Understanding Google Algorithms

A deep dive into how Google algorithms work.

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July 22, 2026

6

min to read

Last update: October 7, 2026

Author

Temara Inigo

Senior Account Manager

Reviewed by

Leighton Emmons

Co-founder

Introduction

Generative engine optimization (GEO) is the practice of increasing how often, and how favorably, AI engines such as ChatGPT, Google AI Overviews, Gemini, Perplexity, and Claude mention, cite, and recommend your brand in their answers. Where SEO earns positions on a results page, GEO earns presence inside the answer itself.

Key takeaways

GEO makes your brand visible inside AI-generated answers, not just on ranked results pages.
The audience is already there. OpenAI reported 900 million weekly ChatGPT users in February 2026, and Google says AI Overviews reach 2.5 billion people a month.
Clicks shrink where AI answers appear. Pew Research found users click a website on only 8% of searches that show an AI summary, versus 15% without one.
Third-party coverage wins citations. Muck Rack found 84% of AI citations point to earned media rather than brand-owned pages.
A working GEO strategy pairs answer-first owned content with citation building on the sources AI engines trust, tracked at the prompt level.

The behavior shift behind those numbers is structural. Buyers now ask an engine a full question, read one synthesized answer, and often never open a results page. If your brand is not named or cited in that answer, you are invisible at the exact moment a buyer forms a shortlist. This guide covers how generative engine optimization works, how it differs from SEO, and what a serious program includes.

How GEO Differs From SEO

GEO and SEO share the same foundations. Both need crawlable pages, genuine expertise, and content that answers real questions. They differ in where the competition happens and what winning looks like. The table below compares the two disciplines side by side.

Dimension

SEO

GEO

Where you compete

A ranked results page

A single synthesized answer

Unit of success

Positions and clicks

Mentions and citations

Strongest signal

Links, relevance, technical health

Third-party citations and consensus across trusted sources

Query behavior

Short keywords

Long conversational prompts with follow-ups

How you measure

Rankings, organic traffic, conversions

Share of voice, citation counts, AI referrals

The disciplines are complementary, and we treat them as equals rather than ranking one above the other. Strong SEO gives AI engines high-quality pages to retrieve. Strong GEO makes sure those pages, and third-party coverage of your brand, actually surface when engines assemble an answer. Running GEO vs SEO as a budget fight misses the point. The programs feed each other.

How AI Engines Decide Which Brands To Cite

AI engines choose citations through live retrieval, not through a stored ranking. When a user asks a question, the engine fans the prompt out into several background searches, retrieves candidate pages, and writes one answer with a small set of cited sources. Getting into that small set is the whole game.

Three patterns show up consistently in what gets cited.

Extractable answers win. Pages that state a direct answer in the first two lines under a clear heading get quoted. Pages that bury the answer get skipped, even when they rank.
Consensus beats volume. Engines favor brands that multiple independent, credible sources describe the same way. Muck Rack's 2026 research found 84% of AI citations point to earned media, which makes third-party coverage the strongest single lever.
Freshness matters. Engines retrieve live pages and weight recent, dated, attributed content over stale material.

Being the citation also matters more than the click math suggests. Pew Research found only 1% of users click a link inside an AI summary. The mention is the impression. Buyers carry the brand name from the answer into their next prompt or their vendor shortlist, which is why AI search optimization is a brand visibility discipline first and a traffic channel second.

What A Working GEO Strategy Includes

A working GEO strategy runs on three tracks at once. Owned content that engines can quote, earned coverage on the sources engines already trust, and measurement built around prompts instead of keywords.

Owned media that answers first

Structure every page so the direct answer sits in the first two lines under each heading, backed by a short takeaways block, an FAQ section, named authors with real credentials, and a visible last-updated date. Add Person, Organization, and FAQ schema so engines can parse who is speaking and why they are credible. This is the standard we apply through [internal link: GEO Content Optimization service page].

Earned media where engines already look

Map the publications, review sites, and listicles that engines cite for your category, then earn placements there. Since 84% of citations go to earned media, this is where most programs win or lose. Our [internal link: Citation Building service page] work targets exactly these domains.

