Data-Driven Marketing (5 cr)
Code: MAR002AS2AE-3008
Basic information of implementation
- Enrollment
- 02.01.2025 - 10.01.2025
- Enrolment for the implementation has ended.
- Timing
- 13.01.2025 - 14.03.2025
- Implementation has ended.
- ECTS Credits
- 5 cr
- Campus
- Pasila Campus
- Teaching languages
- English
- Seats
- 30 - 100
- Degree programmes
- INTBBA International Business
- Teachers
- Maria Vickholm
- Groups
-
IBE5PAMARINTBBA, 5. semester, Pasila, Marketing and Sales
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CAMPUSONLINECAMPUSONLINE
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VIRTUALVirtual implementation
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INSTRUCTEDInstructed virtual implementation
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EXCHEXCH Exchange students
- Course
- MAR002AS2AE
Evaluation scale
H-5
Schedule
Will be defined later
Implementation methods, demonstration and Work&Study
Contact, blended, online
Demonstration (RPL): If you already have command of the course contents, you can demonstrate your competence. Ask the course teacher for more information. For more information on Recognition of Prior Learning: https://www.haaga-helia.fi/en/recognition-learning
Some of the self-directed learning that is part of the course takes place in third-party services according to the student’s own choice. The student is responsible for registering as a user of these services.
Intro
In this course, you will learn the fundamentals of modern and effective marketing through the study of the central concepts, practices and systems. You will gain an understanding of the possibilities of data-driven marketing in different channels and you will be able to plan targeted marketing that supports business objectives. You will learn how to make use of various metrics and their importance in agile marketing.
Materials
Chaffey, Dave & Ellis-Chadwick, Fiona, 2022. Digital Marketing 8th Edition.
Available as e-book in Haaga-Helia library.
Driscoll Miller, J, Lim, J. & Meerman Scott, D. 2020. Data-First Marketing : How to Compete and Win in the Age of Analytics. John Wiley & Sons, Incorporated.
Available as e-book in Haaga-Helia library.
Haimowitz, I.J., Verhoef, P.C., Kooge, E., Walk, N. & Wiering, N. 2022. Data analytics for business : lessons for sales, marketing, and strategy. Routledge. Second edition.
Järvinen, J. & Karjaluoto, H. (2015). The use of web analytics for digital marketing performance measurement. Industrial Marketing Management, Vol. 50, pp. 117-127.
Teaching methods and instruction
This is a virtual implementation. Students go through the course material and complete the assignments at their own pace and no lectures are offered with mandatory attendance.
Working life connections
Material and assignments from and for real case organizations.
Exam dates and re-exam possibilities
No exam, only assignments.
Internationality
All material, examples and assignments are created for international students and from a global perspective.
Completion alternatives
Work & Study or RPL
If you feel that you have already attained the competence level required for this course, such as through previous work experience, you can get the credits through Recognition of Prior Learning (RPL). For more information on either Work & Study or RPL, contact the responsible teacher.
Learning assignments
Aiming for grades 1-2 (individual work)
- Review of the material and related multiple-choice review (max. 50 points, to be taken at the end of the course)
- Self-reflection exercise (max. 10 points, to be completed at the end of the course)
Aiming for grade 3 (to be done individually)
- Review of the material and related multiple-choice review (max. 50 points, to be taken at the end of the course)
- Self-reflection exercise (max. 10 points, to be completed at the end of the course)
- Google Fundamentals of Digital Marketing certification (max. 10 points)
Aiming for grades 4-5 (to be done individually and in groups)
Individual work:
- Review of online material and related multiple-choice review (max. 50 points, to be completed at the end of the course)
- Self-reflection exercise (max 10 points, to be completed at the end of the course)
- Google Fundamentals of Digital Marketing certification (max. 10 points)
Group work:
- Data-driven marketing analysis of a case study (max. 30 points)
Assessment methods
Grading scale
90 - 100 points = 5
71 - 89 points = 4
61 - 70 points = 3
51 - 60 points = 2
40 - 50 points = 1