Definition
Next Best Action Strategy is part of Salesforce's AI capabilities that bring intelligent automation and insights into CRM workflows. It applies advanced algorithms to organizational data to generate predictions, recommendations, or autonomous actions.
Real-World Example
Consider a scenario where a solutions architect at DeepSight Analytics is working with Next Best Action Strategy to enhance decision-making with AI-driven insights embedded directly in the CRM workflow. Next Best Action Strategy processes thousands of records and delivers actionable recommendations that help the team prioritize their efforts and improve outcomes measurably.
Why Next Best Action Strategy Matters
Next Best Action Strategy is the configuration layer where Salesforce administrators and developers define the logic that drives NBA recommendations. Built using the Strategy Builder (a visual flow-like tool), strategies consist of connected elements that load recommendations, filter them based on business rules, enhance them with model scores, and prioritize the final output. Each strategy can reference standard and custom objects, call Einstein prediction models, apply audience segmentation, and rank recommendations by score, priority, or custom criteria. The strategy is where raw data and business intent converge into actionable, prioritized suggestions.
As recommendation complexity grows, well-architected strategies become the difference between useful guidance and overwhelming noise. Organizations scaling NBA across multiple use cases — cross-sell, retention, compliance, and service — need separate strategies or strategy branches for each context. Without proper strategy design, users receive too many irrelevant recommendations and lose trust in the system. The most effective implementations use modular strategy design: separate strategies for different business contexts, reusable recommendation libraries, and A/B testing of strategy variations to continuously improve acceptance rates and business outcomes.
How Organizations Use Next Best Action Strategy
- Atlas Financial Services — Atlas builds a strategy that loads all eligible product recommendations, filters out products the customer already owns, scores the remaining options using an Einstein propensity model, and surfaces only the top two recommendations per customer. The strategy includes a branch that prioritizes compliance-required disclosures over sales offers, ensuring regulatory actions always appear first.
- Velocity Retail — Velocity creates separate NBA strategies for in-store and online channels. The in-store strategy prioritizes loyalty program enrollment and accessory upsells, while the online strategy focuses on cart abandonment recovery and subscription offers. Both strategies share a common recommendation library but apply different filtering and ranking logic based on the interaction channel.
- HealthFirst Medical Group — HealthFirst designs a care gap strategy that loads preventive care recommendations from a custom object, filters by patient age and insurance eligibility, and prioritizes recommendations by clinical urgency. The strategy uses an Einstein model trained on appointment adherence data to boost recommendations for patients most likely to schedule when contacted.