Media intelligence

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.

Share of voice (SOV) measures a brand’s portion of total category conversation, advertising spend, or mentions over a defined period: SOV = (brand’s spend, impressions, or mentions ÷ total category spend, impressions, or mentions) × 100. The media-spend version, used in advertising planning, compares ad budgets or impressions against competitors. The social-mention version tracks a brand’s share of online conversation instead. Researchers Les Binet and Peter Field popularized excess share of voice (ESOV), the gap between a brand’s share of voice and its share of market, as a predictor of future growth, since brands with SOV above their market share tend to gain ground. One caveat for brand health tracking: raw mention-based SOV can spike during negative coverage, so it should be paired with sentiment data rather than read as a standalone positive signal.

Sentiment analysis scores the emotional tone of a conversation to flag risk and measure experience without waiting for a survey response. Applied across every contact rather than a sampled few, it covers the large majority of customers who never complete a satisfaction survey at all.

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.