5. Search Engine Marketing and Intent-Based Advertising

Introduction and Context

Search engines occupy a distinctive position in digital advertising because they mediate an expressed need. A person who types “best accounting software for a small business” is not merely exposed to a message; the person is articulating a problem, a category, and often an imminent commercial possibility. Search engine marketing (SEM) seeks to make a brand visible at this moment of expressed demand. Its effectiveness therefore depends not only on media buying, but also on interpretation: the advertiser must understand what a query means, how close the searcher is to action, what competing advertisers are offering, and whether the post-click experience fulfils the promise made in the advertisement.

Paid search is commonly associated with sponsored results on search engine results pages (SERPs). In practice, SEM is a coordinated system involving demand analysis, keyword selection, account architecture, auction participation, creative development, landing-page design, measurement, and continuous experimentation. The system is particularly important in performance marketing because outcomes can often be connected to measurable actions such as leads, purchases, registrations, calls, or store visits.

Intent-based advertising provides the conceptual foundation for this chapter. Rather than treating all impressions as equivalent, it distinguishes audiences according to the purpose implied by their behaviour. Search advertising can respond to informational, navigational, commercial-investigation, transactional, local, or urgent service intent. The central managerial question is not simply, “Which keyword has the greatest volume?” It is, “Which demand should the organisation capture, at what cost, with what message, and through which experience?”

Learning Objectives

After studying this chapter, students should be able to:

1. Explain the role of search engine marketing within digital and performance marketing.

2. Interpret search queries and classify them according to user intent and customer journey stage.

3. Conduct keyword research using demand, relevance, competition, and business-value criteria.

4. Design a logical paid-search account structure and write intent-aligned advertisements.

5. Explain auction dynamics, quality signals, bidding strategies, and budget allocation.

6. Assess landing-page relevance, conversion readiness, and the relationship between ad and post-click experience.

7. Apply shopping and other search advertising formats appropriately.

8. Diagnose campaign performance using meaningful metrics and analytical comparisons.

9. Recognise ethical, regulatory, privacy, automation, and governance issues in SEM.

Search Advertising as an Intent-Recognition System

Search marketing begins with a query, but a query is only a visible expression of a broader decision process. Consider the difference between “what is term insurance,” “term insurance plans comparison,” and “buy term insurance online.” These phrases may concern the same product, yet they signal different degrees of knowledge, evaluation, and readiness. An effective advertiser does not place all three phrases in one undifferentiated campaign. Each may require a different message, landing page, bid level, and conversion expectation.

A useful classification of search intent includes the following:

• Informational intent: The user seeks knowledge, explanation, or guidance, as in “how does cloud accounting work?”
• Navigational intent: The user is attempting to reach a particular brand, website, or service, such as “Adobe Creative Cloud login.”
• Commercial-investigation intent: The user is comparing alternatives or assessing suitability, for example “best electric scooter for city commuting.”
• Transactional intent: The user demonstrates readiness to purchase, subscribe, book, download, or contact a provider, such as “order noise cancelling headphones.”
• Local or service intent: The query includes a location or implies immediate local fulfilment, such as “dentist near me” or “24-hour appliance repair.”
• Branded intent: The query contains a company, product, or proprietary brand name. Branded searches may have high conversion rates but require careful interpretation because some users would have converted without the advertisement.

Intent is not fixed by the wording alone. Geography, device, time, previous interactions, and the searcher’s stage in the buying process can alter its commercial meaning. “Laptop repair” on a mobile device at 8 p.m. may indicate an urgent local need, while the same phrase in an academic article may reflect research. Search platforms use contextual signals, but advertisers must also use analytics and judgement.

The value of a keyword is therefore multidimensional. Search volume indicates potential reach; relevance indicates fit with the organisation’s offer; competition reflects the difficulty and cost of obtaining visibility; and business value concerns the probability and economic significance of conversion. A low-volume phrase with high commercial intent may be more valuable than a high-volume generic phrase that produces curiosity but little action.

From Search Demand to Account Architecture

Keyword research as market interpretation

Keyword research is often presented as a list-building exercise. At postgraduate level, it is better understood as a form of demand research. It reveals the language customers use, the problems they recognise, the alternatives they consider, geographical differences, seasonal patterns, and gaps between organisational terminology and consumer terminology.

