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Course module: 325223-B-6
325223-B-6
Marketing Analytics for Big Data
Course info
Course module325223-B-6
Credits (ECTS)6
CategoryBA (Bachelor)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Marketing; TiSEM: Marketing;
Is part of
B Economics and Business Economics
Minor Marketing Analytics
Minor Elective Minor
Lecturer(s)
Lecturer
dr. G. Knox
Other course modules lecturer
Academic year2020
Starting block
SM 1
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 25/08/2020 up to and including 20/08/2021
Aims
Marketing Analytics for Big Data surveys several topics within the new and emerging field of big data. Marketing analytics itself (e.g., profiling, segmentation) is not new. Yet big data has brought about several new techniques, new applications, and new types of data that are all becoming increasingly important for product and marketing managers, analysts and others to understand and apply. The aim of the course is to give students a broad overview of several areas rather than an in-depth presentation of any one particular technique, application or data type (which could easily occupy an entire course or more). We will cover some of the math and calculations of the models. We will not be estimating these models on large datasets, or coding in R or Python.
 
At the end of the course, students should be able to:
 
  • Describe several different ways big data on customers has allowed researchers and managers to study customers.
  • Evaluate and critique a selection of models used for predicting customer behavior and informing business decisions.
  • Calculate outcomes for a handful of cases, communicating clearly the results and implications
Content
The course comprises 6 topics:
 
  1. Introduction to big data: new data, models, and societal impact
  2. Experimentation with A/B testing, multivariate testing and multi-armed bandits
  3. Customer journey analytics & multichannel attribution
  4. Dynamic targeting: recommender systems
  5. Sentiment analysis, opinion and text mining
  6. Network analytics, social contagion
Course available for exchange students
Conditions of admission apply
Contact person
dr. G. Knox
Timetable information
Marketing Analytics for Big Data
Written test opportunities
DescriptionTestBlockOpportunityDate
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Written exam (50%) / Written exam (50%)EXAM_01SM 1114-12-2020
Written exam (50%) / Written exam (50%)EXAM_01SM 1219-01-2021
Required materials
-
Recommended materials
-
Tests
Assignment (50%)

Written exam (50%)

Final grade

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