AI-Driven Customer Insights & Personalization
Start
Index
What we'll talk about this session.
From marketing to machine learning
What AI-driven insights really mean
How AI understands customers
Why personalization matters now
The marketer’s new toolkit
Data, privacy, and trust
Individual to organisational learning
Case Studies
Understanding AI-Driven Customer Insights
For decades, understanding customers depended on a mix of intuition and quite some limited data. Marketers read survey answers, watched focus groups, and checked monthly sales sheets. The insights were slow, partial, and mostly backward-looking. AI changed the rhythm. Instead of analysing what happened, we can now anticipate what will happen. Algorithms process billions of clicks, searches, and purchases, revealing relationships no human analyst could spot. AI doesn’t replace curiosity but it scales it. Where one researcher might notice that people who buy avocados often add lemons to their basket, AI finds thousands of similar patterns across products, every hour of the day.
What “AI-driven insights” really mean
At its simplest, insight means understanding why people behave the way they do. When we refer to "AI-driven insights," we mean use machine learning to find the "why" through data, instead of conjecture.
What “AI-driven insights” really mean
Detect patterns
They cluster customers with similar behaviours.
Predict outcomes
They estimate the probability of future actions (repurchase, churn, clicks).
Optimise decisions
They recommend the best next step (which message to send, when, and through which channel)
It's a dynamic insight, the system is always learning. The model is incrementally altered with each new purchase or search, resulting in more accurate forecasts in the future.
From hindsight to foresight
Traditional analytics worked like a rear-view mirror: they were valuable, but always late. You’d finish a campaign, then run a report. AI turns that mirror into a GPS. It reads signals in real time and adjusts while the journey is happening. Imagine a food-delivery app. Old analytics would show how many users ordered pizza last Friday. Now AI can predict who’s likely to order again this Friday and even tell at what time or which cuisine they’ll choose. Our role as marketer shifted from reporting results to steering outcomes.
Campaigns develop into dynamic systems that don't have to start from scratch every time: they learn, adapt, and self-optimize.
How AI “understands” customers
Several techniques work together:
Recommendation engines
Clustering / segmentation
Use collaborative filtering to suggest products others with similar profiles liked.
It groups people that have similar behaviour patterns, not demographics.
Predictive analytics
Natural-language processing
Estimates probabilities for each individual as purchase, cancellation, click-through or even upgrades.
Or NLP. It reads reviews and social comments to detect sentiment and emerging topics (in a fastest way).
Why personalization matters now?
McKinsey research shows that over 70 % expect brands to tailor interactions, and most feel frustrated when that doesn’t happen. Personalisation used to mean adding a name in an email but not anymore. Now it’s adjusting the entire experience, and this can be from the landing page layout to the product sequence and price promotions, for each and every user. But what makes AI-based personalisation distinct is its behavioural foundation. It looks at what people do, not just who they are. Two customers of the same age and income can behave completely differently online, AI treats them accordingly.
Why personalization matters now?
Well-designed personalisation serves three purposes:
- Convenience. We spend less time in searching, and to get to quicker decisions.
- Connection. Perfeption is key, we offer a sense that the brand “gets me”.
- Performance with higher conversion and loyalty from our customers.
Here, the big risk is over-personalisation. When messaging becomes too specific, people feel watched so create relevance with distance: be helpful, but never too invasive.
What do we want to predict?
Designing data questions
The marketer’s new toolkit
What data should we not use?
Choosing ethical boundaries
AI adds speed and scale, but humans still frame the questions. The marketer’s job now includes:
Which insights deserve creative action?
Interpreting patterns
Turning probabilities into campaigns.
Telling the story
Automation handles the heavy lifting but the strategy and the empathy always remain human.
Data, privacy(?) and trust
Within the EU, GDPR defines clear limits. Customers must give consent, know what’s collected, and have the right to explanation. They have the right to ask why a recommendation appeared, and here transparency is the price of relevance. In another point, algorithms learn from existing data, and if that data excludes certain groups, the system will too. Responsible personalisation means continuous monitoring and diversity in data sources.
From individual actions to organisational learning
Organizational intelligence is the true benefit of AI, not just micro-personalization. The entire company learns more quickly when insights are looped back into service, logistics, and product design.
- Retailers adjust stock before shortages
- Banks spot fraud patterns within minutes
- Media platforms adapt content suggestions continuously
AI can help turn marketing from a communication department into the nervous system of the company (if you used it the right way).