A disciplined process usually begins with business categories, products, customer problems, competitor references, locations, and desired actions. These seed concepts are expanded through keyword-planning tools, search-term reports, competitor observation, internal site search, customer-service records, and web analytics. The resulting list should be evaluated rather than accepted automatically.

Important evaluation dimensions include:

1. Semantic relevance: Does the phrase accurately represent the product or service?

2. Intent strength: Does it suggest research, comparison, or action?

3. Expected commercial value: What is the likely revenue or strategic value of a conversion?

4. Volume and reach: Is there sufficient demand to justify dedicated management?

5. Competition and cost: Are bids likely to be economically sustainable?

6. Language and geography: Does the phrase reflect the market’s actual vocabulary and location?

7. Landing-page availability: Can the advertiser provide a relevant and credible destination?

Keyword match settings are mechanisms for controlling how closely a user’s search must relate to a selected keyword. Exact, phrase, and broad approaches differ in reach and interpretive flexibility, though platform behaviour and terminology evolve over time. The practical principle remains constant: greater reach requires stronger monitoring, conversion data, and negative-keyword discipline.

Negative keywords are terms for which an advertisement should not be shown. They may exclude irrelevant meanings, unsuitable audiences, job seekers, free-resource seekers, unsupported locations, or products not offered by the advertiser. Negative-keyword management is not merely a cost-control activity. It protects brand relevance and prevents performance data from being distorted by inappropriate traffic.

Campaign and ad-group structure

Account structure should reflect meaningful differences in user intent, offer, geography, product, or measurement requirement. A common error is to create an excessively granular structure in which every minor variation receives its own campaign, fragmenting data and making optimisation difficult. The opposite error is to place unrelated products, intents, and landing pages in one ad group, preventing precise messaging.

A practical structure may separate campaigns by business objective, geography, product line, brand versus non-brand demand, or budget and bidding requirements. Within each campaign, ad groups should contain closely related themes that can share an advertisement and landing page. For example, a furniture retailer could separate “ergonomic office chairs” from “gaming chairs,” because the audiences, product features, and landing pages differ. A generic ad group called “chairs” might attract broad traffic but weaken message relevance.

Structure also creates managerial visibility. A campaign divided by region can reveal different costs and conversion rates, but regional division is useful only when the organisation can act on that difference. Structure should support decisions, not merely create administrative complexity.

The Search Auction and Quality Signals

Paid search generally operates through an auction that determines whether an advertisement is eligible, where it appears, and what the advertiser pays. The auction is not a simple ranking of the highest bid. Platforms consider the bid or bid-related value, the expected quality and usefulness of the advertisement, the relevance of the landing page, the expected impact of assets, and contextual factors such as device, location, query meaning, and competition.

Although exact platform formulas are proprietary and subject to change, the managerial implication is stable: an advertiser may improve visibility and efficiency by improving relevance rather than only increasing bids. Quality signals commonly relate to:

• expected click-through rate;
• relevance of the advertisement to the query;
• usefulness and experience of the landing page;
• historical account and keyword performance, interpreted cautiously;
• expected contribution of additional ad assets; and
• contextual fit with the searcher’s situation.

Quality should not be treated as a single number that explains every outcome. A strong quality signal does not guarantee a top position if the advertiser’s bid, budget, eligibility, or commercial offer is inadequate. Conversely, a high bid cannot repair a fundamentally irrelevant or misleading experience. Advertisers should therefore distinguish between auction eligibility, visibility, click efficiency, conversion efficiency, and profitability.

Bidding and Budget Decisions

Bidding is the process of assigning economic value to opportunities to obtain qualified traffic or conversions. Manual bidding can provide control where data are limited or where a manager needs to protect specific constraints. Automated bidding uses observed signals and historical data to adjust bids according to a chosen objective, such as maximising clicks, conversions, conversion value, or return on advertising spend (ROAS).

No bidding strategy is universally superior. A conversion-focused strategy may be appropriate when conversion tracking is reliable and sufficient signal is available. A traffic-oriented strategy may be useful for an awareness or data-collection phase, but it should not be mistaken for business success. Value-based bidding is potentially powerful for organisations whose conversions differ substantially in economic worth, provided that revenue or value data are accurate.

