Social listening

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.

Brand sentiment is the classified emotional valence of brand mentions across reviews, social posts, and support conversations. Mentions are typically sorted as positive, negative, neutral, or mixed. Net sentiment is calculated as the share of positive mentions minus the share of negative mentions, a structure similar to Net Promoter Score. Brand sentiment analysis relies on machine learning or language models to classify text at scale. Documented limitations remain: sarcasm, mixed statements within a single review, and category-specific slang can all lower classification accuracy. A single sentiment score is directional, not representative of the full customer base. It should be read alongside mention volume and topic breakdown.

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.

Topic clustering groups conversations by theme to size the drivers behind contact volume, without requiring an analyst to define categories in advance. Because clusters emerge from what customers actually said, the method surfaces new issues that a fixed intent taxonomy would file under a generic other.

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.