Next Best Action
| Approach | Rules-Based | Next Best Action (AI) |
|---|---|---|
| Logic | If-then rules | Predictive models |
| Adaptability | Static until changed | Learns from outcomes |
| Example | Day 3: send email B | Based on engagement: email C or call |
| Best for | Simple, linear processes | Complex, variable buyer journeys |
💡TL;DR
Next Best Action uses AI to recommend what a rep should do right now with a specific deal. Instead of static sequences (Day 1 email, Day 3 call), it adapts based on prospect behavior. For SMBs, start with rule-based NBA (if no response in 7 days → follow-up) before investing in ML models. Simple rules that execute consistently beat sophisticated models that aren't trusted.
Definition
AI-driven recommendation of the optimal action for a rep to take with a specific prospect at a specific moment, based on historical patterns, current signals, and predicted outcomes.
🏢What This Means for SMB Teams
ML-based Next Best Action requires significant data. SMBs should start with rule-based: if deal stalls for X days → action Y. This captures 80% of value with 20% of complexity.
Track /pricing, /demo, content engagement. Act before competitors.
Turn anonymous browsing into qualified conversations.
📋Practical Example
A 30-person sales team had reps guessing what to do next. They implemented rule-based NBA: proposal sent + no response 7 days → follow-up email task; email opened 3x without reply → call task; deal in negotiation 14+ days → manager review. Simple rules, but consistent execution. Win rate improved 19% because reps stopped forgetting follow-ups.
🔧Implementation Steps
- 1
Audit current decision points: Where do reps decide what to do next?
- 2
Define rules: What action makes sense based on stage + signal + time?
- 3
Implement as tasks/alerts: Surface the NBA in rep workflow, not a separate dashboard
- 4
Measure compliance: Are reps taking the recommended actions?
- 5
Iterate: Add rules for common scenarios; remove rules that don't drive results
❓Frequently Asked Questions
When should SMBs move from rules-based to ML-based NBA?
When you have 1,000+ closed deals for training data and rules-based is producing diminishing returns. Most SMBs are years away from this. Master rules-based first—it's 80% of the value.
How do we get reps to follow NBA recommendations?
Make it easy: surface in their workflow (not a separate tool), make it one click to execute. Show results: "Reps who followed NBA had 24% higher win rate." Let them see it works, then compliance follows.
⚡How Optifai Uses This
Rule-based NBA for deal stages and signals. AI recommends follow-up, escalation, or re-engagement based on deal context and historical patterns.
Action Recommendations📚References
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Related Terms
Revenue Action
An automatically executed action triggered by buying signal detection (email sending, calendar booking, etc.) that focuses on "execution" rather than "suggestion".
System of Action
A design philosophy where AI executes actions automatically rather than just suggesting them. The evolution from "System of Record" (CRM) to "System of Engagement" (Sales Engagement) to "System of Action".
Sales Enablement
The process of providing sales teams with the resources, tools, content, and information they need to effectively engage buyers and close deals.
Signal Detection
Real-time detection of buying intent signals from web behavior, email engagement, and deal stage changes.