Proactive customer service strategies for CX leaders | Decagon

This post details how Decagon's AI agents are being used to implement proactive customer service strategies. It outlines the process of identifying signals (e.g., stalled onboarding, repeating errors, slipping usage), designing triggers, and defining actions for AI agents. The post emphasizes the shift from reactive to proactive customer service, detailing how AI agents can take action within interactions (e.g., processing renewals, explaining benefits) rather than just sending notifications. It also introduces key metrics for measuring proactive programs, such as contact avoidance rate, deflection rate, outreach engagement rate, and signal-to-action time, and highlights the importance of A/B testing for establishing baselines. The post also touches on the operational changes for CX teams, moving from responders to architects of proactive outreach.