Brand Audits, Tracking and Equity Dashboards

Introduction and context

Brand equity is not managed effectively through occasional opinion surveys or isolated campaign reports. It requires a disciplined system that connects what the organisation does, what customers experience, what competitors offer, and what commercial outcomes follow. Brand audits, brand tracking, competitive benchmarking and executive dashboards form the core of that system.

A brand audit is a comprehensive, periodic examination of a brand’s sources of equity and its performance in the market. It combines an internal brand inventory with an external consumer and stakeholder study. Brand tracking is the continuing measurement of selected indicators over time. A dashboard translates these indicators into a decision-oriented visual system for managers.

The central managerial challenge is not simply to collect more data. It is to determine which measures are valid indicators of brand health, how they relate to one another, and what action should follow when performance changes. A high awareness score, for example, may coexist with weak preference, poor service experience or declining price tolerance. Effective evaluation therefore requires a chain of diagnosis rather than a single score.

Learning objectives

By the end of this chapter, students should be able to:

1. Distinguish brand inventories, brand audits, exploratory studies and tracking systems.
2. Design an internal inventory of brand touchpoints and equity-building activities.
3. Plan qualitative and quantitative exploratory research for diagnosing brand meaning and experience.
4. Benchmark competitors using comparable definitions, samples and measures.
5. Diagnose gaps between intended, delivered and perceived brand value.
6. Select leading and lagging KPIs for different strategic objectives.
7. Construct scorecards and executive dashboards that support decisions rather than merely report data.
8. Evaluate methodological, ethical and governance risks in brand measurement.

1. The architecture of brand evaluation

Brand evaluation should be understood as a measurement architecture with four connected layers:

The layers are related but not interchangeable. Marketing expenditure is an input, awareness is a perception, repeat purchase is a behaviour, and margin is a financial outcome. A dashboard that mixes them without showing their relationships encourages superficial interpretation.

A useful causal logic is:

Activities and experiences → mental availability and associations → consideration and choice → loyalty and advocacy → commercial and financial value.

This logic is probabilistic, not deterministic. Strong awareness does not guarantee choice, and high satisfaction does not always produce retention when switching costs are low or competitors offer better value.

Important terminology

A brand inventory is an internally compiled description of all current brand elements, marketing activities, products, communications, channels and customer-facing experiences. A brand exploratory is an external investigation of how customers and other stakeholders perceive the brand. A brand audit integrates these perspectives to identify strengths, inconsistencies and opportunities.

Brand tracking is repeated measurement using stable or deliberately controlled methods. A benchmark is a reference point used for comparison, such as a competitor, category norm, historical baseline or strategic target. A leading indicator tends to change before a business outcome, whereas a lagging indicator records an outcome after it has occurred. A diagnostic measure helps explain why performance changed; a descriptive measure reports what happened.

2. Conducting a brand inventory

The inventory begins with evidence collection rather than judgement. The audit team should document the brand as it exists, not merely as it appears in a positioning document.

2.1 Inventory scope

A comprehensive inventory normally covers:

• corporate purpose, values and brand architecture;
• masterbrand, sub-brands, endorsed brands and private-label relationships;
• names, logos, colours, typography, packaging and sonic or verbal assets;
• current and historic positioning statements;
• product and service portfolio, variants and innovation pipeline;
• paid, owned, earned and shared media;
• retail, digital, sales, call-centre and partner touchpoints;
• pricing, promotions, distribution and availability;
• customer experience standards and complaint processes;
• internal training, incentives and employee brand understanding;
• legal registrations, claims substantiation and usage guidelines;
• agency, technology and data suppliers; and
• performance evidence, including previous research and market results.

The inventory should identify the owner, audience, objective, investment, timing and intended equity effect of each activity. A campaign may intend to build trust, for example, while its execution mainly produces short-term attention. Recording intended and observed effects separately helps reveal strategic drift.

2.2 Touchpoint consistency and strategic coherence

Consistency does not mean mechanical uniformity. A bank may communicate security differently in an app, branch and television advertisement, but the underlying promise should remain recognisable. The audit should therefore distinguish:

1. Identity consistency: whether visual and verbal codes are recognisable.
2. Promise consistency: whether the same central value proposition is communicated.
3. Experience consistency: whether actual delivery supports the promise.
4. Portfolio consistency: whether sub-brands clarify rather than cannibalise or confuse.
5. Cultural consistency: whether employee behaviour reinforces the external claim.

