A brand tracker is a survey of category buyers, repeated on a consistent design, that shows how a brand's standing in memory changes over time and against competitors. Its traditional content follows the funnel: unprompted and prompted awareness, consideration, preference and attitudes such as trust or perceived quality. These measures are familiar to boards, but they have well-known weaknesses. Attitudes tend to follow behaviour rather than lead it (Sharp, 2010), so a brand's users rate it more warmly than non-users do, and a bigger brand looks "better liked" largely because it has more users. Awareness, meanwhile, is close to saturation for established brands and says little about whether a brand comes to mind at the moment someone is ready to buy.

Mental availability addresses that gap. Romaniuk and Sharp (2004) define brand salience as the propensity of a brand to be noticed or thought of in buying situations, not simply top-of-mind recall. Romaniuk (2023) turns this into metrics built on category entry points, the needs, occasions and cues that lead people into a category, which you met in Module 2. Respondents are shown a list of entry points and asked which brands they link to each. From the answers come mental market share (the brand's share of all brand–entry point links), mental penetration (the share of category buyers who link the brand to at least one entry point) and network size (how many entry points the brand is linked to among those who link it to any).

A useful tracking plan therefore samples category buyers rather than only customers, measures the brand alongside its main competitors, pairs mental availability with distinctive-asset recognition from Module 3 and keeps a small number of attitude measures for context. It also plans for noise: tracker scores move slowly, so wave-to-wave changes within the margin of error should not trigger decisions. Brand health in this sense connects the diagnosis you made with Keller's pyramid in Module 1 to evidence that can be collected every quarter.

Share of voice (SOV) is a brand's share of all the advertising in its category, usually measured as its media spend divided by total category media spend. Share of market (SOM) is its share of category sales. The difference between them, SOV minus SOM, is excess share of voice (ESOV). Jones (1990) observed that across many categories large brands tend to spend below their share of market while small brands have to spend above it to hold or gain share, which made the comparison a practical starting point for setting budgets.

Binet and Field's analyses of the IPA Effectiveness Databank gave the relationship a number (Binet and Field, 2007, 2013). Across the campaigns they studied, each ten points of ESOV was associated with roughly half a percentage point of annual market-share growth for consumer brands. Later work by LinkedIn's B2B Institute with Binet and Field put the figure for B2B brands higher, at about 0.7 points per ten points of ESOV (LinkedIn, 2020). The same summary quotes 0.6 for consumer brands, a reminder that these figures are rounded averages, not constants. The arithmetic is simple: a brand with 15% SOV and 10% SOM has an ESOV of five points and might expect around a quarter of a point of share growth a year.

The limits matter as much as the rule. It is a correlation across an average of campaigns, not a law for any one brand, and the IPA Databank consists of award entries, so it leans towards successful work. The effect needs sustained ESOV, typically over several years, and varies by category and by creative quality. Field (2019) found that the share-growth efficiency of creatively awarded campaigns fell sharply in the decade to 2018 as work shifted towards short-term activation, which suggests that what brands say matters as much as how loudly they say it. The inputs are also uncertain. Category spend must be estimated from advertising-intelligence data that capture digital spending imperfectly, and the answer depends heavily on how the category is defined: specialist outdoor clothing and all clothing give very different shares. Treat an ESOV calculation as a planning estimate that frames the budget debate, not a forecast.

Module 4 treated the brand–activation split as a planning decision, and Fellwise's autumn plan put 65% (£390,000) into brand building and 35% (£210,000) into activation as a hypothesis to test. This lesson asks how you would know whether the split worked. The difficulty is that the two sides work on different clocks. Binet and Field (2013) found that activation produces sales spikes that are immediate and short-lived, while brand building produces smaller immediate effects that accumulate over months and years, raising base sales and reducing price sensitivity. Most dashboards judge activity within days: click-through rates, cost per acquisition and last-click attribution, which credit the final touchpoint and see nothing of the memories that sent someone to search for the brand. Judged that way, activation nearly always looks more productive, and Binet and Field (2013) warned that optimising for short-term return erodes long-term growth.

Three kinds of evidence answer different parts of the question. Econometrics, or marketing mix modelling (MMM), uses regression on several years of weekly sales data to separate base from incremental sales and to estimate each medium's contribution while controlling for price, distribution, seasonality, weather and competitor activity (Cook and Holmes, 2004). It needs long, consistent data and real variation in spend, and standard models capture short- and medium-term effects far better than slow growth in base sales, so they tend to understate brand building unless designed to look for it. Incrementality tests, such as geographic holdouts in which matched regions receive no advertising or heavier weight, or platform conversion-lift studies with a randomised control group, give cleaner causal answers to narrow questions, but usually over weeks rather than years. Brand tracking over time shows whether the memory structures that brand building targets are moving: the leading indicator of effects that sales data will reveal only later.

None of these is sufficient alone. A mature programme uses tracking for brand health, econometrics for contribution and experiments to calibrate the model and settle specific decisions, and it judges a split over two or three years rather than one campaign, because the optimum varies with category and brand situation (Binet and Field, 2018).

Public relations and corporate communication have long had their own measurement debate. For decades, coverage was valued with advertising value equivalents (AVEs), the cost of buying the same space as advertising, often multiplied by an arbitrary factor. AVEs measure space rather than effect, and they give the same value to hostile and favourable coverage. The Barcelona Principles, first agreed in 2010 and revised as version 3.0 by AMEC, the International Association for the Measurement and Evaluation of Communication, reject them outright (AMEC, 2020).

The seven principles of version 3.0 are: setting measurable goals is a prerequisite for planning, measurement and evaluation; measurement should identify outputs, outcomes and potential impact; outcomes and impact should be identified for stakeholders, society and the organisation; evaluation should be both qualitative and quantitative; AVEs are not the value of communication; measurement should be holistic across all relevant online and offline channels; and it should be rooted in integrity and transparency to drive learning (AMEC, 2020). AMEC's Integrated Evaluation Framework puts them into practice, moving from objectives, inputs and activities through outputs (what was delivered), outtakes (what audiences noticed and understood) and outcomes (changes in attitude or behaviour) to organisational impact. The principles fit the stakeholder view you took in Module 5: reputation with employees, regulators and communities counts, not just coverage.

Reporting to leadership is where all this evidence is tested. A board rarely wants every metric; it wants to know whether the work met its objective, what it contributed to the business and what to do next. A good one-page effectiveness report states the objective and the investment, shows results at each level from output to impact with a benchmark for each, separates brand-health trends from short-term sales results, is honest about uncertainty and what cannot be attributed, and ends with a recommendation. It uses finance's language where it can, but it does not claim more precision than the evidence allows. This report becomes the measurement section of the plan you build in Module 8.