FROM SERP TO “SPY-RP”: HOW PERFORMANCE MARKETERS CAN USE AD INTELLIGENCE TO WIN IN AI SEARCH ECOSYSTEMS

AI-powered search is changing how consumers discover brands, compare products, and decide which businesses deserve their attention. For anstrex.com/blog/what-billboard-campaigns-from-ally-primark-and-purito-can-teach-performance-marketers-about-native-ad-creative" target="_blank" rel="noreferrer noopener">performance marketers, this shift creates a new opportunity to combine competitive intelligence, conversions" target="_blank" rel="noreferrer noopener">conversion optimization, and Answer Engine Optimization (AEO) to reach potential customers earlier in the buying journey.

Rather than focusing exclusively on traditional search rankings and paid advertising placements, marketers can now examine how brands appear in AI-generated answers, which sources influence those answers, and what information helps customers move toward a purchase.

This approach builds on familiar performance marketing principles. By studying competitors, identifying visibility gaps, and creating content that addresses real customer needs, businesses can strengthen their presence across traditional and AI-powered discovery channels.

The opportunity is not simply to appear in more AI responses. It is to become a credible, relevant option when potential customers are researching a problem, comparing alternatives, or preparing to make a purchase.

Performance marketing has always adapted to changes in how consumers discover products and services. Search engines, social media platforms, video advertising, and shopping marketplaces have each introduced new opportunities to reach audiences and measure results.

AI-powered search represents another change in that process.

Tools such as ChatGPT, Perplexity, Gemini, Microsoft Copilot, and Google's AI Overviews can synthesize information from multiple sources into conversational responses. Instead of browsing several websites independently, users may receive summaries, comparisons, and recommendations before deciding which sources to explore.

This changes how brands compete for attention.

Traditional search optimization often emphasizes rankings, impressions, and clicks. AI search introduces additional questions: Is your brand mentioned in relevant answers? Are your pages cited as supporting sources? Does the information available about your business accurately communicate its strengths and limitations?

Research into AI search and brand credibility increasingly emphasizes the role of trust and authority in discovery. See MarTech's analysis of trust in AI search.

For performance marketers, the practical lesson is to expand competitive research beyond conventional search engine results pages (SERPs).

AI Search Creates New Opportunities for Competitive Analysis

Consider a buyer researching software for a growing business. Instead of immediately searching for individual vendors, the buyer might ask an AI assistant to recommend solutions, compare features, explain pricing models, and identify the best option for a specific use case.

A brand that appears in those early recommendations may influence the buyer's shortlist before a website visit occurs.

That creates an opportunity for marketers to investigate:

• Which brands appear in relevant AI-generated answers.
• Which websites and third-party sources support those recommendations.
• What differentiators and product attributes are associated with competing brands.
• Which customer questions remain unanswered by existing content.
• Whether AI-referred visitors eventually engage with pricing pages, product demonstrations, or other conversion points.

AI visibility is not a conventional advertising auction. Marketers cannot assume that paying more, publishing more content, or acquiring more mentions will automatically produce a higher position in an AI answer.

However, the competitive research process is familiar: identify where competitors are visible, understand the factors that may contribute to that visibility, and develop a strategy to address the gaps.

Why Commercial Intent Still Matters

AI-assisted research does not eliminate the need for effective conversion paths.

A buyer might use an AI assistant to understand a category, compare vendors, and identify potential solutions. The next step may be a branded search, a pricing-page visit, a product demonstration, or a direct conversation with a sales team.

The relationship between AI discovery and traditional paid search varies by industry, query type, and user behavior. Analysis from Real FiG Advertising & Marketing on ChatGPT and PPC provides additional context on how AI search may affect different types of search demand.

Rather than assuming that informational searches are disappearing or that every remaining click has stronger purchase intent, marketers should measure these changes within their own accounts.

Track AI referral traffic, branded search demand, lead quality, conversion rates, and revenue alongside conventional search metrics.

The objective is to understand where AI influences the buying journey and how that influence translates into measurable business outcomes.

Competitive intelligence platforms help marketers understand how other businesses attract attention, communicate value, and direct visitors toward conversion.

