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Course module: 328054-M-6
328054-M-6
Customer Analytics
Course info
Course module328054-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Marketing; TiSEM: Marketing;
Is part of
M Econometrics and Mathematical Economics
M Marketing Analytics
M Business Analytics and Operations Research
M Marketing Management
M Data Science and Society
Lecturer(s)
Lecturer
dr. G. Knox
Other course modules lecturer
Lecturer
A. van der Vliet, MSc
Other course modules lecturer
Academic year2020
Starting block
BLOK 2
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 13/10/2020 up to and including 31/08/2021
Aims
Customer analytics is about applying simple models to understand and predict customer behavior, from the level of the individual customer, as in true one-to-one marketing, to the aggregate level of the entire customer base, which may be a good proxy for firm value.  Companies have more data about their customers than ever before. Yet they may be “drowning in data” yet “starving for insights,” because they have no way to organize their data within a larger statistical framework.  The role of the model is to summarize patterns and generate predictions of customer behavior in the future. 
 
The models we cover fall into two broad categories: next-period models, and long-term models.  Next-period models are good for predicting what happens immediately following some action.  For example, customer targeting or selection falls into this category.  Long-term models use a long horizon and are for forecasting retention over time, or calculating customer lifetime value or customer equity (the value of the customer base). 
 
We will examine a few types of models to analyze customer behavior, including:
 
  1. Recency-Frequency-Monetary (RFM) analysis
  2. Logistic regression
  3. Decision trees
  4. Bernoulli process models
  5. Empirical Bayes methods 
 
We will use these approaches to answer several substantive questions such as:
 
  1. Test marketing: When should a test be conducted, how large should the test be?
  2. Targeting: which customers should be selected for e.g., acquisition, retention, cross-selling, direct mailing?
  3. Retention: what is the retention rate, and how does it change when customers are heterogeneous (i.e., different) and may drop out, or churn, over time?
  4. Customer lifetime value (CLV): how do you calculate the value to the firm of the customer over his or her lifecycle?  What is the value of a firm’s entire customer base, i.e., customer equity (CE)?
 
 

Course Purpose:

Managers (as well as consultants, analysts, and investors) are increasingly tasked with providing valid answers to such questions. Yet many of them lack the tools to address these problems. This course is designed to give you the powerful, cutting-edge tools to address these issues.
 
The course is organized around several case studies that illustrate an important concept with data. All these examples will be “hands-on” and have an emphasis on real-time problem solving. You will develop the necessary skills to estimate these models and evaluate their results in SPSS & Excel.

Learning Goals:
 
  1. For a given model, students should be able to interpret results and explain intuitively what it assumes about customer behavior.
  2. Students should understand how to validate models and avoid overfitting.
  3. Students should be able to fit these models to real-world data; they should also be able to evaluate predictions across models and derive the managerial implications.
  4. Students should be able to calculate customer lifetime value (CLV).
  5. Understand the role of unobserved heterogeneity in measuring customer loyalty.
 
Specifics

Enrollment

ATTENTION! The deadlines for application deviate from the standard OSIRIS deadlines.
The enrollment procedure for this course is different for different groups:
  • Students in MSc Marketing Analytics  need to enroll via the procedure published on the MSc Marketing Analytics General Information CANVAS page  before September 15 (23.59hrs).
  • Students in MSc Marketing Management September 2019 entrants need to enroll via the procedure published on the MSc Marketing Management General Information CANVAS page before September 15 (23.59hrs).
  • Students in MSc Marketing Management of earlier entry moments need to send a message to TiSEM-MSc-Marketing-Management-Research@uvt.nl before September 15 (23.59hrs).
  • Students in Research Master need to send a message to TiSEM-MSc-Marketing-Management-Research@uvt.nl before September 15 (23.59hrs).
  • This course is not open to student from other programs. We make no exceptions.

All students who registered correctly will be enrolled in the OSIRIS course by program management, after which they will be automatically added to the Canvas page of the course.
We will not take into account requests for enrollment after this deadline. You will be put on a waiting list. After placing all students (second half of September), we will check if there are seats left and let you know if you can do the course or not.
On October 28 courses will start – only de-enrollments can be taken into account.
 
If there are any questions or problems, please contact tisem-msc-mm-ma@tilburguniversity.edu immediately.
 
Content
Type of instructions
Lectures, Computer Labs
Type of exams
Assignments, computer final exam

Compulsory Reading
  1. Selected articles. The reading list will be available on Blackboard before the start of the course..
  2. Other material will be announced on Blackboard, no need to purchase any materials in advance..
Contact person
dr. G. Knox
Timetable information
Customer Analytics
Written test opportunities
DescriptionTestBlockOpportunityDate
Digital Exam (70%) / Digital Exam (70%)EXAM_01BLOK 2227-01-2021
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Digital Exam (70%) / Digital Exam (70%)EXAM_01BLOK 2121-12-2020
Required materials
-
Recommended materials
-
Tests
Assignments (30%)

Digital Exam (70%)

Final grade

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