| name | sf-einstein |
| description | Implement Einstein AI for Salesforce Commerce — Einstein Recommendations (product-to-product, user-to-product, trending), predictive sort, search ranking, Einstein Search Dictionaries, and Data Cloud personalization. Use when adding AI-powered features to commerce storefronts.
|
Einstein AI for Salesforce Commerce
Before Writing Code
Fetch live docs before implementing Einstein AI features.
- Web-search: "Salesforce Commerce Cloud Einstein Recommendations API 2026"
- Web-search: "Salesforce Einstein Search dictionaries relevance tuning 2026"
- Web-search: "Salesforce Data Cloud B2C Commerce personalization 2026"
- Web-search: "Salesforce Commerce Cloud PWA Kit Einstein recommendations 2026"
- Web-fetch the Einstein Recommendations API reference for current configuration parameters
- Web-fetch Data Cloud connector setup and unified profile schema docs
Conceptual Architecture
Einstein Recommendations
Recommender Types:
| Type | Description | Typical Placement |
|---|
| Product-to-Product | Similar or complementary items (cross-sell) | PDP |
| Recently Viewed | User's browsing history | Homepage, category |
| Also Bought | Products frequently purchased together | Cart, PDP |
| Trending | Popular items across all users | Homepage, category |
| Top Sellers | Best-selling products by category/site | Homepage, category |
| Personalized | ML-driven per-user recommendations | Homepage (returning users) |
Recommender Configuration (Business Manager):
- Create recommenders with specific types and filtering rules
- Configure zone placement (homepage, PDP, cart, category page)
- Set filtering: exclude out-of-stock, price range limits, category restrictions
- Map recommendation zones to recommenders
Activity Collection:
Einstein activity tracking uses a collect.js library loaded on storefront pages. It automatically captures product views, add-to-cart, purchases, and search events. Configured via Business Manager > Einstein > Activity Tracking.
Warning: The _etmc beacon pattern is for Marketing Cloud Einstein, not Commerce Cloud Einstein. Do not confuse the two.
Recommendation Zones:
- Define placement areas on storefront pages
- Map zones to recommenders in Business Manager
- Customize rendering per zone (carousel, grid, list)
Einstein Predictive Sort
Personalized category page sorting powered by ML.
| Aspect | Detail |
|---|
| Input | User behavior (clicks, purchases, browse history) |
| Output | Per-user product ranking on category pages |
| Fallback | Default sorting for new / anonymous users |
| Config | Per-category toggle in Business Manager |
Einstein Search
Search Dictionaries:
| Dictionary Type | Purpose | Example |
|---|
| Synonyms | Map equivalent terms | sneakers -> running shoes |
| Hypernyms | Broader category terms | iPhone -> smartphone |
| Compound Words | Multi-word phrases | ice cream, swimming pool |
Search Relevance Tuning:
- Boost or bury specific products in search results
- Configured per site/locale in Business Manager
- Sorting rules: relevance, price, newest, custom
Typeahead Suggestions:
- Search-as-you-type with phrase suggestions and hit counts
- Configured via Business Manager search settings
Data Cloud Personalization
Integration Architecture:
B2C Commerce -> Data Cloud Connector -> Unified Profile
-> Segmentation + ML Models (Einstein)
-> Personalized Recommendations / Content
-> Commerce Storefront (SFRA / PWA Kit)
Key Concepts:
- Aggregates data from Commerce Cloud, Service Cloud, Marketing Cloud into unified profiles
- Real-time profile updates via Data Cloud connector
- Segment membership drives personalized content and recommendations
- Cross-channel offer consistency based on unified customer view
Data Cloud vs Commerce Cloud Einstein:
| Aspect | Commerce Cloud Einstein | Data Cloud Personalization |
|---|
| Data source | Commerce activity only | Cross-cloud unified profile |
| Setup | Business Manager config | Data Cloud connector + config |
| Segments | Implicit (ML-driven) | Explicit (rule-based + ML) |
| Best for | Product recommendations | Cross-channel personalization |
Zone Placement Strategy:
| Page | Recommended Zones |
|---|
| Homepage | Trending + personalized (returning users) |
| PDP | Similar products + complementary items (cross-sell) |
| Cart | Cross-sell + upsell opportunities |
| Category | Predictive sort + trending in category |
| Search Results | Einstein-ranked results |
Code Examples
var recs = einsteinAPI.getRecommendations(zone, customer);
const {data} = useRecommendations({recommenderName, products});
Best Practices
Activity Collection
- Configure activity tracking before enabling recommendations (minimum 2-4 weeks of data)
- Track all key events: views, add-to-cart, purchases, search queries
- Validate tracking via Einstein Activity Dashboard
Performance
- Cache recommendations (5-15 min TTL) to reduce API calls
- Lazy-load recommendation zones below the fold
- Limit number of products per zone (8-12 typical)
Privacy and Consent
- Respect customer privacy preferences (GDPR, CCPA)
- Allow opt-out from personalized recommendations
- Implement clear data retention and right-to-be-forgotten policies
Rollout Strategy
- Start with one high-traffic zone (homepage); monitor 2-4 weeks
- A/B test Einstein vs. manual curation or top-sellers fallback
- Track CTR, conversion rate, revenue attribution per zone
- Gradually expand to PDP, category, cart after proven ROI
Fetch the Einstein Recommendations API reference and Data Cloud connector docs for exact configuration parameters and SDK versions before implementing.