Tools such as Google Ads Auction Insights, Similarweb, Adbeat, Meta Ads Library, and TikTok Creative Center provide different views of the competitive landscape. Their capabilities vary, but the underlying research questions remain consistent:

• Which competitors are visible in the market?
• What messaging, formats, and offers do they use?
• Which audiences and customer needs appear to receive the most attention?
• Where are there opportunities to differentiate or improve performance?

AI search analysis applies a similar investigative approach to a different discovery environment.

Instead of studying only advertisements and landing pages, marketers examine AI-generated answers, brand mentions, citations, and the sources associated with recommendations.

The distinction is important: advertising intelligence reveals publicly observable promotional activity, while AI visibility tools help monitor how brands appear in selected AI responses. Neither automatically reveals the complete decision-making process behind a platform's results.

Measure AI Visibility as a Separate Performance Indicator

In paid search, impression share helps advertisers understand how frequently their ads appear relative to eligible opportunities.

AI search does not have a directly equivalent universal metric. Nevertheless, marketers can create a consistent measurement framework using a defined set of prompts.

For example, a software company might test 100 relevant prompts across selected AI platforms and record how often its brand appears.

If the brand is mentioned in 20 responses, its observed mention rate is 20% for that particular sample.

This should not be interpreted as universal market share or a guaranteed measure of all AI search visibility. Results can vary according to the prompts tested, platform, location, model version, personalization, and date.

A useful tracking framework includes:

• Brand mention rate: How often the brand appears in sampled responses.
• Citation rate: How frequently the brand's website is linked or cited.
• Competitive visibility: How often competing brands appear under the same testing conditions.
• Context and sentiment: Whether the brand is described accurately and favorably.
• Referral performance: Whether identifiable AI-referred visitors engage or convert.
• Coverage gaps: Which important questions produce answers that overlook the brand.

An overview of the differences between conventional SEO and Answer Engine Optimization is available in Semrush's guide to AEO vs. SEO.

Study the Content Patterns Associated With Visibility

Advertising research often reveals recurring messaging patterns. Several competitors might emphasize the same benefits, risk-reversal offers, customer testimonials, or product demonstrations.

AI search research can identify recurring patterns in the sources associated with brand recommendations.

For each important prompt, investigate:

• Which pages are cited by the answer engine.
• Whether the sources are first-party websites or independent publications.
• What specific facts or product attributes the answer highlights.
• Whether comparison pages, tutorials, case studies, or expert resources appear.
• What information is missing or inconsistent across available sources.

Do not assume that a frequently cited page is necessarily the reason a brand appears in an answer. Treat these observations as hypotheses to investigate, not proof of a particular ranking mechanism.

Use the findings to identify useful content opportunities and develop stronger resources supported by clear evidence.

Translating ad intelligence into AEO involves a change in what you analyze. Instead of examining only which creative attracts attention, investigate which information helps people and AI systems understand a brand's relevance to a specific question.

For additional context on AEO content trends, see Search Engine Journal's AEO and content marketing trends recap.

Mine Conversational Questions to Uncover Customer Intent

Traditional keyword research helps marketers identify what people search for. AI search research adds another opportunity: understanding how people express complex needs in natural language.

A customer may ask:

• Which CRM is best for a small sales team?
• What should I consider before switching platforms?
• Which solution integrates with my existing software?
• How do two competing products compare in total cost?
• What limitations should I understand before purchasing?

These questions represent different stages of the buying journey.

Informational questions require clear explanations. Evaluation questions benefit from balanced comparisons. Transactional questions need specific product information, transparent pricing where available, and an easy path to take action.

Marketers can discover these questions through customer support conversations, sales call notes, website search data, customer interviews, and carefully designed prompt research. Customer data should be handled with appropriate consent, access controls, and privacy safeguards.

The resulting intent map can guide both SEO and AEO content planning.

For further context, see HubSpot's guide to AI search behavior.

A competitor's marketing funnel can begin influencing a customer before the customer reaches the competitor's website.

AI-generated explanations and comparisons may help users identify a problem, evaluate potential solutions, and narrow their choices. Website visits, product demonstrations, and purchases can happen later in the process.

