Mode checkout integration, plus agentic features, users won’t even need to leave AI platforms to buy By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO
understand shifts & AI optimization needs Study Data Scope AI Traffic vs AI Citations Semrush 40 sites · Travel, Finance, Real Estate, Retail Where AI Search Sends Traffic Similarweb ~87.6M AI clicks · 10 markets · Ecom, Finance, Travel Semrush 15 brands · 150 top source slots · AI Mode, Gemini, ChatGPT What It Takes for SaaS Brands to Win Semrush 15 SaaS brands · CRM, Collaboration, Accounting Local vs Global AI Citations Similarweb UK, Spain, Brazil vs US · 10 topics AI Search Is a 3rd-Party Citation Problem By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO
what decision makers see and will tend to prioritize SHARE OF AI TRAFFIC BY PAGE ROLE Brand entry (homepages) · 57.7% Action, task & operational · 19.9% Other deep / non-standard URLs · 13.4% Your homepage, logins & checkouts get most of the clicks 78% of measured AI visits land there. Now Discovery & evaluation · 8.9% aleydasolis.com/en/ai-search/ai-traffic-citations-research/ letʼs look at what AI actually cites…
news/reviews sites are key to maximize AI citation presence, although their relative importance change by vertical and platform https://www.aleydasolis.com/en/ai-search/ai-search-citations/
traffic & conversions and start measuring AI presence, readiness and business Impact with proxy metrics BRAND RECALL DIRECT CLICK CHECKOUT & AGENTIC COMMERCE AI SEARCH TRAFFIC DECISION IN AI PLATFORM PURCHASE IN AI PLATFORM INDIRECT & MULTI-TOUCH WEBSITE VISIT AI SEARCH REVENUE By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO OFF PLATFORM INFLUENCE
Business Impact Metric Layer Why it matters KPI Roles 1. Presence Are you actually appearing, and how? Replaces traffic-only thinking with visibility and representation measurement Visibility KPIs Optimization and monitoring 2. Readiness Are you structurally prepared be surfaced? Explains why visibility is weak, strong, or unstable; the diagnostic layer Diagnostic KPIs Diagnosis and prioritization Connects AI search activity to commercial outcomes without overclaiming attribution Outcome KPIs Executive reporting and decision-making 3. Business Impact Are visibility and readiness translating into value? to https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-e
to use 3 metrics layers Observed Proxy (own & third party) Modeled Question: How many users clicked and converted from an AI answer? Question: 1. Own: Is there evidence users are seeing us in AI answers even when they do not click? 2. Third Party: How does our AI presence compare to competitors and which prompts are driving AI traffic? Question: If we assume X% of branded search lift is AI-attributable, what is the implied pipeline? Metrics from platforms passing a referrer or a UTM. Highest confidence, lowest coverage. Directional signals that correlate with AI influence but do not prove it. Medium to medium-low confidence, broader coverage. Estimates produced by applying assumptions to observed and proxy data. Lowest confidence, used for planning, never for proof. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
with the highest confidence although lowest coverage AI sessions by platform, landing page, device, and product line. Top AI landing pages: category, PDP, guide, and comparison pages. Engagement rate and average engagement time vs. organic / shopping benchmarks. AI-assisted add-to-carts, checkouts, purchases, and revenue. AI conversion rate, AOV, and revenue per visit by platform, where volume allows. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
in some of the ceiling. To be valuable, there should be a pattern consistent with an AI driven story. How to do it Branded search trend GSC query report filtered to brand terms, or the native branded/non-branded toggle. Track week-over-week and month-over-month. Direct and unattributed traffic trend GA4 Direct channel, especially to pages that are not shared in email or paid campaigns. Demand for frequently-surfaced pages Impressions and direct/organic traffic to the pages you have verified are being cited in AI answers. Survey-based discovery One question added to signup, demo, or post-purchase flows Bing Webmaster Tools AI Performance First-party citation counts, cited URLs, and grounding queries for Microsoft Copilot and Bing AI summaries. The only first-party citation data available from any AI ecosystem today. Social listening on brand mentions in AI-adjacent contexts Reddit, LinkedIn, Slack communities where “has anyone used X?” conversations happen. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
users attributed to Direct or Branded Organic is the strongest proxy that AI influence exists beyond what analytics shows https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
