Crisis monitoring

Always-on brand monitoring is the continuous collection of brand mentions and customer feedback from social media, review sites, support conversations, and news. It replaces periodic survey waves with a running feed of data. It captures unprompted, real-world language at high speed and volume. That makes it useful for tracking share of voice and early sentiment shifts between formal brand health tracking cycles. Its limitation is self-selection bias: contributors skew toward people motivated enough to post, so the sample isn’t statistically representative like a recruited panel. It costs less to sustain than repeated survey fielding, though it trades sampling control for reach. Minoan cut its voice-of-customer analysis time by 55% using this continuous approach instead of manual review.

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.

Media intelligence is the collection and analysis of coverage and conversation about a brand across news, broadcast, social media, forums, and review sites. The output is a set of decisions, not a running list of clippings. It differs from plain media monitoring, which only tracks that a mention occurred. Media intelligence classifies each mention by topic, sentiment, and reach, then connects the pattern to action. For contact centers, this matters because public complaints often surface before ticket volume does. OnClarity’s Voice of Customer aggregates 100+ sources, including social media and reviews, to catch these signals early. Example: a delivery complaint theme appears in app store reviews a full week before contact volume starts to rise.

Root cause analysis is the practice of tracing a symptom, such as a spike in complaints, back to the underlying process, policy, or product defect that produced it. The alternative, resolving each ticket in isolation, never stops the symptom from recurring. Established methods include 5 Whys (repeated questioning to reach the originating cause), fishbone (Ishikawa) diagrams (mapping possible causes by category), and Pareto analysis (isolating the small number of causes driving most of the volume). In CX, this starts with clustering tickets by theme, then tracing the largest theme to its source. OnClarity’s Voice of Customer automates this by surfacing root causes directly from feedback. Grubhub uses it to turn feedback into insights that shape its roadmap. The real test of success is recurrence: a fix that addresses the root cause should stop the issue from reappearing.