LLM Optimisation
GEO in Practice: Turning AI Answers into Revenue
Ahrefs’ latest analysis finds that AI Overviews appear on ~16% of US searches, that position #1 loses ~34.5% of clicks when an Overview shows, and that assistants can answer directly—often without linking. In short: classic SEO still matters, but you also need to earn a place inside AI answers.
30 October 2025
8 min read
A summary of Ahref's GEO Analysis
What changed (and what to do about it)
Search results compress the decision. When AI answers sit on top, fewer users click to compare ten blue links.
Your job shifts from “rank to be clicked” to “be cited to be chosen.” Mentions, facts, and proofs about your brand must be easy for models to extract.
Action checklist
Be mention-worthy beyond your site. Secure credible third-party coverage (rankings, reviews, expert roundups).
Make facts machine-readable. Product attributes, proofs, policies, and FAQs in clean copy and schema/JSON-LD.
Align stories across surfaces. Keep specs and claims consistent across D2C, PR, and marketplace PDPs.
Optimise for “answer inclusion.” Write content that a model can lift verbatim to satisfy constraints and trade-offs.
How this maps to the real buying journey
Google frames the problem → Chat assistant clarifies the shortlist → Amazon/retailer converts.
Your category explainers and comparison pages seed authority. Your assistant-friendly answers (attributes, outcomes, proofs) win inclusion. Your PDPs close the loop with clear bullets, on-image specs, A+, and Q&A that mirror real prompts.
Content that tends to win inside answers
Best/top explainers with explicit criteria and trade-offs
Vs/comparisons that quantify differences, not just describe them
How-tos/FAQs written as direct answers, with sources and specs
Product/Service pages with crisp specs, proofs, and policies
Metrics to watch
AI share of voice / Share of Model: % of relevant AI answers that name or feature your brand
Assistant-referred traffic: smaller volume, but often higher intent once the shortlist is set
Sell-through lift: marketplace add-to-cart and ordered units after content/schema updates
LMO7’s playbook (how we execute GEO)
Signal Architecture: unify attributes, proofs, and FAQs so models can quote you.
Lexem.io clustering: map real buyer prompts by funnel stage, then script prompt paths to guide content and PDPs.
Model Surface Monitoring: track brand mentions across assistants and AI Overviews; fix gaps fast and re-publish.
Retail hand-off: keep D2C and Amazon perfectly in sync so the narrative holds from answer → cart.
Bottom line
You don’t abandon SEO, you extend it to win in-answer visibility. Earn credible mentions, structure your facts, and measure presence inside AI responses. Do that, and AI becomes a conversion engine rather than a traffic drain.
What changed (and what to do about it)
Search results compress the decision. When AI answers sit on top, fewer users click to compare ten blue links.
Your job shifts from “rank to be clicked” to “be cited to be chosen.” Mentions, facts, and proofs about your brand must be easy for models to extract.
Action checklist
Be mention-worthy beyond your site. Secure credible third-party coverage (rankings, reviews, expert roundups).
Make facts machine-readable. Product attributes, proofs, policies, and FAQs in clean copy and schema/JSON-LD.
Align stories across surfaces. Keep specs and claims consistent across D2C, PR, and marketplace PDPs.
Optimise for “answer inclusion.” Write content that a model can lift verbatim to satisfy constraints and trade-offs.
How this maps to the real buying journey
Google frames the problem → Chat assistant clarifies the shortlist → Amazon/retailer converts.
Your category explainers and comparison pages seed authority. Your assistant-friendly answers (attributes, outcomes, proofs) win inclusion. Your PDPs close the loop with clear bullets, on-image specs, A+, and Q&A that mirror real prompts.
Content that tends to win inside answers
Best/top explainers with explicit criteria and trade-offs
Vs/comparisons that quantify differences, not just describe them
How-tos/FAQs written as direct answers, with sources and specs
Product/Service pages with crisp specs, proofs, and policies
Metrics to watch
AI share of voice / Share of Model: % of relevant AI answers that name or feature your brand
Assistant-referred traffic: smaller volume, but often higher intent once the shortlist is set
Sell-through lift: marketplace add-to-cart and ordered units after content/schema updates
LMO7’s playbook (how we execute GEO)
Signal Architecture: unify attributes, proofs, and FAQs so models can quote you.
Lexem.io clustering: map real buyer prompts by funnel stage, then script prompt paths to guide content and PDPs.
Model Surface Monitoring: track brand mentions across assistants and AI Overviews; fix gaps fast and re-publish.
Retail hand-off: keep D2C and Amazon perfectly in sync so the narrative holds from answer → cart.
Bottom line
You don’t abandon SEO, you extend it to win in-answer visibility. Earn credible mentions, structure your facts, and measure presence inside AI responses. Do that, and AI becomes a conversion engine rather than a traffic drain.