Budget allocation should follow marginal opportunity rather than historical habit. A campaign receiving more budget is not necessarily the best campaign; it may simply be spending more. Managers should examine incremental conversions, marginal cost per acquisition, profit contribution, impression share limitations, and the possibility that additional spending will reach lower-intent users. Budget decisions also require awareness of seasonality, inventory, service capacity, and operational constraints. A campaign that generates leads faster than the sales team can process them may create a performance problem outside the advertising account.

Conversion measurement is foundational. A conversion should represent a meaningful business action, and primary conversions should be distinguished from secondary actions such as page views, button clicks, or time on site. Enhanced or modeled measurement can help address incomplete observation, but it does not remove the need for consent, data governance, and careful interpretation.

Writing Ads and Building Relevance

Search advertisements operate under severe space and attention constraints. Their purpose is not to repeat every product feature, but to connect the searcher’s intent with a credible value proposition and a clear next step. Effective copy normally aligns four elements: the user’s problem or category, the offer’s distinctive benefit, evidence or reassurance, and an actionable invitation.

For example, a search for “online postgraduate data analytics course” may be addressed with a message about flexible study, recognised faculty, project-based learning, and an application deadline. A generic advertisement about “quality education” is less useful because it does not reflect the query’s specific decision criteria.

Ad assets extend the information available in the primary message. Sitelinks can direct users to relevant sections; callouts communicate concise benefits; structured snippets describe categories or features; location and call assets support local action; price and promotion assets may clarify commercial terms. Assets should be accurate, current, and strategically selected rather than added merely to occupy space.

Claims require substantiation. Expressions such as “lowest price,” “guaranteed results,” or “number one” can create legal and ethical exposure if they are ambiguous or unsupported. Advertising copy must also respect platform policies concerning prohibited products, discriminatory targeting, misleading destinations, trademarks, and sensitive personal circumstances.

Landing-Page Relevance and Conversion Design

The click is not the outcome of search advertising; it is the beginning of the post-click evaluation. A landing page should deliver continuity between query, advertisement, and experience. This continuity includes the product or service itself, the language used, the expected price or offer, the geographic availability, and the action requested.

A relevant landing page typically has a clear heading, concise explanation of value, visible evidence, an appropriate call to action, accessible design, and trustworthy information about price, privacy, delivery, cancellation, or eligibility. Mobile usability and page speed are especially important because many searches occur on mobile devices, but speed should be considered alongside comprehension and accessibility.

The landing page must also be evaluated according to the conversion’s nature. A high-consideration B2B service may require case evidence, technical specifications, security information, and a low-friction consultation request. An impulse retail purchase may require product imagery, availability, delivery information, reviews, and a short checkout path. Forcing every visitor into the same form is usually a sign of poor intent alignment.

Landing-page optimisation should not focus only on conversion rate. A misleading page may generate more immediate submissions but lower lead quality, increase cancellations, or damage brand trust. Quality assessment therefore includes conversion quality, downstream revenue, customer satisfaction, and compliance.

Shopping Formats and Product-Led Search

Shopping advertising is particularly relevant for retailers because it can present product imagery, price, merchant identity, availability, and other attributes before the click. Its performance depends heavily on the quality of the product feed. Product titles, descriptions, identifiers, categories, prices, images, availability, and promotional information must be accurate and synchronised with the website.

Retail advertisers should treat the feed as a strategic data asset rather than a technical upload. Titles should reflect the terms customers use while retaining essential attributes such as brand, model, size, colour, or material. Images should represent the product accurately. Discrepancies between feed data and the landing page can cause disapprovals, poor user experience, or loss of trust.

Shopping analysis extends beyond click-through rate. Important questions include whether products attract profitable orders, whether high-spend products produce adequate margin, whether returns differ by category, and whether advertising shifts demand among products that customers would have purchased anyway. Product-level profitability, stock availability, and fulfilment capacity must inform bidding and promotion decisions.

Measuring and Diagnosing Performance

SEM analysis requires movement from surface metrics to causal and economic interpretation. Common metrics include impressions, clicks, click-through rate, average cost per click, conversions, conversion rate, cost per acquisition, conversion value, ROAS, impression share, and lost impression share due to budget or rank. Each metric answers a different question.

A high click-through rate may indicate strong relevance, attractive copy, or an overly broad promise. It does not prove profitability. A low conversion rate may reflect poor landing-page experience, weak offer, low-intent traffic, technical failure, delayed purchase cycles, or inaccurate tracking. A high ROAS may be desirable, but excessive efficiency can also indicate underinvestment if profitable opportunities are being missed.