monthly 1. Prompt samples driving traffic to your top AI landing pages. Use it to: • Extend your Presence prompt set • Diagnose landing-page mismatch • Inform Readiness work 2. Competitive benchmarking of AI traffic share, top landing pages, and top prompts per page. 3. AI platform mix over time, benchmarked. Which AI platforms refer traffic to your site and to competitors. Use it to assess: • AI traffic share trend vs competitors over time • Which pages competitors are getting AI traffic to and which are distinctively yours. • Prompt overlap per page vs competitors Use it to assess: • Platform specific decay • Platform specific wins • Catch category shifts early https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
you can’t measure directly (Incremental branded clicks, visits, leads, or pipeline above baseline) x (stated AI influence assumption %) = modelled influenced value https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
in practice https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
readiness efforts impact and ROI AI-referred sessions Shows whether known AI traffic is growing or shrinking. Remember this is the floor, not the ceiling. Branded search / direct / surfaced page demand AI conversion rate / revenue per visit Shows whether known AI traffic is growing or shrinking. Remember this is the floor, not the ceiling. Survey AI discovery rate AI-assisted conversions Shows whether AI contributes to conversion paths even when it is not the final click. Third-party AI traffic share vs. peers Detects recall and downstream demand effects beyond measurable referrals. Surfaces AI influence on users who arrive via branded or direct, otherwise invisible. Shows whether observed growth is share-taking or category-riding. Flat share during category growth = loss. Third-party prompt samples per top landing page Third-party AI platform mix vs. peer average Modeled influenced pipeline / revenue Fills the gap analytics cannot: what question triggered the traffic. Drives prompt set updates and page fixes. Surfaces platform specific risks and opportunities early. Over indexing on one platform is a fragility signal. Useful for planning, never for proof. Overclaiming here erodes trust in the whole dashboard. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
each AI biz KPI for ecommerce, SaaS & Finance sites https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
“Women’s straight-leg jeans under $100” 1. Presence: Identify the gap 2. Readiness: Diagnose and act 3. Business impact: Measure the outcome The product line rarely wins relevant shopping recommendations. For prompts such as “Best women’s straight-leg jeans under $100,” it appears in 40% of answers, receives a store link in 25% of appearances, and wins 10% of relevant comparisons. The audit finds missing fit details, outdated prices and limited independent reviews of this line. Add rise, inseam, stretch and sizing guidance; synchronize prices and availability across product pages and feeds; pursue independent reviews testing these jeans’ fit and quality. Following improvements: linked citation rate rises 25% → 50% and comparative win rate 10% → 30%. What this means: The jeans are occasionally included, but shoppers are more often directed toward competing products. What to prioritize: Help AI systems and shoppers verify which jeans match their fit, budget and purchase requirements. How to interpret it: More recommendations and links coincide with higher tracked sales for this line. Purchases of this line in tracked AI referred sessions rise 12 to 28; at an $80 average selling price, attributed product revenue rises $960 to $2,240. Searches for “[brand] women’s straight-leg jeans” rising 12% provide a supporting proxy; they don’t prove AI caused the growth. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
Metrics for the AI Search Era” guide going through it with examples https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
assessment, without relying on traffic Metric How to select it What to learn / action Low readiness + low visibility Structural conditions are holding the brand back. Prioritize access, extractability, entity clarity, corroboration. High readiness + low visibility Brand is underdistributed or underrepresented in the source ecosystem. Focus on source presence, distribution, trust ecosystem, competitive disadvantage. Visibility improving + impact flat Brand is appearing but not memorably, persuasively, or on the right pages. Improve recommendation quality, linked citations, memorability, landing-page fit. Strong informational + weak commercial visibility Visible early in the journey but not winning shortlist or selection moments. Improve commercial prompt coverage and transaction-ready surfaces. High visibility + strong recommendation + weak representation accuracy Being talked about but described wrong. Entity and source correction: Wikipedia/Wikidata, schema consistency, review sites, supplier pages, analyst briefings. One segment strong, another weak Issue is segment-specific, not brand-wide. Run a segment-specific readiness and source-ecosystem review. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
