Foundations of Web and Digital Marketing Analytics

Introduction

Digital marketing analytics is the systematic use of data, analytical methods, and managerial judgment to understand digital interactions and improve marketing decisions. It includes the measurement of website behaviour, search activity, digital advertising, social media, email, mobile applications, electronic commerce, and the relationships among these channels.

The subject is broader than reporting how many people visited a website. A graduate-level analyst must connect digital observations to business outcomes: revenue, profitability, customer lifetime value, retention, brand development, service quality, and strategic growth. This requires an understanding of the digital marketing ecosystem, customer behaviour, measurement design, attribution, experimentation, privacy, and organizational decision-making.

Digital analytics is also a field of inference. A recorded click is an observation, not automatically evidence of causation. A campaign may coincide with increased sales without causing them; a high conversion rate may result from a small and unrepresentative audience; and a low-cost acquisition channel may produce customers with poor retention. Sound practice therefore combines data quality, conceptual models, statistical reasoning, business knowledge, and ethical governance.

Learning Objectives

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

1. Explain the digital marketing ecosystem and its principal participants.
2. Distinguish paid, owned, and earned media and evaluate their interactions.
3. Define key web and digital marketing analytics terminology.
4. Describe customer journeys, marketing funnels, and their limitations.
5. Apply the RACE and See–Think–Do–Care frameworks to planning and measurement.
6. Select metrics that correspond to managerial objectives rather than merely reporting available data.
7. Interpret basic performance measures and recognize attribution, privacy, and data-quality limitations.
8. Use analytics to support managerial decisions while preserving appropriate human judgment.

1. The Digital Marketing Ecosystem

The digital marketing ecosystem is a network of organizations, technologies, platforms, data flows, audiences, and regulatory institutions. It includes the following components:

• Organizations and brands: firms that define objectives, develop offerings, communicate value, and serve customers.
• Audiences and customers: individuals, households, business buyers, communities, and other stakeholders whose behaviour and consent shape the system.
• Digital channels: websites, search engines, social networks, marketplaces, email, applications, messaging services, video platforms, and connected devices.
• Intermediaries: advertising exchanges, agencies, publishers, influencers, payment providers, customer-data platforms, and technology vendors.
• Measurement systems: web analytics, application analytics, advertising-platform reports, customer relationship management systems, marketing automation, surveys, experiments, and financial systems.
• Rules and institutions: privacy law, consumer-protection law, platform policies, industry standards, and organizational governance.

These components are interdependent. A social post may direct a user to a website; the website may place the user in a remarketing audience; an email may later complete the sale; and the customer relationship management system may record a purchase and subsequent service interactions. No individual platform necessarily provides a complete account of the journey.

A useful distinction is between touchpoints and outcomes. A touchpoint is an interaction, such as an impression, visit, video view, click, product comparison, or customer-service conversation. An outcome is a result that matters to the organization, such as a qualified lead, purchase, renewal, contribution margin, or increase in brand consideration. Analytics creates value when it links touchpoints to outcomes without overstating what the data can prove.

Data-generating layers

Digital marketing data commonly arise at four layers:

The layers should not be confused. Reach is not attention, attention is not intent, and intent is not revenue. A manager should define the intended progression before selecting indicators.

2. Essential Terminology

Terminology varies across platforms, so definitions must be documented in an organization’s measurement plan.

