Let the AI assign leads to the rep most likely to convert them — based on past win data.
Rule-based routing assigns leads fairly. ML-based routing assigns leads intelligently. The difference matters most in teams where reps have clearly different strengths — some consistently close SMBs, others win enterprise accounts; some dominate in manufacturing, others in retail. Explicit rules can approximate this ('SMB leads go to Rep A, enterprise to Rep B'), but ML routing learns from your actual win-loss history and continuously improves its predictions.
This feature requires data to work well — typically 200+ closed deals with win/loss outcomes. For teams that are new to CRM or have fewer than 6 months of data, rule-based assignment (Lead Assignment Rules) is the better starting point. Smart Routing is a growth-stage capability for teams that have outgrown simple rules and want AI to optimise their assignment logic.
Win-Rate-Based Assignment
The model scores each new lead against each available rep's historical win rate for similar lead profiles and assigns to the best match.
Explainable Assignments
Every AI-based assignment shows which factors drove it — so managers can understand and audit the logic.
Override Controls
Managers can override any AI assignment and flag it to improve the model over time.
SaaS company with vertical-specialist reps
A Bengaluru SaaS company had 8 reps, each with experience in 2–3 verticals. Simple round-robin was wasting high-value manufacturing leads on reps who'd never sold to that vertical. After 9 months of CRM data, Smart Routing was enabled. The model learned to send manufacturing enquiries to the rep with the highest historical win rate in that vertical. Win rate for manufacturing leads improved by 22% without any change in team composition.
Assignment Rules follow explicit if/then logic you define. Smart Routing learns from historical outcomes and makes probabilistic decisions — it should be used after you have sufficient win/loss data (typically 3–6 months).