A simple inventory matrix can rate each touchpoint against the intended brand promise. Such ratings are managerial judgements, not objective measurements; they should be supported by evidence such as content coding, customer feedback or mystery shopping.

2.3 Internal interviews and document analysis

Internal research should include senior managers, frontline employees, sales teams, product owners, customer service staff and channel partners. Different groups often hold different versions of the brand. Interview protocols should explore the target customer, category frame, distinctive assets, reasons for choice, main competitors, unacceptable experiences and evidence for claimed strengths.

Document analysis should compare strategy documents with actual execution. Common findings include outdated personas, contradictory claims, campaigns aimed at incompatible segments, inconsistent product naming, or an innovation pipeline that does not support the stated positioning.

3. Exploratory studies: learning what the numbers cannot show

Quantitative tracking can tell managers that trust declined; exploratory research helps explain what “trust” means to respondents and which experiences damaged it. Exploratory work is therefore essential before finalising a questionnaire or interpreting unexpected movement.

3.1 Qualitative methods

Suitable methods include:

• Semi-structured interviews: useful for personal histories, sensitive categories and expert stakeholders.
• Focus groups: useful for observing language, social norms and competing interpretations, although group conformity can distort findings.
• Ethnography and contextual observation: useful where behaviour differs from stated attitudes.
• Mobile diaries: useful for capturing experiences close to the moment of use.
• Digital conversation analysis: useful for identifying recurring topics, but online participants are not automatically representative of the market.
• Projective and metaphor techniques: useful for surfacing implicit meanings, provided interpretation is cautious.
• Customer journey workshops: useful for locating moments of friction and emotional significance.

Qualitative samples are selected for relevance and variation rather than statistical representation. Researchers should specify why participants were included and when thematic saturation was judged sufficient. Saturation is a reasoned methodological conclusion, not a fixed number of interviews.

3.2 From language to measurable constructs

Exploratory findings should be translated into clearly defined constructs. “Modern,” “good service” and “premium” may have different meanings across segments. Construct development involves:

1. collecting natural customer language;
2. identifying recurring dimensions and contrasting cases;
3. distinguishing related concepts, such as trust and reliability;
4. writing items that are specific and understandable;
5. piloting the items for comprehension and response quality; and
6. testing whether the items behave consistently and validly.

Reliability is not the same as validity. A set of items can produce consistent answers while measuring the wrong concept. Researchers should consider content validity, convergent validity, discriminant validity and criterion-related validity where appropriate.

3.3 Quantitative exploratory and diagnostic research

A survey may estimate awareness, salience, consideration, associations, perceived quality, trust, distinctiveness, relevance, satisfaction, advocacy and behavioural intention. Sampling should reflect the decision population. A sample of existing customers cannot estimate awareness among non-users, and a social-media sample may overrepresent highly engaged individuals.

Question order, wording, scale labels, recall periods and interviewer mode can affect results. Tracking studies should preserve core wording and procedures whenever trend continuity is important. Changes may be necessary, but they create a break in the series and should be documented.

4. Competitive benchmarking and equity-gap diagnosis

Benchmarking asks “compared with what?” At least four reference types are useful:

4.1 Comparable measurement

A valid comparison requires comparable definitions, populations, timing, question wording and data treatment. If Brand A’s awareness is aided and Brand B’s is unaided, their percentages should not be presented as equivalent. Similarly, a competitor’s social engagement rate is not automatically comparable if reach, platform mix or paid activity differs.

The basic proportion for a binary metric is:

where p is the estimated proportion, x is the number of respondents or cases with the characteristic, and n is the total valid number of observations. The result is often reported as a percentage, 100p%. This formula assumes that cases are defined consistently and that missing or ineligible observations are handled transparently.

For a simple difference between a brand and a benchmark:

where ∆ is the percentage-point gap. A percentage-point gap must not be confused with relative percentage change. If consideration rises from 20% to 25%, the increase is 5 percentage points but 25% relative to the original level.

4.2 Gap categories

A useful diagnostic separates at least five gaps:

• Awareness gap: the brand is not sufficiently known or mentally available.
• Meaning gap: consumers know the brand but do not associate it with the desired benefits.
• Distinctiveness gap: the brand’s associations are relevant but not unique.
• Experience gap: delivered performance falls below the communicated promise.
• Conversion gap: positive perceptions fail to become trial, purchase, repeat or advocacy.

