Information Package / Course Catalogue
| Business Administration | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Faculty of Engineering | ||||
| Course Code | IE 432 | ||||
| Course Title in English | Marketing Analytics | ||||
| Course Title in Turkish | Pazarlama Analitiği | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom,Lecture | ||||
| Level of Course | Advanced | ||||
| Semester | Spring | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 157 hours per semester | ||||
| Number of Credits | 6 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites |
IE 104 - Computational Methods for IE MATH 227 - Probability and Statistics for Engineering I |
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| Expected Prior Knowledge | Basic principles of computational methods and introductory level probability/statistics | ||||
| Co-requisites | None | ||||
| Registration Restrictions | - | ||||
| Overall Educational Objective | To learn the basics of marketing analytics processes with hands-on applications using modern tools | ||||
| Course Description | This course provides an introduction to the basic concepts of marketing analytics. Topics include market response models, customer segmentation, basket analysis and pricing models. The subjects covered throughout the course provide students with the required knowledge in the design and management of marketing analytics. The course considers both the systems observed in real-life applications and the basic analytical methods to provide insights for effective management. | ||||
| Course Description in Turkish | Bu ders pazarlama analitiği temel kavramlarına bir giriş sağlar. Konular içerisinde piyasa tepki modelleri, müşteri segmentasyonu, sepet analizi ve fiyatlandırma modelleri yer almaktadır. Ders boyunca işlenecek bu konular öğrencilere pazarlama analitiği tasarımı ve yönetiminde kullanılan stratejileri geliştirmek için gerekli bilgiyi sağlayacaktır. Bu ders etkin yönetim anlayışını sağlamak için hem gerçek hayattaki uygulamalarda gözlemlenen sistemleri hem de temel analitik yöntemleri göz önünde bulundurur. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) understand the basics of using programming for marketing analytics; 2) demonstrate an understanding of the key methods for customer response prediction; 3) understand the key methods for customer segmentation; 4) function effectively as a member of a team; 5) organize and deliver effective verbal, written, virtual, and graphical communications. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1) Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | |||||
| 2) Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | |||||
| 3) Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | |||||
| 4) Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | |||||
| 5) Demonstrates individual and professional ethical behavior and social responsibility | |||||
| 6) Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | |||||
| 7) Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | |||||
| 8) Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | |||||
| 9) Displays computer proficiency to support problem solving and decision-making | |||||
| 10) Demonstrates teamwork, leadership, and entrepreneurial skills | |||||
| 11) Displays learning skills necessary for further study with a high degree of autonomy |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Has a broad foundation and intellectual awareness with exposure to mathematics, history, economics, and social sciences | H | Exam |
| 2) | Demonstrates knowledge and skills in different functional areas of business (accounting, finance, operations, marketing, strategy, and organization) and an understanding of their interactions within various industry sectors | S | Exam |
| 3) | Applies theoretical knowledge as well as creative, analytical, and critical thinking to manage complex technical or professional activities or projects | H | Project |
| 4) | Exhibits an understanding of global, environmental, economic, legal, and regulatory contexts for business sustainability | N | |
| 5) | Demonstrates individual and professional ethical behavior and social responsibility | H | Project |
| 6) | Demonstrates responsiveness to ethnic, cultural, and gender diversity values and issues | N | |
| 7) | Uses written and spoken English effectively (at least CEFR B2 level) to communicate information, ideas, problems, and solutions | N | |
| 8) | Demonstrates skills in data and information acquisition, analysis, interpretation, and reporting | N | |
| 9) | Displays computer proficiency to support problem solving and decision-making | N | |
| 10) | Demonstrates teamwork, leadership, and entrepreneurial skills | N | |
| 11) | Displays learning skills necessary for further study with a high degree of autonomy | N |
| Prepared by and Date | ÖZGÜR ÖZLÜK , December 2019 |
| Course Coordinator | UTKU KOÇ |
| Semester | Spring |
| Name of Instructor | Öğr. Gör. DICLE ASLAN |
Course Contents
| Week | Subject |
| 1) | Introduction to Marketing Analytics |
| 2) | Introduction to R |
| 3) | Introduction to R |
| 4) | Segmentation by Clustering |
| 5) | Segmentation by Clustering |
| 6) | Segmentation by Clustering |
| 7) | Predicting Customer Response: Logistic Regression |
| 8) | Predicting Customer Response: Logistic Regression |
| 9) | Predicting Customer Response: RFM Analysis |
| 10) | Developing Customers: Basket Analysis |
| 11) | Retaining Customers: Customer Lifetime Value |
| 12) | Retaining Customers: Churn Modeling |
| 13) | Presentations |
| 14) | Presentations |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | Marketing Analytics, W. L. Winston, 1st Edition, WILEY, 2014 | ||||||||||||||||||
| Teaching Methods | Lectures/contact hours using “flipped classroom” as an active learning technique | ||||||||||||||||||
| Homework and Projects | - Problems from textbook (they will not be collected and not graded, quiz questions will be very similar or identical to the problems). - A term project that covers all topics learned in this course. | ||||||||||||||||||
| Laboratory Work | - | ||||||||||||||||||
| Computer Use | - | ||||||||||||||||||
| Other Activities | - | ||||||||||||||||||
| Assessment Methods |
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| Course Administration |
asland@mef.edu.tr - Exams and quizzes: Closed book and closed notes. Homework: Problems from textbook will be given as extra course material (they will not be collected and not graded, quiz questions will be very similar or identical to the problems). Rules for attendance: YÖK regulations. Rules for late submission of assignments: N/A Missing a quiz: No make-up will be given for the missed quizzes. For certain excuses (decided by the instructor) the percentage of the missed quiz may be added to the midterm or to the final. Missing a midterm: You are expected to be present without exception and to plan any travel around these dates accordingly. Medical emergencies are of course excluded if accompanied by a doctor’s note. A note indicating that you were seen at the health center on the day of the exam is not a sufficient documentation of medically excused absence from the exam. The note must say that you were medically unable to take the exam. Provided that proper documents of excuse are presented, missed midterm by the student will be given the grade of the final exam. No make-up will be given. If you fail to take the exam on the assigned day and do not have a valid excuse, you will be given zero (0) on the exam. Employment interviews, employer events, weddings, vacations, etc. are not excused absences. Eligibility to take the final exam: YÖK regulations. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 1 | 70 | ||
| Homework Assignments | 14 | 2 | 28 | ||||
| Quiz(zes) | 4 | 3 | 1 | 16 | |||
| Midterm(s) | 1 | 20 | 2 | 22 | |||
| Final Examination | 1 | 18 | 3 | 21 | |||
| Total Workload | 157 | ||||||
| Total Workload/25 | 6.3 | ||||||
| ECTS | 6 | ||||||