This means marketers should study both the visible conversion funnel and the information that shapes discovery before the click.

Step 1: Identify the Questions That Introduce Competitors

Start with a commercially important category, such as enterprise email deliverability software, customer relationship management systems, or local HVAC marketing services.

Develop a set of prompts that reflects how a real buyer might research that category.

Examples include:

• What is the best solution for a specific business type?
• Which providers support a particular integration?
• What are the main differences between two competing products?
• Which vendors serve businesses in a particular region?
• What are the disadvantages of each available option?
• Which solution offers the best value for a defined budget?

Run these prompts across the AI platforms that matter to your audience. Record the date, platform, wording, response, brand mentions, and cited URLs.

Where practical, repeat the tests to identify patterns rather than drawing conclusions from a single response.

Tools such as Profound and Bluefish can support AI visibility research. HubSpot's comparison of these platforms explains how marketers can approach monitoring and optimization across AI-generated responses: Profound vs. Bluefish.

The result is a map of the questions associated with competitor visibility.

Step 2: Investigate the Sources Supporting Competitor Recommendations

When a competitor appears in an AI answer, examine the supporting sources.

Look for:

• Independent reviews and comparison articles.
• Industry publications and expert commentary.
• Partner directories and integration documentation.
• Product documentation and detailed feature pages.
• Original research, case studies, and customer evidence.
• Consistent descriptions of the brand across reputable websites.

These sources may help explain why a competitor is easy to understand, verify, or recommend in a particular context.

However, an AI answer citing a source does not establish that the source caused the brand to be recommended. The relationship should be investigated carefully.

Use these findings to identify legitimate opportunities for stronger documentation, more complete product information, independent validation, and useful content.

Avoid copying competitor messaging or attempting to manufacture misleading reviews and endorsements.

Step 3: Connect AI Discovery to High-Intent Website Visits

The next stage is to understand what happens when a user moves from an AI answer to a website.

Review available referral data in analytics tools, landing-page engagement, branded search trends, form submissions, sales conversations, and conversion records.

Where the available tracking permits, examine whether AI-referred visitors interact with:

• Product and service pages.
• Pricing and comparison pages.
• Integration documentation.
• Case studies and customer testimonials.
• Demo requests, contact forms, or checkout pages.

Do not assume that AI-referred traffic always converts better than other channels. Conversion performance will depend on the audience, offer, platform, and measurement method.

Instead, compare conversion rates and lead quality using your own data. Account for small sample sizes, unattributed visits, and differences between first-touch and assisted conversions.

For context on AI referral behavior, consult HubSpot's AI search behavior analysis.

Step 4: Reconstruct the Full Customer Journey

Combine AI visibility observations with conventional SEO, advertising, and conversion data.

Your analysis should answer four questions:

1. What questions cause potential customers to investigate the category?
2. Which brands and sources appear during the research process?
3. What information helps buyers compare and evaluate available options?
4. Which pages and experiences help qualified prospects take the next step?

This creates a more complete picture of the journey, connecting early discovery with measurable outcomes.

Use those findings to build targeted content, improve landing pages, refine paid search campaigns, and address unanswered customer questions.

The objective is not to imitate a competitor's entire funnel. It is to identify what customers need at each stage and create a more useful, credible experience.

Content that performs well in AI-assisted discovery should also serve the people who read it.

A useful page answers a specific question, explains relevant trade-offs, provides supporting evidence, and helps readers decide what to do next. It should not exist solely to attract an AI citation.

Build Comprehensive Resources Around Customer Needs

A single keyword rarely represents every question a potential buyer has about a product or service.

Someone researching a marketing platform may need to understand its features, integrations, pricing, implementation requirements, limitations, and alternatives.

A comprehensive resource can address these related needs in one organized location.

Consider including:

• Problem definition: Explain the challenge and the audience affected.
• Evaluation criteria: Identify the factors buyers should consider.
• Use cases: Describe how requirements differ across industries or business sizes.
• Comparisons: Explain meaningful differences between alternatives.
• Implementation guidance: Provide practical steps and realistic expectations.
• Next steps: Link readers to relevant documentation, product pages, or consultations.

