Additional resources
Additional resources
This MOOC equips participants with the knowledge to understand, evaluate, and apply AI technologies across marketing and business functions, enabling data-driven decision-making and responsible AI adoption.
Course description
Artificial intelligence has stopped being a future promise — it's now the edge that separates leading organisations from the rest. In every sector, companies are relying on AI and machine learning to get a sharper picture of their customers, spend their budgets more efficiently, and reach decisions with greater speed than ever. This course exists to prepare you to take an active role in that shift.
The programme offers a hands-on, business-first grasp of AI, with no need for a background in coding or maths.
We start by laying the groundwork. You'll look at where AI and machine learning sit within the wider world of business and marketing, and get familiar with the kinds of data that drive intelligent systems. Building on that, you'll learn to distinguish supervised from unsupervised learning, and understand why that difference shapes the business problems each approach is suited to solving.
From there, we look at AI through two essential lenses: that of the consumer on the receiving end, and that of the company putting it to use — so you can weigh up both the possibilities it opens and the responsibilities it demands. That sets up one of today's most pressing business discussions: fairness and bias in AI, and how companies can design systems that stay transparent, trustworthy, and equitable.
Once these fundamentals are established, the course shifts toward the real-world uses now transforming business. You'll see how AI drives customer segmentation, recommendation engines, and forecasts of customer lifetime value — making genuine personalisation possible at scale. You'll also learn how sentiment analysis and social listening turn scattered opinions into usable strategic insight, and how conversational AI and chatbots are changing the face of customer service and engagement.
Turning to the commercial and analytical dimension, you'll cover predictive analytics applied to churn, conversions, and campaign performance; machine-learning-driven marketing mix modelling; programmatic ad buying and bid optimisation; and attribution modelling — equipping you to measure impact, allocate spend wisely, and demonstrate the value your marketing and business efforts deliver.
This course is built for anyone ready to shift from watching the AI revolution unfold to actively taking part in it. No technical background is required — just curiosity, a drive toward better decision-making, and the ambition to guide your organisation confidently into a future shaped by data.
The course is divided into four weeks/modules, guiding participants through a structured learning path.
WEEK 1 - Foundations of Al & Machine Learning
- Introduction to Al/ML in a business context
- Data types and sources
- Supervised vs. unsupervised learning
WEEK 2 – Al in Business — Perspectives & Responsibility
- Al from the consumer perspective
- Al from the company perspective
- Ethics & bias module
WEEK 3 – Know Your Customer — Al-Powered Insights
- Customer segmentation
- Recommender systems
- Lifetime value prediction
- Sentiment analysis
- Conversational Al
WEEK 4 – Smarter Spending — Al for Marketing Performance
- Predictive analytics
- Marketing mix modelling
- Programmatic advertising
- Attribution models
Each module includes videos, video transcripts, interactive activities, infographics, readings, and self-assessment quizzes.
Total workload of the course: 50 hours
This MOOC was produced as part of the Edvance project – Digital Education Hub per la Cultura Digitale Avanzata. The project is funded by the European Union – Next Generation EU, Component 1, Investment 3.4 “Didattica e competenze universitarie avanzate".
Intended Learning Outcomes
By actively participating in this MOOC, you will achieve different intended learning outcomes (ILOs).
- Explain core AI/ML concepts and data foundations — articulate what AI and machine learning are, how they differ from traditional analytics, and evaluate the types and quality of data needed to support AI applications, including the distinction between supervised and unsupervised approaches.
- Evaluate AI's strategic and ethical implications for business — assess the opportunities and operational challenges AI presents for organisations, anticipate how AI-driven interactions shape consumer experience, and apply ethical frameworks to identify bias and evaluate fairness and transparency in AI-driven decisions.
- Apply AI-powered techniques to understand and engage customers — use segmentation, recommender systems, and customer lifetime value models to inform targeted engagement, retention, and investment decisions.
- Extract insight from customer-generated data — apply sentiment analysis, social listening, and conversational AI (chatbots) to interpret unstructured customer data and identify appropriate deployment use cases in service contexts.
- Use predictive and quantitative models to optimise marketing performance — apply predictive analytics to anticipate churn, conversion, and campaign response, and use marketing mix modelling to quantify channel contribution.
- Assess AI-driven media and measurement strategies — describe how programmatic advertising and bid optimisation function, and compare attribution models to select appropriate approaches for measuring marketing effectiveness.
Prerequisites
No prerequisites are required to participate in this course.
Activities
Throughout the course, you will find activities to help consolidate your understanding, to practice translating abstract concepts into practical applications in the real-world, and to hone your skills in assessing AI tools and applications with a critical eye and from a managerial perspective. Furthermore, at the end of each week, you will encounter a Reflection Point.
Assessment
Your final grade for the course will be based on the results of your answers to the assessed quizzes. You have an unlimited number of attempts at each quiz, but you must wait 15 minutes before you can try again. You will have successfully completed the course if you score 60% (or higher) in each one of the assessed quizzes. The maximum score possible for each quiz is given at the beginning of the quiz. You can view your score in the quiz on your last attempt or on the 'Grades' page.
Certificate
You can achieve a certificate in the form of an Open Badge for this course, if you reach at least 60% of the total score in each one of the assessed quizzes and fill in the final survey.
Once you have completed the required tasks, you will be able to access ‘Get the Open Badge’ and start issuing the badge. Instructions on how to access the badge will be sent to your e-mail address.
The Badge does not confer any academic credit, grade or degree.
Information about fees and access to materials
The course is delivered in online mode and is available free of charge.
Course faculty
Michele Russo
michele.russo@sdabocconi.it
Fellow, SDA Bocconi School of Management
Recent publications
RUSSO M., PRIX S., GOERGEN J., DE BELLIS E., The 3 Types of Customers Who Buy Smart Products—and How to Market to ThemHarvard Business Review, 4 Novembre, 2025
GABBI G., TONINI D., RUSSO M.A Novel Supervised-Unsupervised Approach for Past-Due PredictionRisk Management Magazine Aifirm, 2024, vol.19, no. 02, pp.4-21
CASELLI S., GABBI G., DE ROSSI L., ABBATEMARCO N., RUSSO M., MORETTI S., For a digital euro that citizens will embrace - Per un euro digitale che piaccia ai cittadini2025, SDA Bocconi Insight, Milano, Italia
TAVA L. V., RUSSO M.IDRO - Negotiation Exercise2023, The Case Centre, Gran Bretagna
Contact details
If you have any enquiries about the course or if you need technical assistance please contact pok@polimi.it. For further information, see FAQ page.