• User or visitor: a person or device identity recognized by a measurement system. It may not correspond perfectly to a unique human.
• Session: a defined period of interaction with a digital property. Session rules differ by platform.
• Page view or screen view: a recorded loading or viewing event for a webpage or application screen.
• Event: a recorded interaction, such as a download, video start, form submission, or product selection.
• Impression: one recorded opportunity for an advertisement or content item to be displayed.
• Reach: the number of distinct people or accounts exposed, subject to identity and platform limitations.
• Frequency: the average number of impressions per reached entity. In a simple setting, frequency is impressions divided by reach.
• Click-through rate (CTR): clicks divided by impressions, usually expressed as a percentage.
• Conversion: a defined desired action, such as a purchase, registration, or qualified lead.
• Conversion rate: conversions divided by a specified denominator, such as sessions, users, clicks, or leads. The denominator must always be stated.
• Cost per click (CPC): advertising spend divided by recorded clicks.
• Cost per acquisition (CPA): advertising spend divided by attributed acquisitions or conversions.
• Return on advertising spend (ROAS): attributed revenue divided by advertising spend. It is a revenue-to-spend ratio, not a profit measure.
• Bounce or engagement measure: a platform-specific measure of limited or meaningful interaction. Definitions have changed across analytics systems and should not be assumed to be equivalent.
• Attribution: the assignment of credit for an outcome to one or more marketing interactions.
• Cohort: a group sharing a relevant characteristic or starting period, such as customers acquired in January.
• Lifetime value (LTV or CLV): an estimate of the economic value of a customer relationship over a defined horizon.
• First-party data: data collected directly by an organization through its own interactions and systems.
• Consent: a legally and ethically relevant permission for specified data processing, where required.
• Data warehouse or lake: an infrastructure environment for storing and integrating data for analysis.

A metric becomes a key performance indicator (KPI) only when it is linked to a decision, objective, owner, time frame, and target. Thus, sessions may be useful descriptive measures but are not automatically KPIs.

3. Paid, Owned, and Earned Media

The paid-owned-earned framework classifies media according to control and acquisition mechanism.

Paid media

Paid media consists of exposure purchased from another party. Examples include search advertisements, display advertising, sponsored social posts, video advertising, affiliate commissions, and paid influencer placements. Paid media offers scalability, targeting, speed, and relatively clear cost information. Its limitations include auction inflation, fraud, ad avoidance, platform dependence, and the risk of optimizing for cheap interactions rather than valuable customers.

Owned media

Owned media consists of digital properties controlled by the organization, such as a website, application, email list, blog, customer portal, or branded community. Owned media permits greater control over content, experience, data collection, and relationship development. It is not costless: technology, content, accessibility, maintenance, security, and staff resources require investment. Control also does not guarantee attention or trust.

Earned media

Earned media is attention or advocacy generated by customers, journalists, creators, communities, or other external parties rather than directly purchased or fully controlled by the brand. Examples include organic search visibility, reviews, recommendations, editorial coverage, reposts, and user-generated content. Earned media can provide credibility and network effects, but it is difficult to guarantee, may be negative, and can be manipulated or inauthentic.

The categories frequently overlap. A brand may pay an influencer to create content that is then shared organically; a product page may be owned media whose discovery depends on paid search; and an earned review may appear on a platform that the firm does not control. The framework is therefore a planning aid rather than a claim that every touchpoint has one exclusive category.

Effective strategies coordinate the three types. Paid media can introduce an audience to owned content; owned experiences can convert interest; and satisfactory experiences can produce earned advocacy.

4. Customer Journeys and Marketing Funnels

A customer journey describes how an individual or organizational buyer moves through experiences before, during, and after a decision. It may include problem recognition, information search, comparison, purchase, onboarding, usage, service, renewal, and recommendation.

A funnel is a simplified representation of decreasing populations as people move toward a desired action. A conventional funnel may use awareness, consideration, conversion, retention, and advocacy stages. Funnels are useful because they encourage managers to identify stage-specific objectives and leakage. However, they can misrepresent contemporary behaviour:

1. Customers do not always proceed linearly.
2. People may enter through comparison, recommendation, or purchase rather than awareness.
3. Post-purchase service can influence new prospects through reviews.
4. Several people may participate in a business-to-business decision.
5. A customer may move backward, repeat a stage, or use multiple devices.

Consequently, the funnel should be treated as a measurement scaffold, not a literal psychological law. Journey maps add context by recording customer questions, emotions, barriers, touchpoints, and organizational processes at each stage.

