Quick Answer
For search, voice, and "just tell me what to do".
Customers tell you which products belong together through their buying behavior - if you're listening. AI can analyze purchase patterns to find products frequently bought together, even when the connection isn't obvious. These discoveries inform bundle creation, cross-selling, product placement, and upsell strategies. What seems random often follows patterns AI can detect.
Key Takeaways:
- Purchase patterns reveal product affinities
- Some connections aren't intuitively obvious
- AI can detect patterns across large datasets
- Discoveries inform multiple strategies beyond bundling
- Regular analysis reveals evolving patterns
Playbook
Aggregate purchase history data
Use AI to analyze for co-purchase patterns
Identify strong and surprising connections
Apply discoveries to bundling, placement, and cross-sells
Monitor patterns as they evolve over time
Common Pitfalls
- Small datasets producing unreliable patterns
- Correlation without considering causation
- Missing seasonal or contextual patterns
- Acting on patterns without testing
Metrics to Track
Co-purchase frequency by product pair
Pattern strength and reliability
Revenue impact of pattern-based actions
New patterns discovered per period
Pattern accuracy over time
FAQ
How much data do I need for reliable patterns?
Generally, 100+ purchases with multiple product options. More data produces more reliable patterns. Start with tentative patterns; validate with more data.
What if I don't have purchase data?
Use browsing data, wishlist data, or survey customers about what they'd want together. Even qualitative data helps identify potential combinations.
How often do patterns change?
Depends on your market. Review quarterly at minimum. Seasonal products, trends, and new releases all shift patterns.
Related Reading
Next: browse the hub or explore AI Operations.