IE 432 Marketing AnalyticsMEF UniversityDegree Programs Computer EngineeringGeneral Information For StudentsDiploma SupplementErasmus Policy Statement
Computer Engineering
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
Lecture: 3 Recitation: - Lab: - Other: -
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
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 Competences

Upon 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) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
3) An ability to communicate effectively with a range of audiences
4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Relation to Program Outcomes and Competences

N None S Supportive H Highly Related
     
Program Outcomes and Competences Level Assessed by
1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics H Exam,Project
2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors S Exam
3) An ability to communicate effectively with a range of audiences H Presentation,Project
4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts N
5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives H Project
6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions N
7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies. 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 ReadingsMarketing Analytics, W. L. Winston, 1st Edition, WILEY, 2014
Teaching MethodsLectures/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
Assessment Tools Count Weight
Quiz(zes) 4 % 20
Project 1 % 20
Midterm(s) 1 % 25
Final Examination 1 % 35
TOTAL % 100
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

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