How AI learns your preferences when you shop

Opening scene
You search for a coffee machine — and days later, matching accessories follow you everywhere. It’s not magic; it’s machine learning at work. AI observes, connects, and refines patterns, turning every click into context for smarter suggestions.

Behind the screens
McKinsey and Deloitte report that AI recommendation engines are now standard across major e-commerce platforms. They analyze everything from product clicks to device types, forming dynamic behavioral profiles — all without identifying individuals.

Learning with purpose
Modern AI uses reinforcement learning: it improves with every interaction. Data is anonymized, aggregated, and analyzed to refine personalization — creating experiences that feel intuitive rather than intrusive.

Ethics and transparency
According to PwC’s 2024 consumer trust study, 72% of shoppers say they trust AI tools that explain how recommendations are made. Transparency isn’t just a bonus anymore — it’s a requirement.

Where Marty fits in
Marty learns what matters to you — budget, needs, and style — directly through your search interactions. Your data stays within the conversation, used only to make results more accurate over time. It’s not about selling more, but about helping you find better.

🔗 See how Marty understands your shopping style → heymarty.com

FAQs
1. Does AI really understand preferences?
Yes, by recognizing behavioral patterns across sessions.

2. Is data shared or sold?
No, systems like Marty anonymize and never sell user data.

3. Can AI adapt when my preferences change?
Yes — it reprioritizes new behaviors automatically.

4. Can I control what it learns?
You can reset or limit history anytime.

Sources: McKinsey (2024), Deloitte (2024), PwC (2023), Pew Research (2023).