Brand glossary

Brand awareness is measured two ways. Unaided (spontaneous) awareness asks an open-ended question: “Which brands come to mind when you think of [category]?” No names are supplied. Respondents must recall a brand from memory. Aided awareness asks whether a respondent recognizes a specific brand from a list, typically: “Which of these brands do you know?” Recognition is easier than recall, so aided scores run higher than unaided scores for the same brand. Unaided must be asked first, before any brand names appear, to avoid priming the response. Awareness sits at the top of the brand funnel. It sets the ceiling for consideration and purchase intent further down.

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 consideration is the share of category buyers who would actively consider a brand for their next purchase. It’s typically measured by asking “Which of these brands would you consider buying?” among respondents already aware of the category options. Consideration sits between awareness (mere recognition) and purchase intent (stated likelihood to buy a specific option). It marks entry into a buyer’s consideration set, or evoked set: the shortlist from which an actual choice gets made. Consideration is tracked as a conversion rate from awareness, not a raw percentage. That conversion structure is what makes it one of the more actionable funnel metrics for teams trying to close the gap between recognition and purchase.

Brand equity is the commercial value a brand name adds beyond a product’s functional benefits. Evidence includes a price premium over unbranded or generic alternatives, customer preference when products are otherwise at parity, and lower costs to acquire new customers. Marketing scholar Kevin Keller framed this as customer-based brand equity: the differential effect that brand knowledge has on how consumers respond to marketing. It’s built from awareness and the associations stored in memory. Valuation firms such as Interbrand and Kantar BrandZ publish estimated financial valuations of brands, often used for balance-sheet or M&A purposes. Brand equity is the accumulated asset. Brand health is the current diagnostic reading on that asset’s condition.

Brand health is the current strength of a brand across awareness, consideration, preference, and loyalty among a defined target audience at a given point in time. Brand health metrics are gathered through tracker surveys, run either as periodic waves or continuously. These surveys are supplemented with always-on social listening and operational data such as retention and repeat-purchase rates. Standard dimensions include unaided and aided awareness, consideration, perception (attribute and sentiment ratings), and loyalty measures like advocacy and repeat usage. Brand health differs from brand equity in scope. Health tracks a brand’s current standing on these metrics. Equity captures the cumulative financial and psychological value built up over time.

A Brand Health Score is a composite index. It combines several brand health metrics, typically awareness, consideration, satisfaction, and measures tied to brand equity such as perceived quality or trust, into a single number. Two weighting approaches are common. Equal weighting averages components without judging their relative importance. Driver-analysis-derived weighting uses regression or similar techniques to weight each metric by its statistical relationship to a business outcome like revenue or retention. The main risk is masking: a stable overall score can hide one metric rising while another falls. The index should always be reported alongside its individual components, not as a standalone figure. Reporting only the composite number, without that breakdown, is the most common way these programs miss a problem until it’s already visible in the topline score.

Brand perception is the set of attributes and associations customers assign to a brand. It’s typically measured through an attribute battery, ratings on dimensions such as trustworthy, affordable, or innovative, paired with open-ended association questions coded into themes. Analysts map these attributes against a brand’s intended positioning to find gaps between what a company claims and what customers actually believe. Associations vary by audience. That’s why perception is tracked separately across customer segments, regions, and tenure groups rather than as a single blended score.

Brand reputation is the aggregate judgment a brand holds among customers, employees, regulators, media, and investors. It’s a broader construct than customer perception alone, since it accounts for how non-buying stakeholders like journalists and regulators judge a company’s conduct. Periodic survey measures lag events by weeks. Reputation risk is better tracked through leading indicators: spikes in mention volume, sentiment inversion, and clustering of a single complaint theme across channels. Booking.com’s feedback monitoring works this way, spotting bugs in customer feedback and getting the right engineers the information they need to fix issues fast, by surfacing that complaint cluster early.

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.

A brand tracking study is a repeating survey. It’s fielded in waves against a consistent sample frame, questionnaire, and scale, so results can be compared period over period rather than read as a single snapshot. Cadence is typically monthly or quarterly, chosen based on how quickly a category or campaign calendar moves. Consistency in methodology between waves matters more than frequency, since changing question wording or sample sources breaks comparability. Reliable brand health tracking generally requires a large-enough sample per wave to keep margin of error acceptable for the smallest subgroup being analyzed. This differs from a brand audit, which is a one-time, in-depth review of brand health metrics and positioning rather than an ongoing measurement program.

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.

Digital brand health signals are always-on online metrics used as proxies for attitudinal brand health between formal survey waves. They include branded search volume and share of search (a brand’s share of category search demand, tracked via tools like Google Trends), app store rating and review velocity (where recency and volume of reviews carry more algorithmic weight than the raw star average), and review-site score aggregation across platforms. Their appeal is cost and speed: no fieldwork, no sampling, daily granularity. Their limit is that they measure behavior and visibility, not stated attitude. They should supplement, not replace, survey-based brand health tracking.

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.

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.