IS 612: Product Experimentation and Analytics

Contents
This course examines how firms improve digital products and services – particularly their features, marketing, advertisement, retail, and pricing – through data-driven experimentation and analytics. In many organizations, critical product decisions must be made under uncertainty and at scale. Leading firms such as Amazon, Netflix, Google, Booking.com, and Coca-Cola address this challenge through continuous experimentation – for example, by A/B testing digital services and marketing campaigns – and systematic analysis of behavioral data. The course provides business students with a rigorous understanding of these approaches. Students learn how to define and interpret product metrics, design experiments, and trans­late empirical results into actionable business conclusions. The course covers key state-of-the-art methods in product experimentation and analytics, including A/B testing, multi-armed bandits, and difference-in-differences. The course is aimed at business students with an interest in analytics. The course combines conceptual foundations provided in lectures with hands-on application in exercise sessions. In the exercise sessions, students work on exercise sheets and analyze real-world data using R. No prior experience in programming or statistical software (e.g., R) is required. Students receive an introduction to R as part of the course.

Learning outcomes
After successfully completing the course, students..

  • ..understand how firms use experimentation and analytics methods to inform product decisions about pricing, advertising, recommendations, and consumer experience
  • ..can evaluate product experiments and apply modern analytics methods such as A/B testing, bandits, and difference-in-differences,
  • ..analyze experimental and product data and interpret empirical results,
  • ..draw business-relevant conclusions and communicate insights in a clear manner.

Necessary prerequisites
Successful completion of at least one of the following courses: CC501, CC502, CC503, BE510, CS500, CS530, CS550, CS560, CS652, IE500, IE560. Concurrent enrollment is not sufficient.

Recommended prerequisites

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS8 SWS
Exercise class2 SWS5 SWS
ECTS credits6
Graded yes
Workload180h
LanguageEnglish
Form of assessmentWritten exam, closed book (60 mins)
Restricted admissionyes
Further informationPlease register via the student portal.
Examiner
Performing lecturer
Prof. Dr. Jens Förderer
Prof. Dr. Jens Förderer
Prof. Dr. Jens Förderer
Frequency of offeringFall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. WiPäd, M.Sc. VWL, M.Sc. Wirt. Inf., M.Sc. MMFACT, M.Sc. MMOSCM
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4