Use competitor research to identify missing information, but develop the content from your own expertise, evidence, and customer insights.

The objective is to make the page genuinely more useful than a collection of generic keyword-focused paragraphs.

For additional discussion of content formats and AEO trends, see Search Engine Journal's AEO and content marketing trends recap.

Design Landing Pages That Explain the Offer Clearly

Conversion optimization traditionally emphasizes helping visitors complete a desired action. AI-assisted discovery adds another reason to make product information explicit and easy to verify.

A well-structured landing page should explain:

• Who the product or service is designed for.
• Which problems it solves.
• What differentiates it from alternatives.
• What it costs, where pricing is available.
• Which integrations, requirements, or restrictions apply.
• What evidence supports its claims.
• How customers can get started or request more information.

Use descriptive headings and concise explanations so readers can find answers quickly.

For example, a software landing page might include sections titled "Who Is This Platform Best For?", "Which Integrations Are Supported?", and "What Are the Main Limitations?"

These headings are useful because they reflect real customer questions. They also help search engines interpret the page's organization, although no particular heading structure guarantees an AI citation.

Make Trust and Evidence Visible

Claims such as "industry-leading," "best-in-class," and "trusted by thousands" are difficult to evaluate without supporting evidence.

Replace vague assertions with verifiable information wherever possible.

Examples include:

• Published product specifications.
• Named customer case studies with permission.
• Clearly explained performance benchmarks.
• Independent reviews and relevant certifications.
• Links to original research and authoritative references.
• Transparent descriptions of limitations and eligibility requirements.

Only use testimonials, statistics, certifications, and customer results that can be substantiated.

When making a comparative claim, explain the basis of the comparison and acknowledge relevant limitations.

Research into AI search visibility has highlighted the broader role of credibility and authoritative information. See MarTech's discussion of trust and AI search.

Turn Commercial Landing Pages Into Useful Decision Resources

High-intent landing pages do not need to focus exclusively on a purchase button.

Potential customers may still need answers about implementation, support, compatibility, contract terms, and total cost before converting.

Address those questions alongside the primary offer.

For example, a product page could include a feature summary, a comparison table, supported integrations, implementation steps, frequently asked questions, and a clear call to action.

This reduces unnecessary friction for human visitors and gives search systems well-organized information to interpret.

Do not overload every landing page with unrelated content. Keep each page focused on a coherent audience and intent, then connect related topics through relevant internal links.

Use Structures That Make Information Easy to Understand

Clear content organization benefits both readers and search engines.

Practical techniques include:

• Use descriptive H2 and H3 headings.
• Answer the main question near the beginning of each section.
• Explain technical terms when they first appear.
• Use tables when comparing attributes or specifications.
• Present processes as numbered steps.
• Add FAQs that address genuine customer concerns.
• Use appropriate structured data when it accurately describes the page.
• Keep product information, pricing, and business details consistent across relevant pages.

Structured data can help search engines interpret certain types of information, but it does not guarantee inclusion in AI-generated answers.

Likewise, FAQ sections should provide real value rather than repeat keywords unnecessarily.

For an overview of how AEO and traditional SEO complement each other, refer to Semrush's AEO vs. SEO guide.

AI search visibility depends on more than publishing a single well-optimized page. A brand needs accurate information, credible supporting evidence, and a website that clearly communicates what it offers.

Trust is a useful strategic objective, but it should not be treated as a publicly measurable AI ranking score or a direct equivalent of Google's advertising Quality Score.

Instead, marketers should focus on the elements they can verify and improve.

Strengthen Credibility Across the Wider Web

Customers often evaluate businesses through multiple sources, including their own websites, independent publications, review platforms, industry directories, and professional communities.

Consistent, verifiable information across these sources can help people understand what a business does and assess its credibility.

Useful activities include:

• Publishing original research and practical industry insights.
• Maintaining accurate business profiles and product listings.
• Earning relevant editorial coverage and legitimate backlinks.
• Developing genuine partnerships and expert contributions.
• Encouraging honest customer reviews without manipulating ratings.
• Correcting outdated or inaccurate information about the business.

Prioritize relevance and quality over the sheer number of mentions.

