Case studies / 08
Customer experience system for automatic root-cause analysis and resolution of issues, cutting mean time to resolution by 40%
- 40%
- Reduction of Mean Time to Resolution
Every customer issue had to be understood within its complete operational context
Customer support teams often investigated cases in isolation, with conversations, account history, operational events, and previous incidents scattered across multiple systems. The mandate was to build a unified customer intelligence platform that connected every relevant piece of context, accelerated investigations, uncovered recurring issues, and guided teams toward faster and more consistent resolutions of customer issues.
The platform connected customer context, identified root causes, and recommended resolutions
The system consolidated customer interactions, account information, product usage, operational events, and historical cases into a single investigative environment. By identifying recurring patterns, retrieving similar incidents, and analyzing relationships between events, it surfaced likely root causes, prioritized issues by business impact, and recommended the most appropriate resolution or escalation path.
Resolution quality depended on evidence and likelihood rather than assumptions and intuition alone
Every recommendation was supported by historical cases, customer records, operational events, and related evidence. Confidence thresholds, escalation rules, and resolution playbooks ensured that routine issues could be resolved efficiently while complex or high-impact cases were directed to the appropriate specialists. This reduced the risk of incorrect diagnosis, repeated failures, and prolonged customer disruption.
Customer support shifted from reactive case handling to proactive issue management
The platform became part of everyday support operations, integrating directly into ticket intake, investigation, prioritization, escalation, and resolution workflows. Customer conversations, support history, operational signals, and product data were brought together into a unified context, allowing support teams to resolve issues faster, identify systemic problems earlier, and continuously improve the customer experience.