23C60500 - Marketing Analytics, Lecture, 13.9.2021-27.10.2021

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Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

The student understands how data about customers and the firm s marketing activities can be collected and leveraged to improve marketing productivity and organizational performance. The student understands the statistical and mathematical principles underlying modern marketing analytics tools. The student knows how to apply various techniques in the marketing analytics toolkit in order to harness actionable insights from data concerning the firm s customers, products, marketing communications and pricing.

Credits: 6

Schedule: 13.09.2021 - 27.10.2021

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Kushagra Bhatnagar

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content

  • valid for whole curriculum period:

    The course provides an overview of the applications of marketing analytics in organizations, and covers concepts and tools such as return on marketing investment, customer lifetime value, price and advertising elasticity, marketing experiments, and applications of machine learning to marketing.

  • applies in this implementation

    Over six weeks, the course will cover the following themes:
    • Marketing ROI
    • Causal inference in marketing (A/B testing and experiments)
    • Marketing mix modeling
    • Customer analytics
    • Machine learning for marketers


Assessment Methods and Criteria

  • valid for whole curriculum period:

    Independent assignments: 40 %

    Exam: 60 %

     

    Spring 2021:

    Independent assignments: 40 %

    Quizzes: 60 %

  • applies in this implementation

    Coursework:
    • 5 Independent exercise sets (40% of your grade). Exercise sets will be published on Tuesdays and will be due on the following week's Thursday. There will be no exercise set published on the final week of the course.
    • Final exam (60% of your grade). The exam will be held online.



Workload

  • valid for whole curriculum period:

    Online course:
    6 credits, 160 hours:
    * Independent study (including video lectures)
    * Exam (3 h)

    Lecture-based course:
    6 credits, 160 hours:
    * Lectures
    * Exercises
    * Individual assignments
    * Exam preparation
    * Exam (3 h)

    Spring 2021:

    6 credits, 160 hours:
    * Lectures
    * Exercises
    * Individual assignments
    * Exam preparation
    * Quizzes

DETAILS

Study Material

  • valid for whole curriculum period:

    Venkatesan, R., Farris, P., & Wilcox, R. T. (2014). Cutting-edge marketing analytics: real world cases and data sets for hands on learning. Pearson Education.

    Recommended. (Availability)

    Additional reading assigned assigned by the professor. 

  • applies in this implementation

    Venkatesan, R., Farris, P., & Wilcox, R. T. (2014). Cutting-edge marketing analytics: real world cases and data sets for hands on learning. Pearson Education.

    Not Obligatory (Availability)

    Additional reading assigned assigned by the professor. 


Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    10 Reduced Inequality

    16 Peace and Justice Strong Institutions

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    The number of students admitted to the course is restricted to 80. Priority is given to (1) Aalto BSc Marketing students , (2) Aalto MSc Marketing students, (3) Aalto BIZ exchange students, (4) other BIZ students.


    NB: The course is arranged twice per academic year, once as an online course, and once as a lecture-based course. While the online course will include video lectures and optional in-person exercise clinics, it nevertheless places added emphasis on students' capability to cover the material independently.


    Teaching Period:

    2020-2021 Autumn II (online course), Spring V (online course)
    2021-2022 Autumn I (online course), Spring V (lecture-based course)


    Course Homepage: https://mycourses.aalto.fi/course/search.php?search=23C60500


    Registration for Courses: In the academic year 2021-2022, registration for courses will take place on Sisu (sisu.aalto.fi) instead of WebOodi.

  • applies in this implementation

    Software used in the course
    We will be using R Studio to work on the exercises (note: no prior experience in R or other programming languages is required or expected of you!)
    We will primarily be using Microsoft Teams for video lectures, study groups, and communicating throughout the course. Once the registration period for the course is over, accepted students will receive a link to the Teams course environment.

    Given the current situation, flexibility is naturally the name of the game. We may (and likely will!) adapt the above course structure as the course progresses and we find out what works well and what doesn’t.

Details on the schedule

  • applies in this implementation

    Course schedule:

    • Online lectures on Tuesdays 15:15-17:00.
    • Online exercise sessions on Thursdays 15:15-17:00.


Rules of Conduct - Students´ Rights and Responsibilities (in force from 1 August 2020)

Aalto University Code of Academic Integrity and Handling Violations Thereof