Competitive intelligence

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.

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.

Customer insights are conclusions about customer behavior and needs that are specific enough to act on. They differ from raw feedback, which is simply what a customer said, and from analytics, which shows what happened in aggregate. A customer insight explains why something happened and what to change next. Customer insights come from combining feedback, conversation data, usage data, and survey responses across channels. The step that matters is classifying patterns by root cause, not just counting volume. Example: a spike in refund-related tickets across chat, email, and reviews first looks like a support volume problem. Root-cause classification shows most of those tickets trace back to a single checkout error. OnClarity’s Voice of Customer surfaces that root cause automatically and pushes the finding into Slack, Jira, or Linear. The product team that owns the checkout flow gets the ticket directly, instead of support agents closing the same complaint one at a time for weeks.