Case Studies
Sonae MC / Continente AI for Smarter Segmentation
SONAE MC, which is the big group behind Continente supermarket, uses AI to analyse millions of daily transactions through their Cartão Continente. Instead of grouping consumers based on age or geography, machine-learning models group them according to their lifestyle and purchasing habits. They can also spot shifts (such as a cliente change their preference for baby food) that signify different stages of life.
- Loyalty and purchase data feed predictive models for timing and relevance
- Campaigns became less frequent, but more accurate
Learn more
Case Studies
Spotify and their predictive personalization
If you use Spotify, you know what the app does. Spotify’s recommendation system analyses listening behaviour. This can go from plays to skips, saves or timing to build dynamic profiles adjusted to the user's musical taste. It compares millions of users to predict what each will enjoy next, generating playlists such as Discover Weekly and Daily Mix.
- Personalization is based on behaviour
- Algorithms are feed with fresh data
- Introduces “serendipity” to avoid monotony
Learn more
Case Studies
Zalando and the fashion personalization & predictive fit
Zalando created the Algorithmic Fashion Companion to reduce returns and improve experience. The system analyses browsing history, purchases, returns, and price sensitivity to predict which items customers will keep.
- Suggests full outfits and correct sizes
- Detects colour and style trends from social media to guide collections
Case Studies
Netflix recommendationand retention
Netflix’s recommendation engine personalises both what you see and the artwork you see it with. Algorithms build taste clusters from viewing habits and test multiple thumbnails to learn which image each viewer clicks.
- Predicts the next best show
- Identifies churn risk early
- Over 80 % of watched content comes from recommendations (*Wired)
Learn more
Case Studies
Sephora Europe and AI-Powered beauty personalization
Sephora combines purchase data, browsing patterns, and selfie analysis to tailor product suggestions. Its Color iQ and virtual try-on features match skin tone and texture through computer vision.
- Instant shade recommendations
- Chatbots offer personalised advice 24/7
Case Studies
H&M and AI forinventory and style curation
H&M’s AI department analyses sales, returns, and weather data to decide store assortments and forecast demand. Social media image analysis detects emerging fashion trends before buyers do.
- Reduced over-stocking and waste
- More relevant local inventory and online recommendations
Responsible AI
Learn more
Ethics and Strategic Reflection
AI gives marketers unprecedented control, and equal responsibility.
We've talked about this previously but transparency and consent are crucial under GDPR. Your clients must know why they see a particular communication. Now, every team needs frequent audits and diverse training data, since algorithms can easily reproduce bias if they aren’t monitored. When personalization is done well, it blends relevance with discovery: that’s why Spotify’s Discover Weekly feels human rather than robotic. The goal is to make clients feel seen, not analyzed.
Thank you
AI-Driven Customer Insights & Personalization
Sara Baptista de Sousa
Created on October 28, 2025
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Transcript
AI-Driven Customer Insights & Personalization
Start
Index
What we'll talk about this session.
From marketing to machine learning
What AI-driven insights really mean
How AI understands customers
Why personalization matters now
The marketer’s new toolkit
Data, privacy, and trust
Individual to organisational learning
Case Studies
Understanding AI-Driven Customer Insights
For decades, understanding customers depended on a mix of intuition and quite some limited data. Marketers read survey answers, watched focus groups, and checked monthly sales sheets. The insights were slow, partial, and mostly backward-looking. AI changed the rhythm. Instead of analysing what happened, we can now anticipate what will happen. Algorithms process billions of clicks, searches, and purchases, revealing relationships no human analyst could spot. AI doesn’t replace curiosity but it scales it. Where one researcher might notice that people who buy avocados often add lemons to their basket, AI finds thousands of similar patterns across products, every hour of the day.
What “AI-driven insights” really mean
At its simplest, insight means understanding why people behave the way they do. When we refer to "AI-driven insights," we mean use machine learning to find the "why" through data, instead of conjecture.
What “AI-driven insights” really mean
Detect patterns
They cluster customers with similar behaviours.
Predict outcomes
They estimate the probability of future actions (repurchase, churn, clicks).
Optimise decisions
They recommend the best next step (which message to send, when, and through which channel)
It's a dynamic insight, the system is always learning. The model is incrementally altered with each new purchase or search, resulting in more accurate forecasts in the future.
From hindsight to foresight
Traditional analytics worked like a rear-view mirror: they were valuable, but always late. You’d finish a campaign, then run a report. AI turns that mirror into a GPS. It reads signals in real time and adjusts while the journey is happening. Imagine a food-delivery app. Old analytics would show how many users ordered pizza last Friday. Now AI can predict who’s likely to order again this Friday and even tell at what time or which cuisine they’ll choose. Our role as marketer shifted from reporting results to steering outcomes.
