Store digitalisation

Objectives of this course are:

• understanding the most innovative technologies that can be introduced in a physical store.
• understanding how those technologies contribute to the omnichannel retail strategy,
• improve the customer’ shopping experience and therefore improve the retail company results.
• understanding the KPIs to measure the ROI of new technologies for a retail company

Strategising: new online business models

One of the most popular questions for startups online (and even major ones like Twitter and Facebook) is ‘what is your business model’. Sometimes the question is sincere but often it is voiced more like an accusation: "what the hell is their business model anyway?" The aim of this course is to give a general understanding of the digital business models and of their characteristics (Brokerage, Advertising, Infomediary, Merchant, Manufacturer, Affiliate, Community, Subscription, Utility).

Consumer behavior and identity in a digital world

Students will learn about how such traditional activities as search, shopping, buying and even ostentatious consumption have changed as a result of the transition to the digital environment. Further you will look into the exercise of consumer sovereignty via rating sites and how this affects the evaluation process.

Ethics in data driven world

The course is mainly based on case studies of ethical and unethical behaviour related to digital technologies in marketing, touching issues of privacy, data protection. The introduction  provides a framework for assessing the "ethicallity" of business practice. Students will use this to study real world case studies that they will be asked to find and document themselves.

The main "learning outcome" will be an enhanced capacity for students to

• detect ethical problems related to digital technologies and data sciences
• formulate grounded judgement concerning the ethical status of practice
• devise and implement strategies meant to prevent ethical disasters

Data science and text mining for marketing analytics with Python

• Python CSV data file management review, intro to Pandas
• Pandas main functionalities
• Pandas, GroupBy, TimeSeries, and String forms of data
• Merge, Join and SQL data in Python
• Text data: word counts and part of speech
• NLTK, tf‐idf, gensim, …
• Sentiment analysis
• Clustering topic modeling

You will select 2 electives out of the 3 following courses :

• CRM, data platforms & marketing automation
• Data visualization
• Machine Learning & business cases using Python

CRM, data platforms and marketing

Digital customer relationship management is the use of internet communications channels and technologies to enhance customer relationship management (CRM) and customer experience management (CEM) initiatives.

An important goal of digital CRM is not only to provide an organisation with a clear picture of each customer's habits and preferences, but also to make personalisation and automated messaging easier.

Data visualisation

• Principles of dataviz
• Panorama of tools and methods
• Tableau: features and exemplar case studies
• Importing data in Tableau
• Data cleaning
• Charting numeric / textual / graphical data in Tableau
• Developing a storytelling with Tableau

Machine learning and business cases using Python

Lectures on several themes:

• The learning Problem, Elementary algorithms (kNN, Naïve Bayes, Apriori)
• Learning theory (how can a computer learn from data)
• Linear models 1, Linear models 2 (nonlinear transforms, logistic regression).
• Tree based methods (decision trees, random forests, boostings)
• Regularisation and validation
• Unsupervised learning (principal components, clustering methods) & support vector machines
• Social networks and communities
• Neural networks and deep learning