A useful diagnostic sequence is:

1. Confirm that tracking, attribution, and conversion definitions are functioning.

2. Examine search terms and identify irrelevant or unexpected demand.

3. Compare performance by intent, device, geography, audience, product, and landing page.

4. Assess whether advertisements accurately represent the offer.

5. Check auction, budget, eligibility, and feed issues.

6. Compare lead or order quality, not only recorded volume.

7. Test one meaningful change at a time where possible.

Attribution requires caution. A branded search conversion may be the final recorded interaction after exposure to several other channels. Last-click reporting is useful descriptively but may over-credit the final touchpoint. Incrementality experiments, geo-based tests, holdout designs, and controlled budget changes can provide stronger evidence about causal contribution, although they require statistical discipline and operational feasibility.

An Illustrative Case: MetroFit Fitness Studios

Background

MetroFit is a fictional chain of fitness studios operating in three metropolitan areas. It offers monthly memberships, personal training, and short-term introductory passes. The company has historically purchased broad digital reach but wants to increase qualified trial bookings while controlling acquisition cost.

Situation and problem

The initial paid-search campaign used one campaign for all services. Keywords included “gym,” “fitness,” “personal trainer,” and “gym membership.” The advertisements used general claims such as “Join MetroFit Today,” and all clicks were sent to the homepage. The campaign generated substantial traffic, but many users searched for home workouts, employment, free facilities, or locations where MetroFit did not operate. Sales staff also reported that a large proportion of enquiries were unsuitable or incomplete.

Analysis

The marketing team reviewed search terms, location data, device performance, form completion, and membership sales. It found three distinct intent groups: users seeking a nearby gym, users comparing personal-training options, and users looking for a low-commitment trial. These groups had different information requirements and different values to the business. The team also discovered that mobile users were more likely to call, while desktop users more often completed a form. The homepage did not clearly display trial pricing, studio locations, or class availability.

Strategy and solution

MetroFit reorganised the account into campaigns for local membership, personal training, and introductory passes. Each campaign used geographically appropriate targeting and separate ad groups for closely related themes. Negative keywords excluded jobs, free workouts, home exercise, and unsupported areas. Advertisements used location references, transparent introductory-offer language, sitelinks to timetables and studio pages, and call assets during staffed hours.

Each campaign received a dedicated landing page. The local membership page included nearby studio details, opening hours, membership conditions, and a map. The personal-training page described trainer expertise, session options, and a consultation form. The introductory-pass page explained price, validity, booking steps, and cancellation terms. Calls and forms were tracked separately, and sales-qualified trials were imported as a downstream outcome.

Bidding was initially conservative while the new structure accumulated reliable data. Later, the team tested conversion-oriented automated bidding against a controlled comparison. Budget was shifted toward locations with adequate studio capacity and stronger downstream membership rates rather than simply toward the campaigns with the most clicks.

Outcome

The campaign produced fewer irrelevant visits and a higher proportion of completed trial bookings. More importantly, the organisation could distinguish inexpensive but low-quality enquiries from leads that progressed to paid membership. The exact results would depend on market conditions and implementation, but the strategic improvement came from aligning intent, message, destination, measurement, and operational capacity.

Learning points

The case demonstrates that search optimisation is not achieved by keyword expansion alone. Account structure should reflect meaningful differences in customer purpose. Negative keywords protect efficiency, but landing-page relevance protects conversion quality. Finally, the business outcome depends on the connection between advertising data and the organisation’s sales and service systems.

Risks, Limitations, and Professional Governance

Search advertising has several limitations. Auction prices can rise quickly when competitors enter a category. Search volume may be insufficient for niche offerings. Automated systems can amplify poor inputs, and platform reporting may not reveal all causal effects. Conversion tracking can be weakened by consent restrictions, browser changes, technical errors, or fragmented customer journeys. Search demand also reflects existing awareness; paid search may capture demand without creating it.

Common professional mistakes include selecting keywords solely by volume, confusing clicks with outcomes, using one landing page for every query, ignoring search-term reports, over-segmenting the account, changing multiple variables without a test plan, and optimising to cheap leads rather than valuable customers. Another mistake is allowing automated bidding to operate without adequate conversion quality, budget controls, or business constraints.

