Building a recommendation setup for web, app, and email channels

To build a recommendation setup across web, app, and email, the key is to use one recommendation engine with channel-specific delivery rules.

Recommended architecture

1. Central customer/profile layer

   • Unify user identity across web, app, and email
   • Capture browsing, app events, purchases, email opens/clicks, preferences

2. Single recommendation logic

   • Use the same model or rules engine for:
     • product recommendations
     • content recommendations
     • next-best-action / next-best-offer
   • Keep ranking logic consistent across channels

3. Channel-specific rendering

   • Web: real-time recommendations on homepage, product pages, cart, checkout
   • App: in-app cards, push-driven deep links, personalized feeds
   • Email: batch-generated recommendations at send time, with fallback content if no fresh data

4. Decisioning rules

   • Exclude already purchased items
   • Respect inventory, margin, price, and promo constraints
   • Add frequency capping so users aren’t over-targeted
   • Use channel priorities if the same recommendation could appear in multiple places

5. Event and trigger framework

   • Trigger recommendations based on behavior:
     • browse abandonment
     • cart abandonment
     • purchase follow-up
     • replenishment reminders
     • win-back campaigns

6. Testing and optimization

   • A/B test by channel and globally
   • Measure CTR, conversion, revenue per user, unsubscribe rate, and retention
   • Compare real-time vs batch recommendations

Best practice

A strong setup usually looks like:

• one audience and identity layer
• one recommendation engine
• three delivery surfaces: web, app, email

This avoids inconsistent suggestions and makes it easier to optimize performance.

If you want, I can also give you:

• a reference architecture diagram
• a tool stack recommendation
• or a step-by-step implementation plan for a specific platform like Salesforce, Braze, Adobe, or custom ML.