INFORMATION TECHNOLOGIES (TURKISH, THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
BDA5002 Marketing Analytics Spring 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery:
Course Coordinator : Dr. Öğr. Üyesi SERKAN AYVAZ
Course Lecturer(s): Dr. Öğr. Üyesi SERKAN AYVAZ
Course Objectives: Marketing Analytics develops and utilizes quantitative marketing decision models to plan, implement, and analyze marketing strategies and tactics.
The course objectives are to help the students understand how analytical techniques and quantitative models can enhance decision-making by converting data and information to insights and decisions, help the students learn to view marketing phenomena and processes in a quantitative fashion, and expose the students to successful use of marketing analytics.

Learning Outputs

The students who have succeeded in this course;
1-)Understand how analytical techniques and quantitative models can enhance decision-making by converting data and information to insights and decisions.

2-)Learn to view marketing phenomena and processes in a quantitative fashion

3-)Understand basic concepts and successful usage of marketing analytics.

Course Content

In this course, concepts, methods and applications related to Marketing analytics will be studied with decision modeling. An analytical approach will be presented to topics such as market segmentation, targeting, positioning, pricing and promotional planning.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to Marketing Analytics
2) Linear Regresyon Kullanan Market Response Modelleri
3) Market Response Models Using Logistic Regression
4) Segmentation & Marketing Using Cluster Analysis
5) Segmentation & Marketing Using Discriminant Analysis
6) Customer Value and Loyalty Data
7) Customer Lifetime Value and Prediction of Customer Value
8) Pricing & Sales Promotion Decisions - Deciding on the “Right” Pricing Approach
9) Pricing & Sales Promotion Decisions - Tactical Pricing
10) Retail Analysis - Market-Basket Data
11) Advertising Models
12) Project Presentations
13) Project Presentations

Sources

Course Notes: There is no required text book. The PowerPoint presentations/class notes will also be available on the ItsLearning website following each class.
References: • Principles of Marketing Engineering by Gary L. Lilien et al. 2012. ISBN-978-0985764807 • Marketing Analytics: Data-Driven Techniques by Wayne Winston. 2014. ISBN-978-1118373439

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application 10 % 15
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments % 0
Presentation % 0
Project 1 % 25
Seminar % 0
Midterms 1 % 20
Preliminary Jury % 0
Final 1 % 40
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
PERCENTAGE OF SEMESTER WORK % 35
PERCENTAGE OF FINAL WORK % 65
Total % 100

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Uses basic Software Engineering knowledge and competencies.
2) Applies the software development ability that is necessary for software engineering applications.
3) Uses data structures and applies information about algorithm development.
4) Develops system programs on operating systems.
5) Develops system programs on operating systems.
6) Creates the structure of computer networks and network security.
7) Uses business intelligence, data mining and data analysis tools, applies techniques about them.
8) Develops database applications and WEB based programs.
9) Defines, analyzes, designs and manages information technologies projects.
10) Uses and develops technology-based environments and tools in education.
11) Detects, identifies and solves information technology needs of the business environment.
12) Uses the capabilities of information technologies within the rules of professional responsibility and ethics.