Duration
45h Th
Number of credits
| Master in sales management (120 ECTS) (work and study master) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Sales Analytics refers to the practice of generating insights from sales data, trends, and metrics to set goals and predict future sales performance.
In the Sales Analytics I course, a prerequisite for this one, students were introduced to the fundamental concepts of data collection and management, data visualization and dashboarding to summarize complex data sets, as well as to some descriptive techniques used in the field such as association analysis or (semi-)automated segmentation.
In this advanced course, divided into two main complementary parts, students are introduced to predictive and forecasting techniques.
Part I: Predictive Methods - Machine Learning/AI
Artificial intelligence systems allow to analyze and understand the collected sales data, to learn from it and to recommend actions (e.g. pricing strategies, predicting the next best offer, prioritizing opportunities according to their probability of success,...).
The sales manager of tomorrow must therefore understand the fundamentals of these techniques in order to be able to identify the analysis opportunities they allow, to evaluate the feasibility of a data mining project and to follow its implementation, to analyze the results such methods provide and to be able to critique them.
In this course, the fundamental concepts of predictive methods will be discussed. Several classical techniques (decision trees, regression,...) will be introduced and applied to real data sets. The focus will be on understanding the techniques and the interpretation and discussion of the results.
Part II: Sales forecasting
An effective sales forecast enables better decision making, risk reduction, sales quota alignment, better territory coverage and quota planning, the ability to focus a sales team on high revenue, high return sales pipeline opportunities, ... The use of data-driven predictive analytics reduces the impact of subjectivity and provides a solid forecast basis.
In this course, classic sales forecasting techniques will be discussed and applied to various data sets.
Learning outcomes of the learning unit
Part I:
Upon completion of this unit of instruction, the student will be able to:
- Understand and explain the fundamental principles of predictive methods
- Choose the most appropriate AI technique(s) to answer a problem
- Identify the limitations of the AI techniques used, their advantages and disadvantages
- Use an AI tool/module
- Interpret the results of predictive analysis
- Demonstrate critical thinking and analytical skills in monitoring a project using machine learning
Part II:
Upon completion of this unit of study, the student will be able to:
- Understand and explain the basic principles of forecasting
- Select the most appropriate forecasting technique(s) for a given data set
- Identify the limitations of the forecasting techniques used, their advantages and disadvantages
- Use a forecasting tool/module
- Interpret forecasts obtained with a forecasting model
- Use critical thinking and analytical skills when dealing with sales forecasts
Prerequisite knowledge and skills
The Sales Analytics I course is a prerequisite.
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 on business cases and Q/R session
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 )
Written work / report
Additional information:
The student will be evaluated on the following elements:
Case study 1: Predictive Methods
Case study 2 : Forecasting
Examination: written MCQ or short answer type, closed course.
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. Some class sessions will be dedicated to work on the case studies. However, a significant proportion of the work will also be carried out outside class sessions. Q/R sessions on appointment will enable students to receive personalized coaching for their work.
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 all course software installed.
Contacts
S. Aerts (stephanie.aerts@uliege.be)
M. Dumont (morgane.dumont@uliege.be)