A stage-to-measurement logic

At an awareness stage, reach, qualified impressions, brand search, and aided or unaided awareness may be relevant. At consideration, product-page engagement, comparison activity, content completion, and qualified visits may matter. At conversion, completed orders, qualified leads, conversion rate, acquisition cost, and margin are more useful. At retention, repeat purchase, renewal, churn, service resolution, and customer value become central. The appropriate metric depends on the decision and the stage, not on the ease with which a platform reports it.

5. The RACE Framework

RACE is a practical planning and measurement framework consisting of Reach, Act, Convert, and Engage. It is often used to connect channel activity with customer lifecycle management.

Reach

Reach concerns building awareness and attracting relevant audiences. Activities may include search optimization, paid media, public relations, social distribution, partnerships, and content. Possible measures include qualified reach, impressions, frequency, brand-search change, and cost per qualified visit.

Act

Act represents interactions that indicate interest or intent before the primary conversion. Examples include reading product information, using a calculator, downloading a guide, beginning an application, or adding an item to a cart. Measures can include engaged visits, lead starts, content completion, return visits, and micro-conversion rates.

Convert

Convert covers the primary business action: purchase, booking, application, subscription, donation, or qualified lead. Measures should include volume, conversion rate, revenue, margin, cost per acquisition, and, where possible, incremental contribution.

Engage

Engage concerns continued relationship quality and advocacy. Relevant measures include repeat purchase, renewal, active use, customer satisfaction, referral, complaint resolution, and customer lifetime value.

RACE is valuable because it prevents an acquisition-only view of marketing. Its limitation is that stage boundaries may remain ambiguous, and it does not itself establish causality or prescribe a statistical attribution method. Managers must define events, identities, targets, and decision rules for each stage.

6. The See–Think–Do–Care Model

The See–Think–Do–Care model segments audiences by intent rather than merely by channel.

• See: the largest addressable audience with a relevant need or potential interest, but no immediate intent. The objective is useful awareness and mental availability.
• Think: people considering a category or solution. The objective is education, comparison, and confidence.
• Do: people showing strong intent or ready to act. The objective is to remove friction and facilitate conversion.
• Care: existing customers or supporters whom the organization seeks to retain, develop, and encourage to advocate.

The model helps align content and measurement with audience intent. For example, a broad educational video may be suitable for See; a comparison guide for Think; a clear product page and streamlined checkout for Do; and onboarding or personalized service for Care.

The framework should not be used to label people permanently. A customer may be in Care for one product and Think for another. Nor should managers assume that all See activity must produce immediate conversions. Early-stage activity may be evaluated through reach quality, attention, brand effects, and later assisted behaviour, while maintaining realistic expectations about measurement uncertainty.

RACE and See–Think–Do–Care are complementary. RACE emphasizes the organization’s lifecycle process; See–Think–Do–Care emphasizes audience intent. A planning matrix can use both: See/Reach, Think/Act, Do/Convert, and Care/Engage, while recognizing that the mapping is approximate rather than mandatory.

7. Metrics, Measurement Design, and Basic Quantitative Reasoning

A rigorous measurement system begins with a decision question. Examples include:

• Should budget be shifted from one acquisition channel to another?
• Does a redesigned checkout increase completed orders?
• Which customer cohort has the greatest long-term value?
• Is a campaign creating incremental demand or merely claiming existing demand?

The analyst then defines the outcome, population, time period, identity rules, event specification, data sources, and analysis method. A measurement plan should document the metric name, operational definition, numerator, denominator, source, owner, refresh schedule, and known limitations.

Core formulas

For a campaign with I impressions and C clicks, click-through rate is:

where CTR is click-through rate, C is recorded clicks, and I is impressions. The formula applies when clicks and impressions refer to the same campaign, period, and counting rules. It measures recorded response, not sales impact.

For V conversions and N eligible opportunities:

The denominator may be sessions, users, clicks, leads, or orders initiated. These alternatives produce different rates; therefore, “conversion rate” without its denominator is incomplete.

