BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| COMPUTER ENGINEERING (ENGLISH, 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 | Fall 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 : | Assist. Prof. SERKAN AYVAZ |
| Course Lecturer(s): |
Assist. Prof. 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. |