The same observed outcome may result from different gaps. Low market share could reflect weak distribution, low consideration, poor value, limited availability, or an attractive competitor. Diagnosis must therefore combine customer measures with operational and market evidence.

5. Selecting brand KPIs

A KPI is not merely a metric displayed prominently. It is a measure linked to a decision, owner, review frequency and action threshold. KPI selection should begin with strategic questions:

1. What brand outcome is the strategy intended to change?
2. Which customer or market mechanism is expected to produce that change?
3. What evidence would show progress before financial results appear?
4. What action is available if the measure worsens?
5. What is the cost and reliability of measurement?

5.1 A balanced KPI hierarchy

A practical hierarchy includes:

Input measures: share of voice, reach, distribution coverage, campaign exposure, training completion and service capacity.

Mental measures: unaided and aided awareness, salience, association strength, relevance, perceived quality, trust and distinctiveness.

Relationship measures: consideration, preference, satisfaction, customer effort, perceived fairness, willingness to recommend and emotional connection.

Behavioural measures: trial, conversion, repeat purchase, retention, churn, share of wallet, search behaviour and complaint resolution.

Financial measures: revenue, gross margin, price premium, customer acquisition cost, customer lifetime value, market share and cash-flow contribution.

No universal KPI set exists. A launch may prioritise reach, awareness and trial; a mature subscription brand may prioritise retention, usage depth, trust and lifetime value; a repositioning programme may prioritise associations and consideration before sales effects are expected.

5.2 Useful calculations

A retention rate over a defined period can be expressed as:

where C_start is the number of customers at the beginning, C_end is the number at the end, and N_acquired is the number of new customers acquired during the period. The formula assumes that the customer definition and observation period are stable. If acquisitions are not known reliably, a simpler end-over-start ratio may be reported, but it should not be called a pure retention rate.

Net Promoter Score is commonly calculated as:

Respondents answer a recommendation question on a 0–10 scale; promoters are typically scores 9–10 and detractors 0–6. The score ranges from −100 to +100. NPS is a descriptive loyalty indicator, not a complete measure of loyalty, profitability or causal advocacy. Organisations should report the response distribution and sampling method, not only the single score.

A percentage-point change is:

where Metric_t is the current period value and Metric_t−1 is the preceding comparable value. Comparability requires consistent definitions, population, timing and weighting.

5.3 Leading and lagging indicators

A dashboard should show the assumed pathway between indicators. For example, declining distinctive association may precede lower consideration, which may precede weaker conversion. However, temporal sequence alone does not prove causation. Seasonality, competitor activity, economic conditions and measurement changes may affect all measures.

Managers should define alert thresholds in advance where possible. A threshold should reflect materiality, expected sampling variation, business consequences and the organisation’s ability to respond. Random fluctuation should not trigger expensive action, while a small movement in a strategically critical segment may deserve attention.

6. Designing executive dashboards and scorecards

A dashboard is an information product designed for rapid interpretation and action. It is not a warehouse of every available metric.

6.1 Dashboard principles

An effective executive dashboard should:

• begin with strategic objectives rather than available data;
• show a limited number of high-value indicators;
• distinguish current value, target, prior period and benchmark;
• show trends and meaningful segmentation;
• make definitions, sources and dates accessible;
• signal uncertainty and data quality;
• assign owners and recommended actions; and
• permit drill-down without overwhelming the executive view.

A scorecard is particularly useful when measures are organised against strategic dimensions. For example:

The numbers in this table are illustrative, not empirical findings.

6.2 Composite indices and weighting

A composite equity index can be useful when leadership needs a high-level signal, but it should not conceal important weaknesses. If an organisation creates an index, it must specify the indicators, scales, direction, missing-data rules and weights.

A weighted index may be represented as:

where I is the composite index, z_i is the standardised value of indicator i, w_i is its weight, and k is the number of indicators. Usually weights are non-negative and sum to 1. Standardisation is necessary when indicators use different units; the method, such as rescaling to 0–100 or using a z-score, must be documented. Equal weights are not automatically neutral: they imply that each indicator contributes equally to the index.

Composite indices should be accompanied by their component measures. A stable total score can hide a serious decline in trust offset by a rise in awareness.

