Sales Analytics Part I Data Management

Duration

36h Th

Number of credits

Lecturer

Stéphanie Aerts, Morgane Dumont

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

Sales Analytics is the practice of generating information from sales data, trends and indicators in order to set targets and forecast future sales performance. Data analysis can be used to inform many of the decisions a sales manager needs to make: quantifying the actions that differentiate the best salespeople from the worst performers, planning quotas and targets, obtaining accurate forecasts to plan effective territory coverage, identifying the most promising deals, etc.

Part I: Data collection and management
Any data analysis starts with a good preparation of the data. In this course, students will become familiar with best practices for managing and preparing data (formatting, handling missing values, outliers,...)

Part II: Data visualization
To decipher, consolidate and synthesize data from a variety of channels, and thus inform decision-making, data visualization has become
an essential solution. In this course, students will learn how to create relevant visualizations, depending on the information sought. The essential notions of descriptive statistics will be taught.

Part III: Advanced data analysis
In this section, the sales managers of tomorrow will learn to use more complex techniques of descriptive analysis such as association analysis or (semi-) automated segmentation. The common statistical tests will be introduced: independence test, ANOVA, as well as linear regression.

Part IV: Sales forecasting
An effective sales forecasting process enables better decision-making, risk reduction, sales quota alignment, better planning of territory coverage and quotas, the ability to focus a sales team on high-revenue, high-return sales pipeline opportunities, etc. The use of data-driven predictive analysis reduces the impact of subjectivity and provides a solid foundation.

In this section, classical sales forecasting techniques will be discussed and applied to various datasets.

The software used for this course is Excel.

Learning outcomes of the learning unit

Part I: Data prep
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: Data visualisation
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 appropriate plots.

Part III: Advanced data analysis
At the end of this course, the student will be able to
- Understand and explain the fundamental principles of the descriptive methods taught

- Recognize opportunities for using descriptive algorithms

- Identify the relevant technique to address a given problem

- Identify the limitations of the techniques used, their advantages and disadvantages

- Interpret the results obtained using a descriptive method

- Demonstrate critical thinking and analytical skills in the implementation of a project using the taught techniques.


Part IV: Sales Forecasting
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

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

Course materials and 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 )


Further information:

Further explanation:

The student will be assessed on the following:

• A practical examination on a computer with an open course. During this part, understanding of the concepts, interpretation of the results, choice of tools and justifications will be assessed.
• A written exam of the MCQ or short answer type (results of calculations), with a closed course.

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)

Association of one or more MOOCs