A large volume of low-quality links or repetitive brand mentions is not a substitute for trustworthy evidence.

Improve the Reliability of Your Website

Your own website should provide a clear and consistent account of your business, products, and expertise.

Important areas to review include:

Technical accessibility: Ensure important pages can be crawled and indexed where intended, and resolve broken links or significant rendering issues.

Content accuracy: Keep specifications, pricing, integrations, and service details current.

Structured information: Use appropriate schema markup and ensure it matches the visible content.

Entity clarity: Make the business name, products, authorship, and relationships between relevant pages easy to understand.

Content depth: Provide the details customers need to evaluate the offer, including constraints, alternatives, and practical considerations.

Consistency: Avoid contradictory claims across landing pages, help documentation, product listings, and other official materials.

Technical SEO does not guarantee that an AI system will use a page, but it helps establish a more accessible and coherent information environment.

For further background, see MarTech's analysis of AI search visibility and trust.

Treat Customer Experience as a Business Outcome

The usefulness of AI search optimization should ultimately be evaluated against customer and business outcomes.

A brand mention may increase awareness without producing an immediate website visit. A referral visit may lead to a purchase days later. A customer may also discover a business through AI and subsequently return through a branded search or direct visit.

These paths can make attribution difficult.

To improve measurement, combine several indicators rather than relying on a single metric:

• AI brand mention and citation rates.
• Qualified referral sessions from identifiable AI platforms.
• Engagement with relevant landing pages.
• Conversion rates and qualified leads.
• Revenue and customer acquisition cost where attribution is available.
• Customer satisfaction, retention, and refund trends.
• Accuracy and consistency of brand descriptions across sampled answers.

Use consistent testing methods and compare results over time. Separate observed changes from assumptions about what caused them.

Tools such as Profound, Bluefish, and Peec can help teams monitor selected aspects of AI visibility. Their coverage and features vary, so evaluate each platform according to your required models, reporting capabilities, and measurement needs. See HubSpot's comparison of AI visibility tools.

Turn Visibility Insights Into an Optimization Cycle

AI search analysis becomes more useful when it informs a repeatable process.

Start by selecting a defined group of customer prompts and measuring your current visibility. Investigate the sources and content associated with competitor mentions. Identify the most important gaps, then update your website, supporting resources, or external credibility signals.

After publishing changes, repeat the same tests and compare the results.

A practical cycle looks like this:

1. Measure: Establish a baseline for brand mentions, citations, and referral activity.
2. Investigate: Identify which questions and sources are associated with competitor visibility.
3. Prioritize: Select content and technical improvements based on customer value and business relevance.
4. Implement: Publish original resources, improve existing pages, and strengthen verifiable evidence.
5. Evaluate: Repeat the tests and assess changes in visibility, engagement, and conversions.
6. Refine: Use the findings to determine which improvements deserve further investment.

This approach connects AI search optimization with the same evidence-driven discipline used in performance marketing.

AI-powered search is creating new ways for consumers to discover brands and evaluate their options. For performance marketers, the opportunity is to understand these emerging discovery patterns without abandoning the fundamentals that already drive effective campaigns.

Competitive intelligence can reveal which brands appear in important AI-generated answers, what sources support those answers, and where useful information is missing. SEO and AEO can then help marketers build clearer, more comprehensive resources that address those gaps.

The process begins with careful research. Define the customer questions that matter, monitor visibility across selected AI platforms, investigate the sources associated with recommendations, and connect those findings to website engagement and commercial outcomes.

From there, invest in content that explains the offer clearly, supports claims with evidence, answers meaningful customer questions, and provides a useful path toward conversion.

There is no guaranteed shortcut to AI search visibility, and no single tool reveals every factor behind a recommendation. Results depend on the platform, query, available sources, and many other variables.

The lasting advantage comes from combining competitive analysis with genuine expertise, credible information, sound technical foundations, and disciplined measurement.

Rather than treating AI search as a replacement for traditional SEO or performance marketing, treat it as an additional discovery channel to understand and optimize. The brands best positioned to benefit will be those that make it easier for both customers and search systems to understand what they offer, why it matters, and when it is the right choice.