6.3 Visual and interpretive design

Use line charts for trends, bars for comparisons and scatterplots for relationships. Avoid dual axes unless the relationship is clearly explained. Use colour sparingly and do not rely on colour alone to communicate status. A red indicator should lead to a defined question: what changed, why might it have changed, and what decision is required?

A mature dashboard has three levels:

1. Executive view: a small set of strategic outcomes and alerts.
2. Management view: diagnostic metrics by segment, channel, journey stage and region.
3. Analyst view: sampling details, item-level data, statistical tests, coding decisions and metadata.

7. Continuous tracking systems

Tracking design involves a tension between continuity and responsiveness. Stable measures enable trend analysis; new measures capture emerging risks. A sensible system preserves a core questionnaire while reserving a smaller rotating module for current issues.

7.1 Tracking frequency and design

Frequency depends on decision speed, category volatility, sample cost and expected effect. Monthly tracking may be justified for a fast-moving digital service, whereas quarterly or semiannual measurement may be adequate for durable goods. Continuous tracking does not mean that every measure must be collected continuously.

The design may be cross-sectional, with a new sample each wave, or longitudinal, with the same respondents followed over time. Cross-sectional designs are generally simpler and reduce panel conditioning; longitudinal designs help examine individual change but face attrition and repeated-measurement effects.

7.2 Sampling and statistical caution

Managers should report sample size, fieldwork dates, quotas or probability procedures, weighting, response rate where available, and exclusions. A difference between waves may reflect sampling error. For a simple proportion under a basic random-sampling approximation, the standard error is:

where p is the sample proportion and n is the sample size. This approximation assumes independent observations and is not sufficient by itself for complex samples, weighting, clustering or non-probability panels. Confidence intervals and significance tests should therefore be designed by a statistician or researcher familiar with the sampling method.

Statistical significance is not the same as managerial significance. A tiny difference may be statistically detectable in a very large sample but irrelevant to a decision. Conversely, an important movement in a small segment may be uncertain and require additional evidence rather than immediate rejection.

7.3 Data integration

A tracking system may integrate survey data, transaction records, media exposure, web analytics, customer service logs, social listening and distribution data. Integration should use clear entity definitions and time windows. “Customer,” “active user,” “purchase,” “reach” and “conversion” must be defined consistently.

Data integration does not automatically establish causality. Exposed customers may differ from unexposed customers before a campaign. Where feasible, use experiments, matched comparisons, time-series analysis or quasi-experimental designs. Even then, assumptions must be stated.

8. Illustrative case: Northstar Mobile

Background

Northstar Mobile, a fictional regional telecommunications provider, had strong aided awareness but declining consideration among customers aged 18–34. Management initially proposed increasing advertising expenditure.

Problem

The existing dashboard contained reach, impressions, website visits and monthly revenue, but no consistent measure of distinctive associations, service friction or competitor comparison. Different agencies reported incompatible awareness figures. Executives could see that performance had weakened but not why.

Analysis

The audit team conducted an inventory of campaigns, tariffs, app journeys, retail materials, complaint scripts and employee training. It found that advertising promised simplicity, while tariff structures contained complex conditions. The app used a different tone and visual system from retail stores. A qualitative study found that younger users associated Northstar with reliable coverage but also with confusing billing and slow complaint resolution.

A quarterly survey then measured unaided awareness, aided awareness, consideration, perceived simplicity, reliability, trust, distinctiveness, complaint experience and competitor choice. The survey used stable wording and reported sample, fieldwork dates and weighting. Results showed that Northstar led competitors on reliability but lagged on simplicity and complaint resolution. The consideration gap was concentrated among recent complainants rather than the entire youth segment.

Approach and solution

The company redesigned the dashboard around a causal pathway:

• reliability and simplicity associations;
• complaint resolution satisfaction and customer effort;
• consideration and switching intention;
• retention and revenue per account.

The executive view retained eight measures. A diagnostic view separated new customers, long-term customers and complainants. The firm also created a monthly operational measure for unresolved complaints and a quarterly brand study for perceptions. Advertising was not abandoned, but budget was shifted toward simplifying tariff communication and improving service recovery.

