AI Product Recommendations
A recommendation workflow that combines product catalog data with consented browsing and purchase events. It creates recommendation sets for selected placements, applies merchandising rules, and reports performance by strategy.
Service path: Workflow Automation
How This Workflow Works
How the workflow moves from intake to output.
- 1
Analyzes product catalog and purchase history
- 2
Builds customer preference profiles from consented events
- 3
Learns from similar customer purchase patterns
- 4
Generates personalized recommendations per customer
- 5
Deploys on PDP, cart, checkout, and email
- 6
A/B tests recommendation strategies
- 7
Tracks click-through and conversion by placement
- 8
Reports revenue and click outcomes by placement
Where This Workflow Fits
Example industries for this workflow. We adapt the inputs, rules, and review steps to each operating environment.
Fashion E-commerce
Example industry fit
Electronics
Example industry fit
Beauty
Example industry fit
Home Decor
Example industry fit
Grocery
Example industry fit
Pet Supplies
Example industry fit
What This Workflow Covers
- Test upsell and cross-sell placements
- Personalized per customer
- Multi-placement deployment
- Model update cycle
- Placement performance reporting
- A/B testing built-in
Use Cases
- Show "Frequently bought together" on product pages
- Recommend upsells in cart before checkout
- Send personalized product emails
- Power homepage for returning customers
Build this workflow.
We can map this workflow against your tools, handoffs, and edge cases.
Book a consult