For spend S and conversions V:

CPA has currency per conversion as its unit. It is meaningful only if conversions are defined consistently and are sufficiently valuable to the business.

For attributed revenue R and advertising spend S:

ROAS is dimensionless when both values are expressed in the same currency. It excludes many costs unless they are included in the chosen revenue or cost definitions, so it should not be interpreted as profit or return on investment.

Worked example

Suppose a campaign records 200,000 impressions, 4,000 clicks, 160 purchases, advertising spend of £12,000, and attributed revenue of £36,000.

• CTR = 4,000 ÷ 200,000 × 100% = 2%.
• Click-to-purchase conversion rate = 160 ÷ 4,000 × 100% = 4%.
• CPA = £12,000 ÷ 160 = £75 per purchase.
• ROAS = £36,000 ÷ £12,000 = 3.0, or £3 of attributed revenue per £1 of advertising spend.

These results do not establish that the campaign caused all 160 purchases. They also do not show profitability. If product, fulfilment, platform, agency, and service costs exceed the contribution generated by the purchases, a ROAS of 3.0 may still be economically unattractive.

From descriptive to causal analytics

Descriptive analytics reports what happened. Diagnostic analytics investigates why it happened. Predictive analytics estimates what may happen. Prescriptive analytics evaluates possible actions. These categories overlap in practice, but the distinction is useful for managerial communication.

Causal questions require stronger designs than simple platform attribution. Randomized experiments, geo-experiments, holdout groups, interrupted time series, matched comparisons, and carefully specified econometric models can help estimate incremental effects. Each has assumptions. For example, an experiment requires appropriate randomization, a sufficiently stable treatment, valid outcome measurement, and attention to noncompliance and interference. Observational models may adjust for measured differences but cannot guarantee removal of unmeasured confounding.

8. Attribution and Decision-Making

Attribution assigns credit for a conversion to marketing interactions. Common rules include:

• Last-touch attribution: all credit goes to the final recorded interaction.
• First-touch attribution: all credit goes to the first recorded interaction.
• Linear attribution: credit is distributed equally across selected interactions.
• Position-based attribution: greater credit is assigned to specified beginning and ending interactions.
• Data-driven attribution: credit is estimated using observed patterns and a model.

These are allocation conventions, not automatically causal estimates. Last-touch attribution may undervalue demand creation; first-touch may undervalue conversion assistance; and data-driven systems can inherit tracking gaps, selection bias, and platform incentives. A useful managerial practice is to compare attribution reports with incrementality evidence, customer research, cohort outcomes, and financial contribution.

Analytics supports decisions at several levels:

The objective is not to maximize every metric. It is to choose actions that improve organizational outcomes subject to budget, capacity, customer experience, and legal constraints.

9. Data Architecture and Governance

A typical digital measurement architecture may include tags or software development kits, consent-management tools, web and application analytics, advertising platforms, customer relationship management, transaction systems, a warehouse, and reporting or activation tools. Data integration enables analysis across touchpoints but also creates identity, timing, duplication, and access risks.

Important governance practices include:

1. Clear event taxonomy: event names and properties should have stable meanings.
2. Data quality controls: monitor missing values, duplicate transactions, sudden volume changes, invalid referrers, and inconsistent timestamps.
3. Identity discipline: distinguish users, devices, accounts, households, and sessions; do not imply certainty that the system cannot support.
4. Consent and purpose limitation: collect and use data for legitimate, communicated purposes.
5. Access control: restrict sensitive data according to role and business need.
6. Retention and deletion: maintain data only as long as justified and technically support deletion requests where applicable.
7. Documentation: record definitions, transformations, model assumptions, and changes.
8. Reconciliation: compare digital transactions with authoritative commercial or financial systems.

Privacy regulation varies by jurisdiction, but responsible practice generally includes transparency, lawful processing, data minimization, security, rights management, and careful treatment of sensitive attributes. Consent banners alone do not make an analytics program ethical. Dark patterns, manipulative personalization, discriminatory targeting, covert profiling, and excessive surveillance can damage autonomy and trust even when technically permitted.