Outcome

After two tracking waves, awareness changed little, but perceived simplicity and complaint resolution improved. Consideration rose among customers without recent complaints and stabilised among complainants. Management concluded that the principal constraint had been an experience gap, not an awareness gap. The case does not prove that every improvement was caused by the dashboard; concurrent operational changes and market conditions were also possible influences. Its value lies in demonstrating how an audit can redirect investment from a visible symptom to a diagnosed mechanism.

Learning points

1. High awareness can coexist with weak consideration.
2. Inventory evidence must include actual customer experience, not only communication assets.
3. Competitor comparisons require equivalent definitions.
4. Dashboards are most valuable when each KPI has an owner and an action.
5. Tracking should combine stable trend measures with focused diagnostic research.

9. Governance, ethics and contemporary developments

Brand measurement involves personal data, inferred traits and potentially sensitive behavioural records. Organisations should collect only data necessary for a legitimate purpose, obtain appropriate consent where required, protect identities, control access and observe applicable privacy and consumer-protection law. Data retention periods and deletion processes should be defined.

Social listening and AI-based sentiment analysis require special caution. Automated classification may misread sarcasm, dialect, multilingual content or minority viewpoints. Sentiment scores can also obscure the reasons behind a conversation. Generative AI may assist with coding, summarisation and anomaly detection, but human researchers must validate outputs, protect confidential data and disclose material methodological changes.

Algorithmic dashboards can reproduce bias when historical data reflect unequal service, unequal visibility or unequal sampling. Segment reporting should therefore be used to identify inequity rather than to justify discriminatory treatment. Brand claims, sustainability statements and comparative advertising require substantiation. A dashboard should never encourage managers to optimise a metric through practices that damage customers or misrepresent performance.

Contemporary tracking increasingly uses first-party data, privacy-preserving measurement, experimentation, digital journey analytics and integrated customer data platforms. These developments improve timeliness but increase risks of over-measurement, identity resolution errors and false precision. The governing principle remains methodological fitness: use the least intrusive method capable of answering the decision question.

Best-practice checklist

Before approving a brand audit or dashboard, ask:

• Is the strategic question explicit?
• Are intended brand promise and actual delivery both documented?
• Are customer, competitor and category definitions comparable?
• Are qualitative findings used to improve quantitative measures?
• Does each KPI have a decision owner and response rule?
• Are leading and lagging indicators connected without claiming unsupported causality?
• Are trend breaks, weighting and missing data documented?
• Can executives see the signal while analysts can inspect the evidence?
• Are privacy, fairness, accessibility and claim-substantiation requirements met?
• Is the system periodically reviewed for obsolete measures and unintended incentives?

Chapter Summary

Brand audits integrate internal brand inventories with external explorations of customer and stakeholder perceptions. The inventory documents identity, portfolio, communications, channels, experiences, employees and governance as they actually operate. Exploratory research supplies the language and explanations needed to interpret quantitative results and develop valid measures.

Competitive benchmarking is meaningful only when populations, definitions, timing and measurement procedures are comparable. Equity gaps may involve awareness, meaning, distinctiveness, experience or conversion, and the same commercial symptom can arise from several different causes. KPI selection should connect strategic objectives to leading indicators, customer responses, behaviours and financial outcomes.

Executive dashboards and scorecards should be selective, transparent, trend-oriented and action-linked. Composite indices may summarise performance, but their components, weights and standardisation rules must remain visible. Continuous tracking requires disciplined sampling, stable core measures, documented changes and appropriate interpretation of uncertainty. Finally, privacy, fairness, data quality and responsible AI use are essential conditions of credible brand evaluation.

Key Terms

Brand audit: A systematic assessment of a brand’s internal activities, market perceptions and performance.

Brand inventory: An internal record of brand assets, activities, touchpoints and intended effects.

Brand exploratory: External research into how customers and stakeholders understand and experience a brand.

Brand tracking: Repeated measurement of brand indicators across time.

Benchmark: A reference standard used for comparison.

Leading indicator: A measure expected to change before a later outcome.

Lagging indicator: A measure that records an outcome after it occurs.

Diagnostic measure: A measure used to explain performance or identify a mechanism.

Mental availability: The probability that a brand comes to mind in relevant buying situations.

Distinctiveness: The extent to which brand associations identify the brand as meaningfully different.

Experience gap: A difference between the promised and delivered brand experience.

Conversion gap: Failure of positive perceptions to become desired behaviour.

KPI: A key performance indicator linked to an important objective and decision.

