Minor Learning from Big Data: How to Learn and Interact with Consumers in the Big-Data Age

Category
Broadening minor
Minor code
MINRSM039
Duration
10 weeks

Content

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

Learning objectives

This minor 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.

The minor 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.

Special aspects

Required knowledge

  • Before starting this course, you are expected to have a working knowledge on basic statistical concepts and tools. More specifically, you are expected to be familiar with regression analysis (such as linear regression), and with mean comparisons (such as hypothesis testing using t-tests). For example, you are expected to know how to write out a regression model, run it on the software of your choice (Eviews, stata, spss, R or other), and interpret the results).

Calibrating Expectations

  • This course will NOT teach you how to use R, Python, nor any programming language. You will be exposed to new statistical methods and you will have the freedom to solve them using the tool of your choice (R, Matlab, Stata, e-views or any other).

All RSM minors have mandatory attendance.

 

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 

Examination

Method of examination

  • 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.

The resit will be a closed-book written test that will test in detail your ability. During the test, students will be asked to demonstrate that they proper have the knowledge and skills needed to complete the report that is to be replaced. For example, the student may be given data and asked to calculate intermediate steps, or to describe specific calculations that were involved in that report.

Composition final grade

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

Feedback

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

    Frequently Asked Questions

    Frequently asked questions (FAQ)

    Contact person

    Gui Liberali
    liberali@rsm.nl
    (010) 4082732
    room: T10-14

    Faculty website
    https://www.rsm.nl/people/guilherme-liberali/

    Category
    Broadening minor
    Minor code
    MINRSM039
    Duration
    10 weeks
    Organisation
    Rotterdam School of Management, Erasmus University
    Study points (EC)
    15
    Instruction language
    English
    Location
    Campus Woudestein, Rotterdam

    Registration

    Please read the application procedure for more information.