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

Credits: 6

Schedule: 26.04.2023 - 07.06.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Antti Abel Vassinen

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

Professor of Practice Antti Vassinen: antti.vassinen@aalto.fi

Teaching assistant Mikaela Ebeling: mikaela.ebeling@aalto.fi (primary contact)


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:

    To address the increasing demand from employers for
    marketing professionals who are at ease with analysing (big)
    market data, this course provides students with basic tools of
    statistical analysis to support marketing decision making. The
    course covers different sources and forms of market data and
    provides practical tools for making simple analyses from such
    data. Practical exercises using SPSS and Microsoft Excel
    provide students with a chance to practice making relevant
    calculations in the context of marketing decisions, and business
    cases solved in class provide experience in solving real life
    business problems using market data. The course consists of
    lectures, practical exercises, cases, and home assignments
    (both individual and group assignments).

  • applies in this implementation

    Marketing professionals are increasingly required to be comfortable working with and analyzing large amounts of data. Companies have access to vast amounts of first party and third party data, and the ability to analyse this data effectively and reliably is increasingly important for marketing decision-making. 

    Driving growth and profitability requires a deep understanding of customer behavior, preferences, and needs, as well as the ability to measure the effectiveness and efficiency of marketing campaigns and make data-driven decisions. This course provides students with the fundamental tools and techniques for analyzing marketing data and using it to make informed marketing decisions.

    In addition to technical skills, this course also emphasizes the importance of practical application and problem-solving in a business context. Students will learn to approach marketing problems with a structured, data-driven approach, and to develop credible recommendations based on sound analysis. The applied parts of the course employ company data from mobile gaming, food, market research, and delivery services industries.

    Microsoft Excel is used for instruction. SPSS is demonstrated, and students are welcome to use it, R, Python or negotiable other tools if they so choose.


Assessment Methods and Criteria
  • valid for whole curriculum period:

    • Written exam • Group presentation • Cases • Individual assignments In order to pass the course, the student must pass at least 50 % of the exam.

  • applies in this implementation

    Individual exercises (30)

    Pair exercise (20)

    Exam (50)

    Feedback (bonus - 2)

    Students must achieve a pass (50%) on all required components.

Workload
  • valid for whole curriculum period:

    The course consists of lectures, practical inclass
    assignments, and home assignments.

  • applies in this implementation

    Activity h
    Lectures and live work20
    Readings30
    Individual exercises
    60
    Pair exercise25
    Final exam inc. preparation25
    Total160



DETAILS

Study Material
  • valid for whole curriculum period:

    Materials distributed in class.

  • applies in this implementation

    A list of required and suggested readings will be provided to students at the start of the course.

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:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring V
    2023-2024 Spring V

    Enrollment:

    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 MSc students in Business Analytics (4) Information and Service Management (5) Aalto BIZ exchange students, (6) other BIZ students.



  • applies in this implementation

    Lecture topics and exercises include:

    • Excel as a tool for data analysis in marketing
    • Summarising key features of market data
    • Visualising marketing data
    • A/B tests and experiments in marketing
    • Calculating ROI on marketing campaigns
    • Customer analytics (e.g. churn, LTV)
    • Creating managerial recommendations based on data analysis


Details on the schedule
  • applies in this implementation

    There are six contact sessions on Wednesday mornings, starting April 26th. They run from 9.15 AM to 11:45 (EET).

    There are no face-to-face lecture sessions. Teaching is exclusively over Microsoft Teams. If you wish to sit with other students, you are welcome to use hall U8 - U270 at Kandidaattikeskus. Lecture recordings will be available the same week for some sessions/parts, but not guaranteed for all content (e.g. company guests). Live attendance is not mandatory, but is strongly recommended. Attendance in the sixth session on May 31st is required.

    Each morning's lecture content is followed by a exercise session in small groups. There are an opportunity for you to work on the exercises in study groups and receive help from the instructor/assistant. These sessions are not recorded for privacy reasons.

    The individual assignments are presented on lectures 2, 3, and 4 and are due before the following week's lecture. The group (pair) assignment is given on lecture 5 and due for the final contact session (6), when randomly selected pairs will present their solutions.

    Changes to the planned order and content of lectures and exercises are possible.