Scorecard: A structured display of measures, targets, benchmarks, owners and actions.

Dashboard: A visual information system designed to support monitoring and decisions.

Composite index: A summary measure formed from multiple indicators.

Sampling error: Variation arising because a sample rather than the whole population is observed.

Data governance: Policies and controls governing data quality, access, privacy and use.

Review Questions

Short Answer Questions

1. What is the difference between a brand inventory and a brand exploratory?
2. Why should a dashboard distinguish leading indicators from lagging indicators?
3. Define an experience gap and give one example.
4. What conditions are necessary for a valid competitor benchmark?
5. Why is reliability not sufficient to establish measurement validity?

Long Answer Questions

1. Explain how an organisation can conduct a brand inventory across identity, communication, experience and portfolio dimensions.
2. Discuss the role of qualitative exploratory research in designing and interpreting a brand-tracking system.
3. Develop a KPI hierarchy for a mature subscription brand and justify the inclusion of each level.
4. Explain the principles of an effective executive brand dashboard, including the role of targets, benchmarks, owners and action thresholds.
5. Critically evaluate the benefits and limitations of composite brand-equity indices.

Analytical / Application Questions

1. A brand has high awareness, low consideration and rising complaints. Design an audit plan to determine whether the main problem is meaning, experience or conversion.
2. A tracking study reports that consideration increased from 20% to 25%. Calculate and interpret both the percentage-point change and the relative percentage change. What additional evidence would you require before recommending increased investment?
3. Design a scorecard for a brand repositioning programme. Include at least two mental, two behavioural and one financial KPI, and explain how the measures could be linked without assuming causality.

Multiple Choice Questions with Answers

Question 1

Which activity is the primary purpose of a brand inventory?

A. To record internal brand assets, activities, touchpoints and intended effects

B. To estimate the statistical significance of a competitor’s sales

C. To replace all customer research with management opinion

D. To calculate customer lifetime value from financial statements only

Correct Answer: A. To record internal brand assets, activities, touchpoints and intended effects

Question 2

A brand is well known but is not associated with the benefit it wants to own. Which gap is most directly indicated?

A. Distribution gap

B. Meaning gap

C. Sampling gap

D. Retention gap

Correct Answer: B. Meaning gap

Question 3

If a brand’s consideration rises from 20% to 25%, what is the change in percentage points?

A. 5 percentage points

B. 20 percentage points

C. 25 percentage points

D. 125 percentage points

Correct Answer: A. 5 percentage points

Question 4

Which condition is essential for a meaningful competitor benchmark?

A. Using different definitions for each competitor

B. Comparing measurements with comparable populations and procedures

C. Using only the competitor’s advertising expenditure

D. Excluding the category average

Correct Answer: B. Comparing measurements with comparable populations and procedures

Question 5

In the formula NPS = %Promoters − %Detractors, promoters are conventionally respondents scoring:

A. 0–3

B. 4–6

C. 7–8

D. 9–10

Correct Answer: D. 9–10

Question 6

What is the principal advantage of retaining a stable core in a tracking questionnaire?

A. It guarantees causal conclusions

B. It supports interpretation of trends over time

C. It eliminates sampling error

D. It prevents all respondent bias

Correct Answer: B. It supports interpretation of trends over time

Question 7

Which is a lagging indicator?

A. Campaign reach

B. Training completion

C. Customer retention during the completed period

D. Intended brand association

Correct Answer: C. Customer retention during the completed period

Question 8

Why should a composite equity index display its component measures?

A. To conceal weak dimensions from executives

B. To ensure that a total score does not hide important declines

C. To eliminate the need for benchmarks

D. To make all indicators use different scales

Correct Answer: B. To ensure that a total score does not hide important declines

Question 9

What does the formula SE(p) = √[p(1 − p) / n] estimate under its stated basic assumptions?

A. The standard error of a sample proportion

B. The brand’s price premium

C. The percentage-point benchmark gap

D. The customer retention rate

Correct Answer: A. The standard error of a sample proportion

Question 10

Which dashboard practice best supports managerial action?

A. Displaying every available metric on the executive screen

B. Showing KPIs with owners, targets, trends and defined responses

C. Removing methodology and data-quality information

D. Using a single composite score without components

Correct Answer: B. Showing KPIs with owners, targets, trends and defined responses