Ethical practice requires transparency about price, terms, availability, sponsorship, and data use. Advertisers should avoid exploiting sensitive conditions, making discriminatory inferences, or targeting vulnerable users with manipulative urgency. Personalisation must respect consent and applicable privacy law. Organisations should maintain access controls, change logs, approval procedures, brand-safety rules, and clear responsibility for automated decisions.

Automation, AI, and Emerging Practice

Automation is now embedded in keyword expansion, responsive advertisement generation, audience signals, bidding, budget recommendations, feed management, and anomaly detection. AI can assist with clustering search terms, identifying intent patterns, generating copy variants, summarising performance, and forecasting demand. These applications are valuable when humans define objectives, validate inputs, inspect outputs, and evaluate business consequences.

Generative systems can produce fluent but inaccurate claims, duplicate messages, unsuitable tone, or policy violations. Human review is therefore essential, particularly for regulated sectors and sensitive categories. A useful governance model assigns ownership for prompt and asset approval, factual verification, privacy review, experimentation, and post-launch monitoring.

Search behaviour is also changing through conversational interfaces, voice queries, visual search, and AI-generated result environments. These developments may reduce the number of conventional clicks or alter how users compare providers. Advertisers will need stronger first-party content, structured product data, authoritative answers, distinctive value propositions, and measurement approaches that account for visibility beyond a traditional SERP.

Best-Practice Principles

A mature SEM programme should follow several principles:

• Begin with business objectives and customer intent, not platform settings.
• Build structure around actionable differences in offer, audience, geography, or measurement.
• Use search-term evidence to refine keywords, negatives, advertisements, and landing pages.
• Treat relevance as an integrated relationship among query, ad, destination, and conversion.
• Select bidding strategies only after establishing reliable conversion definitions and data quality.
• Evaluate lead and customer quality alongside media metrics.
• Allocate budget according to incremental value, profitability, capacity, and strategic priorities.
• Test changes systematically and document assumptions and results.
• Review automated recommendations critically rather than accepting them by default.
• Protect user autonomy, privacy, accuracy, and transparency throughout the campaign lifecycle.

Chapter Summary

Search engine marketing captures expressed demand by connecting search queries with paid messages and relevant post-click experiences. Its strategic value depends on understanding intent rather than treating all keywords as equivalent. Informational, navigational, commercial, transactional, local, and branded searches require different expectations and often different campaign treatments.

Effective SEM combines demand research, keyword and negative-keyword management, purposeful account structure, auction and quality-signal understanding, suitable bidding, relevant ad assets, and landing-page continuity. Shopping formats add the importance of accurate product feeds, pricing, imagery, availability, and product-level profitability. Performance analysis must move beyond clicks and conversion counts to include lead quality, downstream value, incrementality, budget constraints, and operational capacity.

Automation and AI can improve scale and responsiveness, but they require reliable data, human oversight, governance, and ethical safeguards. The strongest search programmes are not those that merely buy the most visibility; they are those that convert meaningful intent into valuable and trustworthy customer relationships.

Key Terms

Search Engine Marketing (SEM): Paid promotion of products, services, or organisations through search-engine advertising systems.

Search Engine Results Page (SERP): The page displayed by a search engine in response to a user query.

Search intent: The purpose or need expressed or implied by a search query.

Informational intent: A search purpose focused on learning, explanation, or problem understanding.

Transactional intent: A search purpose indicating readiness to purchase, book, subscribe, or complete another action.

Commercial investigation: Research involving comparison or evaluation of alternatives before a decision.

Keyword: A term or phrase used to help match an advertisement with relevant searches.

Search term: The actual phrase entered by the user into a search engine.

Negative keyword: A term used to prevent advertisements from showing for unsuitable searches.

Ad group: A set of closely related keywords, advertisements, and often a common landing-page theme.

Quality signals: Indicators used by an advertising platform to estimate relevance, usefulness, and expected user response.

Ad asset: An additional information element, such as a sitelink, callout, price, location, or call feature.

Landing page: The webpage reached after a user clicks an advertisement.

Conversion: A defined action that represents a desired business outcome.

Conversion rate: The proportion of measured interactions that result in a conversion.

Cost per acquisition (CPA): Advertising cost divided by the number of acquired conversions or customers, according to the chosen definition.

Return on advertising spend (ROAS): Conversion value generated relative to advertising expenditure.

Impression share: The proportion of eligible impressions obtained by an advertisement or campaign.

