See Scoop in action
Bring your data to life with AI-powered presentations—start your free trial of Scoop.
The only AI that does real data science. It finds patterns, segments, and predictors in your data — Try it now →
.jpg)
IT service management organizations face mounting pressure to deliver rapid, high-quality support amid increasing ticket volumes and complex client demands. In this story, a large-scale dataset containing over 15,400 support tickets spanning numerous clients and agents was ingested into Scoop. The result: actionable, nuanced insights that empowered leadership to address inefficiencies, optimize team member assignment, and enhance client satisfaction. This case is especially timely as IT providers increasingly seek end-to-end automation to manage growing workloads without sacrificing quality.
Scoop’s AI-powered analysis revealed both areas of exceptional efficiency and systemic process gaps previously hidden to the organization. Front-line teams were able to confirm that average time per ticket (0.42 hours) was far lower than industry norms, signaling strong baseline productivity. Yet, the agentic models also spotlighted bottlenecks: over 26% of tickets were either unassigned or lacked proper workflow data, affecting quality and auditability. The ML-driven segmentation identified clear links between agent-client pairings and swift, predictable resolutions, challenging assumptions about random ticket distribution. Most crucially, real billable time and assignment completeness could now be forecast and benchmarked by client, agent, and ticket complexity—influencing planning, resourcing, and service-level compliance.
Represents the full operational support volume covered by the pipeline, ensuring insights were statistically robust and comprehensive.
Indicated either process efficiency or inconsistencies in time tracking—uncovered as a key area for process standardization.
Indicated either process efficiency or inconsistencies in time tracking—uncovered as a key area for process standardization.
Quantified the share of tickets with traceable end-to-end workflow data, directly guiding operational improvement efforts.
Revealed a strong concentration of service demand among a few major clients, informing client account management and resource strategy.
For IT managed service providers, effective support ticket workflows underpin client satisfaction and operational profitability. A major challenge is juggling high ticket volumes with rapid resolution, while avoiding data silos and incomplete tracking. Traditional business intelligence (BI) tools often lag in surfacing client-specific patterns or predicting resource allocations, leading to unresolved tickets, inconsistent assignment, and missed opportunities for process optimization. Support leaders frequently find themselves limited by fragmented data and static dashboards, complicating efforts to identify which clients or team members are driving outcomes, where bottlenecks originate, or how best to re-engineer service delivery for scale and quality. Questions around agent workload, true time-to-resolution, and billable efficiency persist, yet piecemeal reporting and manual attempts fall short in addressing these root complexities.
Scoop’s autonomous data intelligence pipeline executed these steps:
Scoop’s platform thus automated the analytics lifecycle—from ingestion through to insight—delivering not just static metrics but actionable patterns and recommendations.
.jpg)
Scoop’s agentic ML models surfaced root-cause operational drivers invisible to traditional dashboards. Ticket outcome type, client identity, agent assignment, and message rich-text presence were all found to powerfully shape not just resolution speed but billing and workload predictability. Non-intuitive, high-accuracy classification trees demonstrated, for example, that the default billable estimate on tickets is ‘4+ hours’ unless specific agent-client-message patterns are met—explaining disconnects between reported and actual workload. Certain client-assignee pairings (e.g., specific agents handling complex escalations for select clients) consistently achieved rapid (0–15 minute) resolutions, a pattern masked by headline averages.
Further, Scoop mapped how message complexity (measured by description length and HTML formatting) shifted both assignment and final resolution, independently of client priority—an insight requiring machine learning granularity. These patterns drove recommendations for targeted knowledge transfer and policy redesign. Finally, temporal and categorical feature engineering revealed subtle inefficiencies—such as weekend response delays and unassigned ticket handling—that classic reporting overlooks, validating the need for truly automated, agentic analytics.
With actionable insight into workflow gaps, leadership teams launched initiatives to tighten process compliance around ticket assignment and time-tracking, aiming to lift completeness above the 80% mark in the next quarter. The organization now uses Scoop’s predictive models to inform staffing for high-demand clients, proactively balancing workloads for top assignees and fast-tracking knowledge-sharing to replicate the high-efficiency pairings uncovered by the AI. Planned next steps include integrating Scoop with real-time ticketing systems for continuous monitoring, benchmarking inter-client SLAs, and using personalized agent scorecards for professional development. Long term, the findings are set to drive smarter resource allocation, service tiering, and automated SLA compliance verification.