LEARNING OUTCOMES
You are confident in utilising numerical data to support
marketing decisions and have the basic skills for collecting
and analysing such data.
After completing the course you will be able to:
• Collect and analyse diverse types of market data,
• Produce simple analytical reports,
• Critically evaluate market reports produced by external
parties,
• Utilize simple data analysis and market reports effectively to
support marketing decisions.
Credits: 6
Schedule: 23.04.2024 - 03.06.2024
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 Joonas Määttä: joonas.p.maatta@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 work sessions 20 Readings 30 Individual exercises 60 Pair exercise 25 Final exam inc. preparation 25 Total 160
DETAILS
Study Material
valid for whole curriculum period:
Materials distributed in class.
Naresh K. Malhotra: Marketing Research - An
Applied Orientation, 7th edition, Pearson.applies in this implementation
No textbook. A list of required and suggested readings will be provided to students at the start of the course.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
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 VEnrollment:
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) Aalto BIZ exchange students, (5) 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 lectures on Tuesday afternoons, starting April 23rd. They run from 15:15 to 17:00 (EET).
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. An exception is the sixth session on May 28th, when attendance impacts your score.
Each Tuesday's lecture content is followed on Thursday by a virtual exercise session in small groups. These 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.
There are no face-to-face 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.
The individual assignments are given on lectures 2, 3, and 4 and are due for the following week's lecture. The pair assignment is given on lecture 5 and due for the final lecture, when randomly selected pairs will present their solutions.
Changes to the planned order and content of lectures and exercises are possible.