Shopping feed: A structured product data file containing attributes used to create and manage product advertisements.

Incrementality: The additional outcome caused by advertising compared with what would have occurred without it.

Review Questions

Short Answer Questions

1. What is meant by intent-based advertising?

2. Distinguish between a keyword and a search term.

3. Why are negative keywords important in paid search?

4. What is the purpose of ad assets?

5. Define conversion rate and explain what it measures.

Long Answer Questions

1. Explain the major categories of search intent and show how each can influence campaign design.

2. Discuss the principles of effective keyword research and account structure.

3. Explain how auction dynamics and quality signals affect paid-search performance.

4. Analyse the relationship between advertisement relevance and landing-page experience.

5. Discuss how shopping advertising depends on feed quality, product economics, and operational capacity.

Analytical / Application Questions

1. A campaign has a high click-through rate but a low rate of qualified leads. Identify possible causes and propose a diagnostic process.

2. Design an SEM structure for a university offering degree programmes, short courses, and executive education. Explain how intent, landing pages, and measurement would differ.

3. A retailer’s automated bidding system is increasing spend on products with high revenue but low profit margins. What data and governance changes should the marketing manager introduce?

22. Multiple Choice Questions with Answers

Question 1

A user searches “best project management software for a remote team.” Which intent is most evident?

A. Purely navigational intent

B. Commercial-investigation intent

C. Post-purchase service intent

D. Employment-related intent

Correct Answer: B. Commercial-investigation intent

Question 2

What is the strongest reason for separating closely related search themes into different ad groups?

A. To guarantee the lowest possible cost per click

B. To ensure every keyword receives identical bids

C. To improve message and landing-page relevance

D. To eliminate the need for conversion tracking

Correct Answer: C. To improve message and landing-page relevance

Question 3

Which action most directly reduces traffic from people searching for services an advertiser does not offer?

A. Adding negative keywords

B. Increasing the daily budget

C. Adding more broad product keywords

D. Removing all ad assets

Correct Answer: A. Adding negative keywords

Question 4

A high-quality advertisement receives few conversions after many clicks. Which investigation should be prioritised?

A. Whether the landing page fulfils the advertisement’s promise

B. Whether the campaign has enough unrelated keywords

C. Whether impression volume should replace conversion measurement

D. Whether all advertisements should use the same homepage

Correct Answer: A. Whether the landing page fulfils the advertisement’s promise

Question 5

Which statement best describes automated bidding?

A. It removes the need for accurate conversion data

B. It adjusts bids according to a selected objective and available signals

C. It always produces better results than manual bidding

D. It guarantees profitable outcomes regardless of the offer

Correct Answer: B. It adjusts bids according to a selected objective and available signals

Question 6

Why can a high click-through rate fail to demonstrate campaign success?

A. Click-through rate measures only impressions without clicks

B. It may reflect attractive messaging but irrelevant or low-value traffic

C. High click-through rate prevents advertisements from entering auctions

D. Click-through rate is unrelated to user behaviour

Correct Answer: B. It may reflect attractive messaging but irrelevant or low-value traffic

Question 7

For a shopping campaign, which problem is most likely to result from inconsistent price or availability information in the product feed?

A. Improved brand differentiation

B. Higher offline service capacity

C. User distrust or product disapproval

D. Lower need for landing-page relevance

Correct Answer: C. User distrust or product disapproval

Question 8

A branded-search campaign produces many conversions, but most customers already knew the brand. What analytical issue should the manager consider?

A. Whether last-click reporting over-credits the branded advertisement

B. Whether branded keywords should always be excluded

C. Whether conversion tracking should be removed

D. Whether generic keywords have no commercial value

Correct Answer: A. Whether last-click reporting over-credits the branded advertisement

Question 9

Which change best reflects an intent-aligned landing-page strategy for a local emergency repair service?

A. Sending all users to a general corporate homepage

B. Providing location, availability, contact options, and clear service terms

C. Removing phone and location information to reduce clutter

D. Using only educational content about the repair industry

Correct Answer: B. Providing location, availability, contact options, and clear service terms

Question 10

What is the most appropriate governance response when generative AI creates an unsupported advertising claim?

A. Publish it if the wording is persuasive

B. Increase the bid to compensate for uncertainty

C. Require factual review and approval before publication

D. Hide the claim in an ad asset

Correct Answer: C. Require factual review and approval before publication