10. Contemporary Developments and the Role of AI

The digital measurement environment is changing because of privacy regulation, browser restrictions, platform changes, reduced availability of third-party identifiers, connected devices, retail media, and artificial intelligence. Organizations increasingly emphasize first-party relationships, consented data, modeled measurement, clean rooms, and aggregated experimentation.

AI can support classification, forecasting, anomaly detection, recommendation, content generation, and customer-service analysis. Its use requires attention to training-data quality, bias, explainability, security, intellectual property, hallucination, and human accountability. An automated optimization system may improve a platform metric while harming profitability, accessibility, or a vulnerable group. Consequently, AI should be governed by defined objectives, constraints, monitoring, auditability, and escalation procedures.

The loss of easily available identifiers does not eliminate analytics; it changes the unit of analysis and the evidence available. Analysts must become more capable in experimentation, survey design, cohort analysis, media-mix approaches, data governance, and uncertainty communication.

11. Illustrative Case: Northstar Outdoor Equipment

Background

Northstar Outdoor Equipment, a fictional mid-sized retailer, sells hiking equipment through stores and an online site. The company invests in paid search, social advertising, email, organic search, product content, and a loyalty program. Management reports rising digital traffic but stagnant profit.

Problem

The marketing team recommends increasing investment in paid social because its dashboard reports many attributed purchases at a low CPA. The finance team observes that repeat purchase and gross contribution from these customers are weak. The teams also use different definitions of a conversion and cannot reconcile platform revenue with the order system.

Analysis

The analyst begins with a measurement audit. Platform purchases include cancelled orders, while the financial system records completed orders. The social platform uses a seven-day click and one-day view attribution window; email uses last click; and organic search receives no modeled credit. As a result, several channels can claim the same purchase.

The analyst constructs customer cohorts based on acquisition month and channel, using completed orders and contribution margin. The analysis shows that paid social produces inexpensive first purchases but lower 90-day repeat rates. Organic search and email produce fewer reported first purchases but stronger repeat behaviour. The team then identifies a deeper journey pattern: many customers first discover a product through social content, later conduct non-branded searches, read product guides, and finally purchase after an email reminder.

Approach and solution

Northstar adopts a shared taxonomy and reconciles orders daily with the financial system. It reports both platform-attributed outcomes and a management view based on completed orders and contribution. It runs a randomized holdout test for a portion of paid social audiences and uses a separate test to evaluate the redesigned email reminder. The organization also introduces a cohort dashboard showing first-order contribution, 90-day repeat purchase, refund rate, and service contacts.

Rather than eliminating paid social, Northstar changes its objective. Social is evaluated partly as demand creation and content distribution, not solely as a last-step sales channel. Budget is shifted incrementally toward audience groups that demonstrate profitable downstream behaviour. Email and product education are improved for customers who show consideration but do not purchase. Consent preferences are respected, and personalization is limited to information necessary for the stated customer experience.

Outcome

In this illustrative case, first-month attributed revenue does not increase dramatically, but reported profit improves because budget is less concentrated in low-retention customers. Managers also gain a common view of definitions and can distinguish platform reporting from incremental evidence.

Learning points

1. A low CPA does not guarantee profitable customer acquisition.
2. Attribution windows and counting rules can materially change channel results.
3. Cohort and retention analysis complement campaign reporting.
4. Paid, owned, and earned media often operate as a connected system.
5. Incrementality testing is more informative than assuming attributed credit is causal.
6. Governance and definition alignment are prerequisites for trustworthy dashboards.

Challenges and Limitations

Digital marketing analytics faces persistent limitations. Users may block cookies, use multiple devices, decline consent, or appear differently across systems. Platforms may define impressions, clicks, conversions, and users inconsistently. Data can be delayed, sampled, modeled, or altered by tracking changes. Small samples produce unstable rates, while large samples can make trivial differences appear statistically persuasive. Correlation can be mistaken for causation, and optimization can encourage short-term behaviour at the expense of long-term value.

