Syllabus

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: 20.04.2022 - 25.05.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Tomas Falk

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.

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

  Important!

  Before 27. April 2022, all students need to complete: 

  1. Datacamp online-course 'Introduction to Python'. https://app.datacamp.com/learn/courses/intro-to-python-for-data-science
     
  2. A brief online-quiz covering the basics of statistics (link and more details regarding the assignment will be provided in the second week of April). If you need a stats-refresher I highly recommend these videos https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw

  Only students that successfully complete both of the above assignments are allowed to continue with the course!

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

  I will not be following the above textbook from Venkatesan et al.
  Instead I will largely use contents from these two sources. Not mandatory to buy these books but still strongly recommended. 

  1. Marketing Analytics by Wayne Winston, Wiley, 2014 (it's a great source for all sorts of marketing analytics techniques and you might even be able to find a free online-pdf floating around).
  1. Managerial Economics. No specific author recommended (though I like the one by Perloff & Brander), you may just pick any you can get for a reasonable price. Also past editions are fine. It provides the micro-economics foundation for marketing analytics. 

Substitutes for Courses

• valid for whole curriculum period:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  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.

Details on the schedule

• applies in this implementation

  Preliminary class-schedule (changes may occur)

  1. Introduction
     
  2. Predictive models to optimise pricing strategies (linear regression, profit function, elasticities)
  3. Predictive models to optimise marketing communication strategies (multilinear regression, dummy variables, logistic regression)
  4. Market tests and marketing experiments (ANOVA and interaction effects), Customer Lifetime Value
  5. Segmenting consumer preferences (Cluster-Analysis), Recommendation Systems
  6. Forecasting and Diffusion of Innovations

  
  

Description