as a visibility gate AI systems use brand consistency and external validation to reduce recommendation risk By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO 1. Secure third-party mentions and reviews on authoritative sites 2. Ensure consistent brand information across the web 3. Maintain visible customer support, returns, and company info 4. Avoid contradictory claims across channels
& digital PR strategies Link Building Backlinks from related, authoritative sites reviewing relevant businesses Digital PR Positive coverage, citations, backlinks from Media Sites Community Management Positive mentions from relevant social platforms and communities Branding Mentions, promotion, visibility alignment with brand positioning and voice By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO
accuracy with context Representation Accuracy How to calculate it Is the brand, product and key factual information described correctly? 39 of 46 appearances = 85% (95% CI 72-92%) Formula: accurate appearances ÷ brand appearances × 100 Review category, target user, use case, pricing and integrations. Report the binary accuracy rate alongside error categories and severity; a pricing error and a tone of voice issue shouldn't be interpreted as equivalent. https://www.aleydasolis.com/en/ai-search/a-3-layer-framework-to-measure-ai-presence-readiness-and-business-impact-redefining-metrics-for-the-ai-search-er
biz value, etc. To Avoid To Prioritize Independent first-hand reviews and tests Specialist editorial coverage and original reporting Complete marketplace, retailer and reseller listings Generic brand mentions with no clear evidence role Promotional guest posts or sponsored articles repeating brand claims Evidence-led comparisons and recommendation shortlists Expert commentary, interviews, podcasts and event coverage Partner, integration and ecosystem documentation Reach first coverage on irrelevant generalist sites Generic social posts and short-form clips without demonstration Practitioner demonstrations and task led tutorials Data-led articles and reports citing first-party research Third-party case studies and customer stories Syndicated press releases and copied announcements Low-quality directory and profile creation Authentic community Q&A and troubleshooting discussions Detailed customer reviews and maintained review profiles Industry directories, credentials and local or vertical profiles Thin best of listicles and paid inclusions without methodology Manufactured reviews, astroturfed discussions and undisclosed endorsements https://www.aleydasolis.com/en/ai-search/third-party-citation-optimization-ai-search/
that stand beyond AI To Avoid To Prioritize Independent first-hand reviews and tests Specialist editorial coverage and original reporting Complete marketplace, retailer and reseller listings Generic brand mentions with no clear evidence role Promotional guest posts or sponsored articles repeating brand claims Evidence-led comparisons and recommendation shortlists Expert commentary, interviews, podcasts and event coverage Partner, integration and ecosystem documentation Reach first coverage on irrelevant generalist sites Generic social posts and short-form clips without demonstration Practitioner demonstrations and task led tutorials Data-led articles and reports citing first-party research Third-party case studies and customer stories Syndicated press releases and copied announcements Low-quality directory and profile creation Authentic community Q&A and troubleshooting discussions Detailed customer reviews and maintained review profiles Industry directories, credentials and local or vertical profiles Thin best of listicles and paid inclusions without methodology Manufactured reviews, astroturfed discussions and undisclosed endorsements https://www.aleydasolis.com/en/ai-search/third-party-citation-optimization-ai-search/
value and an artifact to support assessment https://www.aleydasolis.com/en/ai-search/third-party-citation-optimization-ai-search/ and https://claude.ai/artifact/9dYnjWBoyHtq1D4ePMAKuA
keyword targeting The best cited AI brands provide full topic graphs to safely recommend them under diverse constraints. By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO 1. For each product line, define a topical map: Core entity (e.g. “women’s road running shoes”), subtypes (daily trainer, long-run, speed, recovery), alternatives, etc. 2. Cover all decision dimensions , not just popular queries (Use cases, constraints, trade offs, risks, edge cases, etc.) 3. Create topic-level summaries above product-level content. 4. Assess & monitor topic completeness gaps : % of subtypes with content, % of products with full attribute sets, % of decision scenarios covered, etc.
