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Artificial Intelligence and Marketing
Artificial Intelligence and Marketing
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.
Section outline
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In week 1, we start at the beginning. In this first module, we place AI and machine learning in their proper business context, cutting through the hype to give you a grounded picture of what these technologies can and cannot do. You will leave with a vocabulary and a mental model that will serve you throughout this course and beyond.
From there, we turn to data — the raw material that makes AI possible. Not all data is created equal, and knowing the difference between data that is rich with signal and data that will lead your models astray is one of the most practical skills a business professional can develop.
We close the module by exploring the two fundamental ways machines learn: supervised and unsupervised learning. These are not abstract concepts — they map directly onto the kinds of questions your business asks every day, and understanding them will help you recognise which approach is right for which problem.
By the time you finish this module, you will have the foundations to engage with AI not as a bystander, but as someone who genuinely understands what is happening under the hood — and why it matters.
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Now that you understand how AI learns, in Week 2 we ask a more human question: what does it mean to actually use it?
AI does not exist in a vacuum. Every algorithm that recommends a product, scores a lead, or decides who sees which advertisement has a real person on the receiving end — and a real organisation accountable for the outcome. Week 2 of our course is about understanding both sides of that equation.
We begin by stepping into the shoes of the consumer. How does it feel to be targeted, personalised, and predicted? What builds trust, and what erodes it? Understanding the customer experience of AI is essential for anyone who wants to deploy it responsibly and effectively.
We then shift to the organisational perspective. What does it take to implement AI successfully? What are the strategic opportunities, and where do companies most commonly stumble? This module gives you a realistic picture of AI adoption — the promise and the complexity.
We close with what may be the most important conversation in this entire course: ethics and bias. AI systems learn from historical data, and history is not always fair. Left unchecked, AI can automate and amplify existing inequalities at extraordinary scale. We will examine where bias enters the system, what the consequences can be, and what responsible organisations are doing about it.
This section of the course will not make you a compliance officer or an ethicist, but it will make you the kind of business professional who asks the right questions — and that matters more than you might think.
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The greatest promise of AI in business is also the most personal: the ability to understand each customer as an individual, at a scale no human team could ever achieve alone.
For decades, businesses have segmented their customers, tracked their sentiment, and tried to predict their next move. AI does not replace that ambition — it supercharges it. In Week 3 of the course, you will explore five of the most powerful ways machine learning is transforming the customer relationship.
We begin with segmentation — not the broad demographic buckets of the past, but dynamic, behaviour-driven groupings that reflect how customers actually think and act. From there, we move to recommender systems, the engines behind "you might also like" that have become one of the highest-value applications of AI in commerce.
Next, we explore customer lifetime value prediction — helping you look beyond the next transaction to understand the long-term worth of every relationship your business builds. We then turn to sentiment analysis and social listening, where AI reads the vast, unstructured conversation happening around your brand and distils it into insight you can act on.
Week 3 closes with conversational AI and chatbots — tools that are redefining the front line of customer service and engagement, and raising new questions about where automation ends and human connection must begin.
Across all four weeks of this course, the theme is the same: AI does not just process customer data - when used well, it helps you serve people better.
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Understanding your customer is only half the challenge. The other half is knowing whether what you are doing about it is actually working — and making every pound, euro, or dollar of your budget work harder.
This final chapter is about performance. It is where the strategic and ethical foundations you have built come together with hard commercial reality: pipelines, budgets, channels, and returns. AI is transforming how organisations measure, optimise, and justify their marketing investment, and this chapter gives you the tools to lead those conversations.
We open with predictive analytics — using machine learning to anticipate customer behaviour before it happens. Which customers are about to leave? Which prospects are most likely to convert? Which segments will respond to your next campaign? Prediction, done well, turns reactive marketing into proactive strategy.
From there, we explore marketing mix modelling — a powerful technique that uses AI to untangle the contribution of every channel and activity to your overall results. In a world of fragmented media and shrinking attention, knowing what is genuinely driving growth is a significant competitive advantage.
We then examine programmatic advertising and bid optimisation — the largely invisible AI systems that now govern much of the digital advertising ecosystem. Understanding how they work will make you a sharper commissioner of media and a more informed partner to your agencies.
We close with attribution modelling — the question of how you assign credit for a sale or conversion across all the touchpoints that contributed to it. It is one of the most debated topics in marketing, and machine learning is finally giving us better answers.
By the end of this chapter, you will be equipped not just to use AI for performance marketing, but to evaluate it critically — asking the right questions, challenging the right assumptions, and making decisions with genuine confidence.
This is where it all comes together. Let's finish strong. -
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Video transcripts Folder
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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.