Quick Answer
For search, voice, and "just tell me what to do".
Not everyone wants the same offer - and the same person wants different things at different times. AI enables dynamic offers that adapt to individual buyer intent: browsing behavior, purchase history, time signals, and engagement patterns. Instead of one-size-fits-all promotions, you present offers precisely matched to what each buyer is ready for. Relevance dramatically increases conversion.
Key Takeaways:
- Intent signals vary by individual and moment
- Dynamic offers increase relevance and conversion
- AI can process multiple signals in real-time
- Match offer aggressiveness to intent strength
- Personalization beats broadcast
Playbook
Identify signals that indicate purchase intent
Map offer types to intent levels
Implement AI to match signals with offers
Test and refine signal-to-offer matching
Balance personalization with simplicity
Common Pitfalls
- Over-personalization that feels creepy
- Signals that don't actually predict intent
- Complexity that breaks under edge cases
- Discounts for buyers who would pay full price
Metrics to Track
Conversion rate by offer-intent match
Revenue per visitor with dynamic vs. static offers
Customer response to personalized offers
Discount efficiency (necessary vs. wasted)
Offer relevance ratings
FAQ
What signals indicate purchase intent?
Repeat visits, time on product pages, cart additions, comparison shopping behavior, and engagement with reviews. Combine signals for stronger predictions.
How do I avoid giving unnecessary discounts?
Reserve discounts for low-intent signals. High-intent buyers often don't need incentives. Match offer aggressiveness to signal weakness.
Will customers expect personalization?
Increasingly, yes. But the personalization should feel helpful, not intrusive. Show you understand their needs without revealing how much you know.
Related Reading
Next: browse the hub or explore AI Operations.