Predictive Routing
Predictive routing uses AI and machine learning to analyze customer data, interaction history, and agent skills to match each incoming inquiry with the agent most likely to achieve the best outcome.
In Depth
Traditional routing is rule-based: round-robin, skills-based, or queue-based. Predictive routing goes further by using machine learning models trained on historical interaction data to optimize for specific outcomes — CSAT, resolution time, revenue, or FCR. The system considers factors like the customer's profile, issue complexity, sentiment, language preference, purchase history, and even personality traits, matching them with agents whose skills, experience, and interaction style are most likely to produce a positive outcome.
For example, a frustrated VIP customer with a billing issue might be routed to a senior agent with strong empathy scores and billing expertise, bypassing the queue entirely. Predictive routing typically improves CSAT by 5-10 points and reduces handle time by 10-20%. In AI-native environments like GuruSup, predictive routing also determines whether an AI agent can handle the interaction autonomously or whether human escalation is needed, optimizing the blend of AI and human resources.
Related Terms
Ticket Routing
Ticket routing is the process of automatically directing customer support requests to the most appropriate agent, team, or AI workflow based on the ticket's content, priority, and required expertise.
Automatic Call Distribution (ACD)
ACD is a telephony system that automatically routes incoming calls to the most appropriate available agent based on predefined rules such as skills, priority, wait time, or customer segment.
Customer 360
Customer 360 is a unified view of all customer data — interactions, purchases, preferences, support history, and engagement — aggregated from multiple systems into a single comprehensive profile.
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