BDA5002 Marketing AnalyticsBahçeşehir UniversityDegree Programs COMPUTER ENGINEERING (ENGLISH, NON-THESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
COMPUTER ENGINEERING (ENGLISH, NON-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
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
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 Outcomes

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 / Textbooks: 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
Application 10 % 15
Project 1 % 25
Midterms 1 % 20
Final 1 % 40
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) Define and manipulate advanced concepts of Computer Engineering
2) Use math, science, and modern engineering tools to formulate and solve advenced engineering problems
3) Notice, detect, formulate and solve new engineering problems.
4) Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
5) Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
6) Work effectively in multi-disciplinary research teams
7) Acquire scientific knowledge
8) Find out new methods to improve his/her knowledge.
9) Effectively express his/her research ideas and findings both orally and in writing
10) Defend research outcomes at seminars and conferences.
11) Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc.
12) Demonstrate professional and ethical responsibility.
13) Develop awareness for new professional applications and ability to interpret them.