Customer and market analytics
Learn to take advantage of available data today by acquiring marketing skills for the next decade. This course teaches you how to understand and employ analytics to improve your business results.
WHY customer and market analytics?
Never before has managers had this much information on their customers, partners and competition at disposal to make informed and smarter decisions. Yet they are more than ever criticised about the lack of actionable insights that derive from it.
In this course you learn how to increase the effectiveness of marketing decisions. We use an “Explain-Show-Do-Practice” approach to teaching that encompasses explanations in the lectures followed by a combination of class discussion, case study analysis and practical hands-on exercises in Excel.
We do not go deeply into the statistics and mathematics behind the methods and tools, but rather provide you with an understanding of what the model could be used for, intuitively how it works, which data you need to have and how to evaluate the output you get from the analyses.
TOPICS IN THIS COURSE INCLUDE:
- Analysing and visualising your customer and product data
- Understanding and segmenting customers and markets, identifying high value future customers
- Exploring your brand positioning against competition and devising potential actions to improve it
- Implementing hands-on tools to develop new products and calculate how much customers are willing to pay for the new or changed product or service
- Developing and measuring effectiveness of loyalty programmes, marketing campaigns, multiple-channels and social media investments
- Evaluating how to optimally allocate resources across different products, channels and sales representatives
WHO IS THIS PROGRAMME FOR?
To those reading analytic reports:
To those who do analytics:
This course is 15 credits and is conducted through a combination of campus and online learning process. The campus module will consist of 3 sessions x 2 days. The online module will combine online content, videos and exercises to support the learning, both individually and in groups. The combination of campus and online learning process equals 75 lecturing hours over one semester.
Please note that while attendance is not compulsory in all courses, it is the student's own responsibility to obtain any information provided in class that is not included on the course homepage or other course materials.
In each session, students will have an opportunity to learn hands-on different tools in Excel software with add on modules. In-class and online exercises will include working on available datasets and case study datasets.
Participating in the course will require access to a pc/laptop with Microsoft Excel installed.
Read the admission requirements for this programme.
There are no requirements for prior knowledge beyond the admission requirements.
Basic English skills are recommended.
The students are evaluated through an individual home assignment and a (final) term paper. The term paper accounts for 60% of the total grade, and may be written individually or in groups of maximum three people.
The 72-hour individual home exam accounts for 40% of the total grade and consists of two parts which are submitted at the same time. The sum of the grades (0-100 points) from the two parts will contribute to the total grade of the 72-hour individual home exam. Student will submit both parts in one PDF document within the exam deadline given.
Part I: Solution to at least three (3) case-studies throughout the course (30%): in online exercises throughout the course students are asked to solve case studies and provide 1-2 Power point slides with their proposed solution. A student should select 3 of his/her solutions uploaded to Insendi and include them in a PDF document (template provided at Insendi platform of this course).
Part II: Self-reflection on learning outcomes (10%): In this short written assignment, students will analyse their learning from the online activities and reflect on how they have applied this learning in practice (or their thoughts about how it could be applied).
Both the term paper and home assignment are based on the application of the concepts and tools learned in the course and provides an opportunity to implement the learned skills on your firm’s data in the final project.
Teaching language is English.
A syllabus is available in the student portal for applicants who have been admitted to the programme. Username and password for the student portal will be sent to you before the start date.
The tuition fee is normally charged in two instalments, 1-2 weeks after the start of each semester. You will find the invoice under My invoice in the student portal.
See Tuition Fees at BI Norwegian Business School for complete overview.
The tuition fee includes all compulsory syllabus and licenses.
Matilda Dorotic is an Associate Professor in the Department of Marketing at BI. Her research interests include customer engagement, customer value management, effectiveness of rewarding and loyalty programmes. Recently, she has also focused on how leaders can analyse the effect of big data and social media, new ways of looking at customer lifetime value and ROI in marketing.
Oslo: Spring 2023
Module 1: 23.02 - 24.02
Module 2: 30.03 - 31.03
Module 3: 27.04 - 28.04
We reserve the right to make changes to the schedule.