Automated reporting

Anomaly detection identifies data points or patterns that deviate from an established baseline of normal behavior. Methods range from static statistical thresholds, which flag values beyond a fixed range, to seasonal or time-series decomposition, which accounts for expected cyclical variation, to unsupervised machine learning models that learn normal patterns without labeled examples. In CX, it flags unexpected spikes in ticket volume, negative sentiment, or a specific contact reason before they become widespread. Thresholds set too tightly generate false positives and alert fatigue, and real problems get buried in noise. OnClarity’s Voice of Customer includes real-time alerting that can flag unusual shifts in feedback volume or sentiment as they surface.

Voice of customer is the discipline of turning reviews, support tickets, surveys, and conversations into operational decisions. The distinguishing feature of a working programme is the closed loop: findings reach the team that can fix the cause, and the resulting change is measured against contact volume.

Crisis monitoring is the continuous tracking of customer and public channels to catch sudden shifts that signal an incident. That might be a complaint spike, an outage, a safety issue, or a story gaining traction online. It differs from routine monitoring by comparing live volume and sentiment against a historical baseline. It flags anomalies, applies alert thresholds, and routes the notification to an on-call owner quickly, before the issue spreads. Example: a payments failure surfaces as a surge in chat contacts before the status page even updates. OnClarity’s Voice of Customer supports this with real-time alerting into tools like Slack. Related terms: media intelligence and root cause analysis.

CX analytics is the practice of combining three data families to explain why customer experience is trending a certain way, beyond simply reporting that it is. Those families are operational metrics (handle time, resolution rate, channel mix), interaction data (calls, chats, emails), and solicited feedback (surveys, reviews). Where standard reporting shows a CSAT drop, voice of customer analytics attributes it to a cause. For example, a policy change surfaced across three channels in the same week, assigned to a team for action. OnClarity’s Voice of Customer supports this by aggregating 100+ feedback sources and classifying them by topic and sentiment to surface root causes. This closes the gap between closed loop feedback and first contact resolution. A mature CX analytics glossary should separate these three data families clearly, since vendors often blend them into a single “experience score” that hides which one moved.