Einstein Next Best Action
A Salesforce AI tool that uses rules-based and predictive models to recommend offers and actions to users at the point of interaction, displayed through a Lightning component on record pages.
Definition
A Salesforce AI tool that uses rules-based and predictive models to recommend offers and actions to users at the point of interaction, displayed through a Lightning component on record pages.
In plain English
“Einstein Next Best Action is a Salesforce feature that recommends what an agent or user should do next, based on rules and AI predictions. It might suggest an offer to make to a customer, an action to take on a record, or a related product to discuss. The recommendations show up right on the record page.”
Worked example
When an AE at Hartmoor Software opens an Account record, Einstein Next Best Action surfaces a recommendation panel: "Schedule renewal call - contract expires in 45 days," "Upsell opportunity for Pro Tier - Einstein scored 87% likelihood," "Send retention offer - customer is high-LTV but engagement is dipping." Each recommendation comes from a Recommendation Strategy combining business rules with AI predictions. The AE accepts or dismisses each; accepted recommendations trigger automated workflows. Einstein Next Best Action turns AI insights into actionable in-context suggestions at the moment of work.
Why Einstein Next Best Action matters
Einstein Next Best Action is a Salesforce AI tool that recommends actions or offers to users at the point of interaction. It combines rules-based logic (configured by admins) with predictive models (built in Einstein Discovery or other tools) to determine which action is most appropriate for a given context, then surfaces the recommendation through a Lightning component on a record page or other UI surface.
Next Best Action is most useful when there are multiple valid actions a user could take and the right choice depends on context. A service agent looking at a customer might see a recommended offer, a recommended retention action, or a recommended cross-sell, depending on what the model predicts will work best for that customer. The framework supports both pure rules (like 'if customer has been with us 5+ years, offer loyalty discount') and ML predictions (like 'this customer is most likely to accept this offer'), and the two can be combined for richer logic.
How to set up Einstein Next Best Action
Einstein Next Best Action (NBA) is the recommendation engine that surfaces "what should I do next" cards on a record — "offer this discount," "escalate to a manager," "share this article." Recommendations come from a Strategy that combines static rules, predictive Einstein models, and recommendation records. Setup is multi-step: Recommendation records → Strategy → Lightning component on the page.
- Confirm Einstein Next Best Action is licensed
NBA is part of Einstein for Service / Sales bundles. Confirm before enabling.
- Build Recommendation records
App Launcher → Recommendations → New. Each Recommendation is a card the user could see — Name, Description, Image, Action (URL / Flow / Quick Action).
- Open Setup → Next Best Action → Strategy Builder
Strategy Builder is the visual flow tool that decides which Recommendations to surface for which records.
- Build a Strategy
Drag Load Recommendations / Filter / Generate / Limit / Output elements. The Strategy reads context (the current record), filters Recommendations, applies Einstein-scored ranking, and outputs a final list.
- Activate the Strategy
Strategies need to be activated to fire. Inactive ones don't return recommendations.
- Add the Einstein Next Best Action Lightning component to the relevant Lightning Page
Lightning App Builder → drop the Einstein Next Best Action component on Account / Case / Opportunity record page → bind to the activated Strategy.
- Save the Lightning Page → Activate
Recommendations now appear inline on records using the Strategy's logic.
The cards users see. Manually curated or generated from external data.
Load / Filter / Generate / Limit / Branch / Predict / Sort / Output.
Optional — score recommendations using an Einstein Discovery model.
Per Lightning Page — different strategies on different records.
- Strategies don't fire until activated. A built-but-inactive Strategy returns nothing — common oversight after testing.
- Without Einstein Predictive ranking, NBA is rules-only — "if this and that, show recommendation X." Adding Einstein scoring requires an Einstein Discovery model bound to the Strategy.
- Recommendation Action types determine what happens on click. URL opens a link. Flow launches a flow. Quick Action invokes an action. Misconfigured action types fail silently when users click.
How organizations use Einstein Next Best Action
Uses Next Best Action in their Service Console to recommend retention offers when an agent is talking to a customer with high churn risk. The recommended offer is tailored to the customer's history and predicted preferences.
Built Next Best Action recommendations for sales reps showing recommended next steps on opportunities based on stage, deal size, and historical patterns for similar deals.
Combines rules-based eligibility (the customer must qualify for the offer) with ML-based ranking (which qualifying offer is most likely to be accepted).
Trust & references
Straight from the source - Salesforce's reference material on Einstein Next Best Action.
- Einstein Next Best ActionSalesforce Help
- Get Started with Einstein Next Best ActionSalesforce Help
Test your knowledge
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