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 course aims at providing students with both theoretical understanding and practical competence to use digital analytics to improve marketing decision making. Students learn how to select relevant metrics against distinct business goals and customer lifecycle stages. They also learn how to interpret the metrics data so that they can identify bottlenecks in web and mobile experience and execute actionable experiments aimed at removing the bottlenecks. Furthermore, students learn about the use of analytics tools in both big and small companies, specialties of enterprise analytics deployments, and get an understanding on how to select the right analytics tool for a given job.

marketing analytics

performance measurement

metrics selection

marketing automation

data-driven decision making

conversion optimization

multi-channel data analysis

search engine optimization

data visualization

regulations and ethics in data analytics

 

Credits: 6

Schedule: 30.05.2022 - 02.09.2022

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Victor de Bruin

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:

    Key topics: 

    marketing analytics

    performance measurement

    metrics selection

    marketing automation

    data-driven decision making

    conversion optimization

    multi-channel data analysis

    search engine optimization

    data visualization

    regulations and ethics in data analytics

Assessment Methods and Criteria
  • valid for whole curriculum period:

    In-class exercises
    Group assignments
    Individual assignments

Workload
  • valid for whole curriculum period:

    Total 160 h (6 ECTS)
    Lectures: 42 h
    Independent and team work: 118 h
    Compulsory attendance, max. 3 absences

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    8 Decent Work and Economic Growth

    9 Industry, Innovation and Infrastructure

    12 Responsible Production and Consumption

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Summer
    2023-2024 Summer

    Enrollment :

    Registration for courses will take place on Sisu (sisu.aalto.fi). Course available as a part of the Information & Service Business track of the Information Technology Program. Application to the program through itp.aalto.fi.