Campaigns develop into dynamic systems that don't have to start from scratch every time: they learn, adapt, and self-optimize.
How AI “understands” customers
Several techniques work together:
Recommendation engines
Clustering / segmentation
Use collaborative filtering to suggest products others with similar profiles liked.
It groups people that have similar behaviour patterns, not demographics.
Predictive analytics
Natural-language processing
Estimates probabilities for each individual as purchase, cancellation, click-through or even upgrades.
Or NLP. It reads reviews and social comments to detect sentiment and emerging topics (in a fastest way).
Why personalization matters now?
McKinsey research shows that over 70 % expect brands to tailor interactions, and most feel frustrated when that doesn’t happen. Personalisation used to mean adding a name in an email but not anymore. Now it’s adjusting the entire experience, and this can be from the landing page layout to the product sequence and price promotions, for each and every user. But what makes AI-based personalisation distinct is its behavioural foundation. It looks at what people do, not just who they are. Two customers of the same age and income can behave completely differently online, AI treats them accordingly.
Why personalization matters now?
Well-designed personalisation serves three purposes:
- Performance with higher conversion and loyalty from our customers.
Here, the big risk is over-personalisation. When messaging becomes too specific, people feel watched so create relevance with distance: be helpful, but never too invasive.What do we want to predict?
Designing data questions
The marketer’s new toolkit
What data should we not use?
Choosing ethical boundaries
AI adds speed and scale, but humans still frame the questions. The marketer’s job now includes:
Which insights deserve creative action?
Interpreting patterns
Turning probabilities into campaigns.
Telling the story
Automation handles the heavy lifting but the strategy and the empathy always remain human.
Data, privacy(?) and trust
Within the EU, GDPR defines clear limits. Customers must give consent, know what’s collected, and have the right to explanation. They have the right to ask why a recommendation appeared, and here transparency is the price of relevance. In another point, algorithms learn from existing data, and if that data excludes certain groups, the system will too. Responsible personalisation means continuous monitoring and diversity in data sources.
From individual actions to organisational learning
Organizational intelligence is the true benefit of AI, not just micro-personalization. The entire company learns more quickly when insights are looped back into service, logistics, and product design.
- Media platforms adapt content suggestions continuously
AI can help turn marketing from a communication department into the nervous system of the company (if you used it the right way).Case Studies
Sonae MC / Continente AI for Smarter Segmentation
SONAE MC, which is the big group behind Continente supermarket, uses AI to analyse millions of daily transactions through their Cartão Continente. Instead of grouping consumers based on age or geography, machine-learning models group them according to their lifestyle and purchasing habits. They can also spot shifts (such as a cliente change their preference for baby food) that signify different stages of life.
Learn more
Case Studies
Spotify and their predictive personalization
If you use Spotify, you know what the app does. Spotify’s recommendation system analyses listening behaviour. This can go from plays to skips, saves or timing to build dynamic profiles adjusted to the user's musical taste. It compares millions of users to predict what each will enjoy next, generating playlists such as Discover Weekly and Daily Mix.
Learn more
Case Studies
Zalando and the fashion personalization & predictive fit
Zalando created the Algorithmic Fashion Companion to reduce returns and improve experience. The system analyses browsing history, purchases, returns, and price sensitivity to predict which items customers will keep.
Case Studies
Netflix recommendationand retention
Netflix’s recommendation engine personalises both what you see and the artwork you see it with. Algorithms build taste clusters from viewing habits and test multiple thumbnails to learn which image each viewer clicks.
Learn more
Case Studies
Sephora Europe and AI-Powered beauty personalization
Sephora combines purchase data, browsing patterns, and selfie analysis to tailor product suggestions. Its Color iQ and virtual try-on features match skin tone and texture through computer vision.
Case Studies
H&M and AI forinventory and style curation
H&M’s AI department analyses sales, returns, and weather data to decide store assortments and forecast demand. Social media image analysis detects emerging fashion trends before buyers do.
Responsible AI
Learn more
Ethics and Strategic Reflection
AI gives marketers unprecedented control, and equal responsibility.
We've talked about this previously but transparency and consent are crucial under GDPR. Your clients must know why they see a particular communication. Now, every team needs frequent audits and diverse training data, since algorithms can easily reproduce bias if they aren’t monitored. When personalization is done well, it blends relevance with discovery: that’s why Spotify’s Discover Weekly feels human rather than robotic. The goal is to make clients feel seen, not analyzed.
Thank you