BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS) | |||||
| 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: | |
| 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) | To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems. | 3 |
| 2) | To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management. | 2 |
| 3) | To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained. | 4 |
| 4) | Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards. | 3 |
| 5) | To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice. | 3 |
| 6) | Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively. | 2 |
| 7) | Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications. | 3 |