills are not yet developed in many business programs, and people with only a methodological training in statistics or mathematics often lack the substantive knowledge to implement the results of their quantitative approaches in practice. As a result, there are only a handful of
companies that use analytics as part of their core strategy and they include Amazon, Google, Harrah?s, Capital One or the Boston Red Sox.
In this course, we will study analytics for marketing decision makers. We will study several essential data-driven analytical methods, we will implement these methods using state-of-the art data mining software, solving real problems and using real data. We will also study the use of analytics within several companies that have made analytics a part
of their core strategy. This course is very hands-on and will have components of lectures, case discussions, data-driven cases and in-class practice sessions.
Prerequisites
Students should have a basic understanding of statistics. I will assume that students have mastered a course in introductory statistics. Students should also have basic knowledge of marketing. Basic software skills (particularly, for handling and manipulating data) is also
a plus. Since assignments are solved in teams, it is not essential that every student is an absolute expert of every skill; but a portfolio of different skills across each team is definitely a plus.
Literature
There is no single textbook for this course. Instead, there will be a variety of handouts and cases that students will prepare for each class. In addition, I recommend several books that cover material relevant to this class.
Further literature
The following is an (incomplete) list of books that cover material relevant to this course:
? Lattin, Carroll and Green ?Analyzing Multivariate Data.? Duxbury/
Thomson. (esp. Chapters 3, 8, 12 and 13)
? Hastie, Tibshirani and Friedman ?The Elements of Statistical Learning? Springer. (esp. Chapters 3, 4, and 14)
? Berry and Linoff ?Data Mining Techniques ? For Marketing, Sales and
Customer Relationship Management? Wiley. (esp. Chapters 5, 6, and 11 ? but also read Chapters 17 and 18)
? Markov and Larose ?Data Mining the Web? Wiley. (esp. Chapters 3 and
5)
? John, Whitaker and Johnson ?Statistical Thinking in Business? Chapman and Hall. (esp. Chapters 3, 4, 8, and 9)
Method of examination
In-class data project presentations (in teams) 25%
In-class case analysis presentations (in teams) 25%
End-of-class paper (in teams) 25%
Class participation (individual) 25%