Organizations also face cultural limitations. Teams may defend channel budgets, select favourable attribution windows, or treat dashboards as objective despite hidden assumptions. Analysts should therefore communicate uncertainty, show alternative definitions, disclose missing data, and link recommendations to decisions. Good analytics is not merely technically sophisticated; it is transparent, reproducible, commercially relevant, and ethically defensible.

Best-Practice Checklist

• Begin with a business decision and customer objective.
• Define outcomes and denominators before collecting metrics.
• Use RACE or See–Think–Do–Care to clarify stage and intent.
• Coordinate paid, owned, and earned media rather than optimizing channels in isolation.
• Reconcile digital events with transaction and financial records.
• Separate descriptive attribution from causal incrementality.
• Analyse cohorts, retention, margin, and customer experience—not only immediate conversions.
• Test important changes where feasible and state assumptions.
• Build privacy, security, accessibility, and fairness into measurement design.
• Document taxonomies, data lineage, model versions, and limitations.
• Present insights as decisions, alternatives, risks, and expected consequences.

Chapter Summary

Web and digital marketing analytics connects digital touchpoints with customer and business outcomes. The digital ecosystem includes brands, audiences, platforms, intermediaries, measurement technologies, and regulatory institutions. Paid media provides purchased reach, owned media provides controlled experiences and relationships, and earned media provides externally generated attention and advocacy; the categories frequently interact.

Customer journeys are non-linear and extend beyond purchase. Funnels are useful planning abstractions but should not be treated as literal descriptions of every buyer. RACE organizes activity around Reach, Act, Convert, and Engage, while See–Think–Do–Care organizes audiences around intent. Both frameworks help align objectives, content, and metrics.

Core measures such as CTR, conversion rate, CPA, and ROAS are useful only when their numerators, denominators, time periods, and counting rules are explicit. Attribution allocates credit but does not automatically establish causation. Incrementality requires stronger designs, including experiments or carefully justified quasi-experimental approaches. Reliable analytics also depends on event taxonomies, data reconciliation, identity discipline, privacy, security, and governance.

The managerial purpose of analytics is informed action. Effective analysts combine quantitative evidence with customer understanding, financial reasoning, experimentation, ethical judgment, and clear communication of uncertainty.

Key Terms

Digital marketing analytics: The systematic use of digital data and analysis to improve marketing and business decisions.

Digital ecosystem: The connected network of organizations, audiences, platforms, technologies, data, and rules involved in digital activity.

Touchpoint: A customer interaction or exposure with a brand, channel, or process.

Paid media: Exposure purchased from an external media owner or platform.

Owned media: A digital property controlled substantially by the organization.

Earned media: Attention, advocacy, or coverage generated by external parties.

Customer journey: The sequence of customer experiences before, during, and after a decision.

Marketing funnel: A simplified stage model representing progression toward an outcome.

RACE: A framework comprising Reach, Act, Convert, and Engage.

See–Think–Do–Care: An intent-based audience framework.

Impression: A recorded opportunity for an advertisement or content item to be displayed.

Reach: The number of distinct entities exposed, subject to identity limitations.

Conversion: A defined desired action.

KPI: A metric connected to an objective, decision, owner, target, and time frame.

Attribution: The assignment of credit for an outcome to marketing interactions.

Incrementality: The additional outcome caused by an intervention compared with an appropriate counterfactual.

Cohort: A group sharing a relevant characteristic or starting period.

First-party data: Data collected directly by an organization through its own interactions.

Customer lifetime value: An estimate of economic value generated by a customer relationship over a specified horizon.

Data governance: Policies and practices that ensure data quality, lawful use, security, accountability, and appropriate access.

Review Questions

Short Answer Questions

1. What distinguishes a touchpoint from a business outcome?
2. Define paid, owned, and earned media.
3. Why is a funnel an imperfect representation of a customer journey?
4. What are the four stages of the RACE framework?
5. Why must the denominator be stated when reporting a conversion rate?

