Duration
36h Th
Number of credits
| Master in sales management (120 ECTS) (work and study master) | 4 crédits |
Lecturer
Substitute(s)
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 quotas 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. Essential notions of descriptive statistics will be taught.
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 descriptive 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
- group work sessions on business cases, Q/A sessions on appointment
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.
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Written work / report
Additional information:
Exam(s) in session
Any session
- In-person
written exam
Written work / report
Additional information:
The student will be evaluated on the following elements:
CASE STUDY: Data preparation & visualization, descriptive methods
The case study will be carried out in groups. A student whose participation in group work is deemed insufficient may nevertheless be penalized by a lower grade than the other members of his/her group.
The case study will draw on all or some of the concepts covered in the course. Work on the case study will be divided into three phases:
- Phase 1: [Creation of a dashboard to answer a managerial question] Class sessions will be dedicated to work on this phase. However, a significant part of the work will also be carried out outside class sessions.Q/R sessions on appointment will enable students to receive personalized coaching for this phase. The dashboard will be accompanied by a written report.
- Phase 2: [Presentation] Case studies will be presented orally in groups during class sessions, according to a pre-established schedule. The presentation will be followed by questions asked to each student individually.
- Phase 3: [Critique] Each group A will be assigned a paired group B, for which it will be asked to critically analyze the work submitted in Phase 1. To do this, Group A must attend Group B's presentation and ask pertinent questions. Group A will also receive Group B's dashboard and report, and will be asked to analyze it critically. This critical analysis will also be the subject of a report.
Important note: Group B's rating for phases 1 and 2 will be independent of the critical analysis carried out by Group A. In other words, Group A's assessment does not play a part in Group B's rating. Group A, on the other hand, will be assessed on the relevance, objectivity and accuracy of its critical analysis.
EXAM: written MCQs or short answers (calculation results), closed-course. The exam may include questions from case studies.
The final grade will be calculated as follows
Case studies (2/3)
Exam (1/3)
Work placement(s)
Organisational remarks and main changes to the course
All sessions require the use of a laptop with Office 365 installed. Other softares used in class have to be installed as soon as possible by the student.
https://www.campus.uliege.be/cms/c_14636926/en/microsoft-office-365-education
Contacts
S. Aerts (Stephanie.Aerts@uliege.be)
M. Dumont (Morgane.Dumont@uliege.be)