Minor Learning from Big Data

Broadening minor


Every day, millions of consumers make online purchases and voice their opinions in product-review websites, blogs and chat rooms. They spontaneously produce massive amounts of user-generated data (UGC), most of which is textual and freely available for downloading and analysis. In this big-data era you can quickly collect large amounts of rich, valuable, and reliable data on consumers. All you need is the right set of tools and the proper training to know how to use them.

However, the wealth of UGC data is so large that it can be hard to know how to track the correct information, and how to separate noise from data. To make this more difficult, UGC data is often unstructured and textual. Do you know how to analyze this? How do you go from consumer textual information to marketing decisions?

The massive adoption of Internet by virtually everyone also made it easy to run large-scale online experiments to identify the best ways to communicate and interact with consumers. These online test/control experiments are often called “A/B tests” because consumers are randomly assigned to a test group (exposed to one of the treatments of interest such as an innovative website design) or to a control group, exposed to a baseline condition such as a traditional website design. The comparison provides information on which condition is the most effective in terms of sales, click-through or another metric of interest. Firms run thousands of such online experiments every day to find out the most effective banners, website design, emails, promotions and even product recommendations.

Thus, learning about consumers has never been so easy. Anyone with the right tools and skills can listen to what millions of consumers are saying, find out what suggestions they have, identify unmet needs and preferences, and experiment to learn the best ways to interact with them. Listening to what consumers are saying is valuable because it can lead to profitable new market opportunities (such as opportunities for new products) and anticipate major problems (such as a recall crisis). 

In this course you will find tools and conceptual frameworks needed to identify and exploit the opportunities from these big data sources.

Learning objectives

This course will push you to develop new analytical skills and to be creative when devising solutions and when interpreting your results to understand the consumer behavior that generated the data you have in your hands.

The objectives of this course are to equip you with the tools and methods necessary to:

  1. Obtain insights on consumer preferences and behavior from user-generated data
  2. Design and run experiments to learn the most effective ways of interacting with a specific set of consumers
  3. Understand major challenges and steps in optimizing online experiments with methods such as website morphing
  4. Understand the potential of user-generated content (UGC) as a way to listen to the voice-of-the-consumer expressed in unstructured data such as texts in blogs and product reviews.
  5. Analyze a real-world online experiment from a real company that has partnered with Erasmus Center for Optimization of Digital Experiments (e-Code: http://www.erim.eur.nl/ecode )

All data analysis in this course can be done using Excel with the templates provided in this course. For example, the analysis of a single movie review from the IMDB website can be done in Excel. However students interested in achieving greater efficiency (such as processing thousands of reviews automatically) can greatly benefit from learning R, which allows you to be far more productive with data.

Special aspects

Preparation is Important!

This Summer prepare yourself for this course with three simple but important tasks:

1. Study this simple R tutorial: www.biostat.jhsph.edu/~ajaffe/docs/undergradguidetoR.pdf

IMPORTANT: Install R in your computer and play with it following the exercises in the link.  

2. If you are not familiar with MS Excel, you MUST learn how to use the v-lookup function at the very minimum. IMPORTANT: Practice extensively and get familiar with using vlookup. You will need this during the course so learn it now, not later

3. Read the chapter on “Regression” and the chapter on “Comparing Two Means” of this book:   
Field, Andy. Discovering Statistics using SPSS (2009 or more recent). London: Sage Publications
IMPORTANT:  you must have read and understood these two chapters BEFORE the start of this course. I will ask you to go back to these chapter several times during the course.

All RSM minors have mandatory attendance.

Overview content

The course has three modules.

  • In the first module (“Mine your Own Business in Blogs, Reviews and Tweets”) we will work with various types of UGC, with a strong emphasis on textual data. We will discuss and use tools to help you automate the detection of both sentiment and content from texts produced by consumers.

The other two modules are focused on online experiments:

  • In the second module (“Learning from Experience: Introduction to Online Experiments”) we will provide an introduction to the popular method of A/B testing (www.wikipedia.com/A/B_testing) and help you run a test on your own.
  • In the third and last module (“Learning While Earning: Advanced Methods for Online Experimentation”) we will focus on adaptive methods, which are the state-of-art of online experiments because they allow firms to run experiments faster and at lower costs. There is one major “module assignment” to be handed in at the end of each module.

Teaching methods:


  • Group Assignments: These are done in groups of 3 to 4 students but the individual grade will vary across students within a group to reflect dedication and contribution. These weights are based on peer assessment and the professor assessment of the actual individual contribution in the overall group output.
  • Individual Assignments 

Teaching materials

Lectures, cases, assignments.


Method of examination


  • Class attendance and Participation (10%)
  • Group Assignments (40%): these are done in groups of 3 to 4 students but the individual grade may be assessed at the individual level to reflect dedication and contribution.
  • Individual Assignments (50%)
  • Resit is possible to replace the grade of one assignment, at most. The grade of the resit will replace the original grade of the assignment even if the grade of the resit is lower than the original grade.

Composition final grade

Each module will have 1/3 of the final grade. The modules are evaluated through assignments


  • Detailed individual feedback via email
  • Additional individual feedback available in person during office hours booked in advance

    Contact person

    Gui Liberali
    (010) 4082732
    room: T10-14

    Faculty website
    Gui Liberali

    Broadening minor
    Rotterdam School of Management, Erasmus University
    Studiepunten (ECTS)
    Campus Woudestein, Rotterdam


    Please read the application procedure for more information.