Long Answer Questions

1. Explain the digital marketing ecosystem and discuss how its participants influence the reliability of marketing analytics.
2. Compare paid, owned, and earned media in terms of control, objectives, advantages, and risks.
3. Explain the RACE and See–Think–Do–Care frameworks and show how they can be used together in a marketing plan.
4. Discuss the difference between attribution and incrementality, including the managerial risks of relying exclusively on platform attribution.
5. Evaluate the role of data governance, privacy, and ethical judgment in building a trustworthy digital analytics function.

Analytical / Application Questions

1. A campaign generates 80,000 impressions, 2,400 clicks, and 96 purchases, with spend of $7,200 and attributed revenue of $21,600. Calculate CTR, click-to-purchase conversion rate, CPA, and ROAS. Interpret what the results do and do not establish.
2. A retailer reports that paid social has the lowest CPA, while email has the highest repeat-purchase rate. Design an analysis that would determine whether increasing paid social would improve long-term profit.
3. Design a measurement plan for a subscription service using RACE and See–Think–Do–Care. Specify objectives, events, KPIs, data sources, and at least two governance safeguards.

Multiple Choice Questions with Answers

Question 1

Which statement best describes digital marketing analytics?

A. The automatic optimization of every advertising platform

B. The systematic use of data and analysis to improve marketing and business decisions

C. The measurement of website traffic without reference to business outcomes

D. The replacement of managerial judgment with dashboards

Correct Answer: B. The systematic use of data and analysis to improve marketing and business decisions

Question 2

Which is an example of owned media?

A. A sponsored search advertisement

B. An independent newspaper review

C. A company’s customer email program

D. An unpaid customer recommendation

Correct Answer: C. A company’s customer email program

Question 3

What is the principal limitation of treating a marketing funnel as a literal customer journey?

A. Funnels cannot contain measurable outcomes

B. Customers always move through exactly the same stages

C. Customer behaviour may be non-linear and involve repeated or multiple stages

D. Funnels apply only to offline marketing

Correct Answer: C. Customer behaviour may be non-linear and involve repeated or multiple stages

Question 4

In the RACE framework, which stage concerns continued relationships and advocacy?

A. Reach

B. Act

C. Convert

D. Engage

Correct Answer: D. Engage

Question 5

A campaign records 50,000 impressions and 1,000 clicks. What is its CTR?

A. 0.5%

B. 2%

C. 5%

D. 50%

Correct Answer: C. 5%

Question 6

A campaign spends $4,000 and produces 80 defined acquisitions. What is its CPA?

A. $20

B. $40

C. $50

D. $320

Correct Answer: C. $50

Question 7

Why is ROAS not automatically a measure of profit?

A. It uses a percentage rather than a ratio

B. It compares attributed revenue with advertising spend but may exclude other costs

C. It cannot be calculated for digital campaigns

D. It measures only customer satisfaction

Correct Answer: B. It compares attributed revenue with advertising spend but may exclude other costs

Question 8

Which approach is most directly intended to estimate whether a campaign caused additional outcomes?

A. Last-touch attribution

B. A randomized holdout experiment

C. Counting impressions

D. Reporting total sessions

Correct Answer: B. A randomized holdout experiment

Question 9

Which audience is most consistent with the “Do” stage of See–Think–Do–Care?

A. People with a broad potential interest but no immediate intent

B. People comparing possible solutions

C. People showing strong intent and ready to act

D. Existing customers receiving retention support

Correct Answer: C. People showing strong intent and ready to act

Question 10

Which practice most improves the interpretability of a conversion rate?

A. Removing the denominator from the report

B. Combining users, sessions, and clicks into one undefined base

C. Documenting the numerator, denominator, period, and counting rules

D. Using the largest available number as the denominator

Correct Answer: C. Documenting the numerator, denominator, period, and counting rules