Duration
36h Th
Number of credits
| Master in sales management (120 ECTS) (work and study master) | 4 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Sales Analytics is the practice of generating insights from sales data, trends and metrics to set goals and forecast future sales performance. Data analytics informs multiple decisions that a sales manager must make: quantifying the actions that differentiate top sales reps from bottom performers, planning qutoas and targets, obtaining accurate forecasts to plan effective territory coverage, identifying the most promising deals,...
Part I: Data collection and management
All data analysis starts with good data management. In this course, students will learn about good practices for data collection, management and preparation.
Part II: Data visualization
To decipher data from different channels, consolidate and synthesize it and thus inform decision-making, data visualization has become an essential solution. In this course, students will learn how to implement dynamic data summaries using a business intelligence software. Through hands-on exercises, they will learn to master the essential features to easily build complete and detailed views of complex data. A few reminders of descriptive statistics will be given.
Part III: Descriptive methods
In this part, tomorrow's sales managers will learn to use more complex descriptive analysis techniques such as association analysis or (semi-) automated segmentation.
Learning outcomes of the learning unit
Part I:
Upon completion of this unit of instruction, the student will be able to:
- Identify data sources
- Consolidate, manipulate and clean data
- Collect and manage a database in an efficient and usable manner
Part II:
Upon completion of this unit, the student will be able to:
- Choose an appropriate representation for a phenomenon to be analyzed
- Analyze business data by building dashboards and mastering a business intelligence tool
Part III:
At the end of this course, the student will be able to
- Understand and explain the fundamental principles of the descriptive methods taught
- Recognize the opportunities of using predictive algorithms
- Identify the limitations of the techniques used, their advantages and disadvantages
- Use a data analysis tool/module
- Interpret the results obtained using a descriptive method
- Demonstrate critical thinking and analytical skills in the follow-up of a project using machine learning
Prerequisite knowledge and skills
Basic notions in statistics
Planned learning activities and teaching methods
The learning sessions will be of several kinds:
- lectures interspersed with exercises of direct application of the concepts
- practical demonstration on software
- questions/answers on exercises to be prepared outside the session
- group work on business cases
For some parts of the course, a flipped classroom approach will be used: students will have to prepare the material through readings, exercises or watching videos/tutorials. During the next session, a question and answer session will be organized on this preparation, followed by the application of the prepared concepts.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face to face
Recommended or required readings
All required documents will be posted on lola.
Assessment methods and criteria
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire )
Written work / report
Additional information:
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire )
Written work / report
Additional information:
The student will be evaluated on the following elements:
Case study 1 : Data visualization
Case study 2 : Descriptive methods I
Case study 3 : Descriptive methods II
Examination: written MCQ or short answer type, closed course.
The case studies will be done in groups, partly during class sessions. The presence of the student is therefore required at each class session. The case study sessions require preparation beforehand (review of the material, individual exercises). A student whose participation in group work is not deemed sufficient (repeated absences, lack of investment, lack of preparation) may be penalized by a lower grade than the other members of his/her group.
In the second session, group work will be replaced by individual work.
The final grade will be calculated as follows
Case studies (70%)
Exam (30%)
Work placement(s)
Organizational remarks
All sessions require the use of a laptop.
Contacts
S. Aerts (stephanie.aerts@uliege.be)