sf-einstein

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.
allowed-tools	Read, Write, Edit, Bash, Grep, Glob, WebSearch, WebFetch

Einstein AI for Salesforce Commerce

Before Writing Code

Fetch live docs before implementing Einstein AI features.

1. Web-search: "Salesforce Commerce Cloud Einstein Recommendations API 2026"
2. Web-search: "Salesforce Einstein Search dictionaries relevance tuning 2026"
3. Web-search: "Salesforce Data Cloud B2C Commerce personalization 2026"
4. Web-search: "Salesforce Commerce Cloud PWA Kit Einstein recommendations 2026"
5. Web-fetch the Einstein Recommendations API reference for current configuration parameters
6. 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

// Pattern: SFRA recommendation zone
// Fetch live docs for Einstein Recommendations API
var recs = einsteinAPI.getRecommendations(zone, customer);
// Render recs in ISML template

// Pattern: PWA Kit recommendations hook
// Fetch live docs for commerce-sdk-react useRecommendations
const {data} = useRecommendations({recommenderName, products});

// Pattern: Fallback when Einstein unavailable
// Fetch live docs for CacheMgr and fallback strategies
// try Einstein -> catch -> return getTopSellers(zone)

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.