authority, and content across the full customer journey By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO * Products How-To Guides & Tutorials * Thought Leadership & Expert Advice * FAQs * New product releases Decision * Comprehensive Product Description * Documentation & Manuals * Pricing & Plans Pages * Case Studies * Compliance content Consideration * Reviews & Testimonials * Studies & Research * Comparison & Alternatives * Product Q&As * Community & Forum Content Post-Purchase Support * Knowledge base articles * Troubleshooting Guides & FAQs * Community & support forum * Video tutorials
now directly influence which product gets selected: AI systems pull reasoning from this content when recommending solutions By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO 1. Create expert-authored Buying guides , “Which should I choose” comparisons, Use-case explainers, Tradeoff content 3. Maintain consistency between guides and PDP attributes 2. Link products explicitly to scenarios and constraints
resilience, potential, biz value, effort... To Avoid To Prioritize Brand and entity pages Original market, audience and usage research Evidence led comparisons and selection guides Standalone commodity definitions without brand or journey context High volume tangential topics without a credible business journey Transaction and task-completion pages Live first-party databases and reference hubs Personalized tools using live or proprietary data Rehashed explainers and how to guides without original value Biased or mass produced ‘best’, comparison and alternatives pages Official product documentation, specifications and policies Documented customer outcomes, experiments and case studies Curated community and practitioner knowledge Fragmented FAQ and keyword variant pages serving one intent Generic calculators, quizzes and generators without distinctive inputs First-hand product tests and performance reviews Product specific implementation and troubleshooting guides Original reporting and source analysis Third-party news and press release rewrites without original input Programmatic pages built from public or competitor data https://www.aleydasolis.com/en/ai-search/content-prioritization-ai-search/ and https://claude.ai/artifact/Umbrgma5WRSfFZjsvE8BKp
for customers To Avoid To Prioritize Brand and entity pages Original market, audience and usage research Evidence led comparisons and selection guides Standalone commodity definitions without brand or journey context High volume tangential topics without a credible business journey Transaction and task-completion pages Live first-party databases and reference hubs Personalized tools using live or proprietary data Rehashed explainers and how to guides without original value Biased or mass produced ‘best’, comparison and alternatives pages Official product documentation, specifications and policies Documented customer outcomes, experiments and case studies Curated community and practitioner knowledge Fragmented FAQ and keyword variant pages serving one intent Generic calculators, quizzes and generators without distinctive inputs First-hand product tests and performance reviews Product specific implementation and troubleshooting guides Original reporting and source analysis Third-party news and press release rewrites without original input Programmatic pages built from public or competitor data https://www.aleydasolis.com/en/ai-search/content-prioritization-ai-search/ and https://claude.ai/artifact/Umbrgma5WRSfFZjsvE8BKp
with examples & artifact https://www.aleydasolis.com/en/ai-search/content-prioritization-ai-search/ and https://claude.ai/artifact/Umbrgma5WRSfFZjsvE8BKp
based on impact & effort 1. How much are AI platforms already contributing to revenue and marketing goals, counting what analytics can't see? 2. Which AI platforms does your audience use, for which tasks, and at what stage of the journey, compared with traditional search? 5. Which sources drive citations for your topics (your own site, publications, aggregators, community sites), and how much of that can you influence? 6. How do the needed actions overlap with existing SEO, Digital PR and community work, and what's genuinely additional? By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO 3. On a representative prompt library, what's your visibility, sentiment and citation share against competitors? Where are the biggest gaps? 7. Score each action by impact × confidence ÷ effort, including time to impact, and prioritize accordingly. 4. How ready are you to be found, read and cited? That covers content coverage, technical accessibility, structured data and feeds and, for ecommerce, agentic checkout.
your own context Crawlability Different bots/user agents for which you might have specific crawlability rules Indexability Relevance Lack of CSR JS support by AI bots Differences in User Search Behavior / Intents to further expand your site topical authority targeting the full journey By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO Popularity Metrics/Goals Bigger Role of third-party Citations / Mentions From Performance (traffic, conversions) only to Brand (visibility, sentiment, share of voice) and Performance metrics and goals
& Founder at Orainti ❏ Co-founder at Finchling ❏ Creator of the SEOFOMO & AI Marketers Newsletters ❏ Maker of LearningAIsearch.com & LearningSEO.io Speaker & Author ❏ Author of SEO, Las Claves Esenciales ❏ Spoke at +300 events in +40 countries By Aleyda Solis · SEO & AI Search Consultant · Orainti & SEOFOMO
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