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How ServiceNow Support Uses Machine Learning to Predict Customer Escalations

Client

ServiceNow

Industry

Technology & IT Services

AI Tech Solution

AI-Powered Predictive Intelligence

Solution Provider

ServiceNow

Challenge

ServiceNow Support aimed to become more proactive in identifying and resolving customer-impacting issues. Traditionally, the team relied on manual monitoring of performance-related events, which made it difficult to predict potential customer escalations before they occurred. The existing system was reactive rather than predictive, meaning the support team could only respond to issues after customers had already experienced problems. Key challenges included lack of predictive intelligence to forecast when a customerâ€™s experience was deteriorating, inconsistent event monitoring that resulted in missing early signs of potential escalations, high dependency on manual processes, and an inability to scale proactive customer engagement. ServiceNow needed a machine learning-driven solution that could analyze trends, detect risks, and proactively engage with customers before problems escalated.

Solution

ServiceNow implemented a machine learning-based predictive model powered by ServiceNow Predictive Intelligence and Event Management. The solution leveraged supervised learning with XGBoost classification models to identify early warning signals based on historical escalation data. A Proof of Concept (POC) was first developed to validate whether enough empirical data was available to pass precision and accuracy thresholds. Following a successful Proof of Value (POV) phase, a Minimum Viable Product (MVP) was deployed, fully automating the process through the ServiceNow platform. The predictive model was integrated with Event Management, enabling real-time analysis of customer interactions, system alerts, and historical escalation data. Automated workflows were then created to notify support teams when a customer showed signs of potential dissatisfaction, allowing proactive outreach before an escalation occurred.

Results

The predictive machine learning model significantly improved ServiceNow's ability to engage customers proactively, shifting from a reactive to a data-driven predictive support model. Before implementation, only 11 percent of customer engagements were proactive. With the new ML-driven system, this number increased to 68 percent, allowing ServiceNow Support to prevent issues before they escalated. By reducing false positives, the final model achieved a 3 percent false positive rate, ensuring that engineering resources were not wasted on unnecessary escalations. Since the go-live phase, the machine learning model has helped engage hundreds of customers per year proactively, reducing escalations and improving overall customer satisfaction. The success of this predictive model underscores the power of AI-driven proactive support, demonstrating how machine learning can revolutionize enterprise customer service by enabling faster, smarter, and more efficient issue resolution.

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