Measure prompts, not keywords

Track a fixed set of buyer prompts weekly across engines, measure your share of voice against named competitors, and tie AI referral sessions to pipeline in analytics. Rankings tell you nothing here. Prompt-level tracking is the reporting unit for generative engine optimization, the same way keywords are for SEO.

[Case in Point placeholder: insert a Mamba GEO client outcome with the exact metric, e.g. share-of-voice lift across tracked prompts, once confirmed with the account team]

[Pull quote card: insert client or team quote on GEO results, with name, title, and photo, per blog template]

Looking Ahead

The engine landscape is fragmenting fast. ChatGPT still leads on usage, but Gemini, Perplexity, and Claude each hold meaningful and growing shares of AI referrals, and Google reports more than 1 billion monthly users on AI Mode. Brands that build generative engine optimization into their core search program now will compound visibility across every engine as the category grows. Brands that wait will watch competitors get named in the answers their buyers read.

Want to see where you stand today? [internal link: Contact / Free Audit page] Request a free GEO audit and we will show you where your brand appears across ChatGPT, Gemini, Perplexity, and AI Overviews, and where competitors are cited instead.

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[FAQ]

Frequently Asked Questions

What is generative engine optimization (GEO)?

Generative engine optimization is the practice of improving how often AI engines mention and cite your brand in generated answers. It covers answer-first content structure, third-party citation building, and prompt-level measurement across engines like ChatGPT, Gemini, Perplexity, and Google AI Overviews.

Is GEO replacing SEO?

No. SEO still governs the largest discovery channel, and the two share foundations like crawlability and content quality. GEO extends that work into AI answers, and strong programs run both as equal disciplines.

Which AI engines should a GEO strategy cover?

Start with ChatGPT, Google AI Overviews and AI Mode, Gemini, Perplexity, and Claude. Then weight effort by where your buyers actually ask questions, which differs by market, industry, and region.

How is GEO measured?

Track share of voice against named competitors across a fixed set of buyer prompts, count citations of your domain in AI answers, and monitor AI referral traffic and conversions in analytics. Measurement runs at the prompt level, not the keyword level.

[FAQ]

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Step 1:

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FERMÀT had just closed its Series B and was scaling fast—new hires, a new office, and new go-to-market motions. But their internal systems weren’t keeping up. Chili Piper managed scheduling but couldn’t reliably segment leads. HubSpot and Salesforce fields didn’t align, and enrichment data came from a messy mix of sources. The tech stack lacked integration, the data was incomplete, and routing logic was inconsistent.

Sarah Madden, Head of RevOps, knew this wasn’t sustainable. Inbound leads were FERMÀT’s most valuable pipeline source, but the qualification and routing process was slow, error-prone, and rigid.

_{“ Thanks for the opportunity to share our thoughts on how. If we implement this, we could see big improvements."
Joy Abiaad.
CMO of Dultmeier Sales}

_‍

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Step 2:

‍

FERMÀT had just closed its Series B and was scaling fast—new hires, a new office, and new go-to-market motions. But their internal systems weren’t keeping up. Chili Piper managed scheduling but couldn’t reliably segment leads. HubSpot and Salesforce fields didn’t align, and enrichment data came from a messy mix of sources. The tech stack lacked integration, the data was incomplete, and routing logic was inconsistent.

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Sarah Madden, Head of RevOps, knew this wasn’t sustainable. Inbound leads were FERMÀT’s most valuable pipeline source, but the qualification and routing process was slow, error-prone, and rigid.

‍

Looking ahead

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FERMÀT had just closed its Series B and was scaling fast—new hires, a new office, and new go-to-market motions. But their internal systems weren’t keeping up. Chili Piper managed scheduling but couldn’t reliably segment leads. HubSpot and Salesforce fields didn’t align, and enrichment data came from a messy mix of sources. The tech stack lacked integration, the data was incomplete, and routing logic was inconsistent.

‍

^{“ Thanks for the opportunity to share our thoughts on how. If we implement this, we could see big improvements."
Joy Abiaad.
CMO of Dultmeier Sales}

‍

[FAQ]