Optimising AI Search: What Google, Microsoft, and Perplexity Really Say About AEO and GEO

The Silent Traffic Collapse: When No One Clicks Anymore  

Imagine: Your website ranks at position 1. Your content is first-class. And yet, your traffic is dropping – month after month, 40 to 60 percent fewer visits in your analytics dashboard. No technical glitch, no penalty, no algorithm update. The answer is being displayed directly in the search engine – before anyone clicks through to your website.

Google AI Overviews answer questions directly within the search results. ChatGPT and Perplexity deliver complete answers with source citations – instead of blue links. Microsoft Copilot summarises what users would have previously read on your site.

The figures highlight the scale of this shift: According to SparkToro (2024, Similarweb clickstream data), 58.5% of US search queries and 59.7% of EU search queries end without a single click to an external website. Out of 1,000 Google searches, only 360 clicks reach the open web. For queries triggering AI Overviews, Seer Interactive reports that the zero-click rate rises to 83%.

This doesn't just change traffic – it destroys the central metric of control: visitor numbers as a measure of success. Your content is cited, summarised, and consumed – but not a single pageview appears in Matomo or GA4. You are providing value, but you can no longer prove it.

The magnitude of this shift is made tangible by three key figures:

Amidst this transformation, two terms have become established: AEO (Answer Engine Optimisation) and GEO (Generative Engine Optimisation). The industry suggests that a completely new strategy is required. But what do the platform operators themselves say?

Table of Contents  

1. The Blind Spot: Why Your Analytics Data is Lying  

The drop in traffic is merely the symptom. The underlying issue: Your analytics dashboard no longer reflects reality. Your content continues to be consumed – just invisibly, within AI platforms.

Mike King (iPullRank, AI Search Marketer of the Year 2025) coined the term "Measurement Chasm" – a growing gap between reality and what your tools can capture. In traditional SEO, the feedback loop was clear: Keyword → Ranking → Click → Conversion. In AI search, this chain breaks. Your content is retrieved, synthesised, and integrated into an AI answer – without a single entry in Matomo or GA4.

What exactly has become invisible?  

What Do the Experts Say?  

Rand Fishkin (SparkToro) speaks of the end of click-based attribution. The previous principle: Click on a Google result → Website visit → Form submission → Customer. Every step is traceable – all attribution models in GA4, Matomo, or HubSpot are based on this.

Fishkin's argument: When AI systems cite and summarise your content, this chain breaks. No referrer, no pageview, no conversion path – your content provides value, but no analytics tool captures it.

His demand: "influence-based marketing measurement". No longer asking "Which click led to the purchase?", but rather: "How often is our brand mentioned in AI answers – and how does that influence subsequent searches and purchasing decisions?"

Mike King (iPullRank) developed a concrete framework for this: moving away from "Do we rank?" towards "Are we cited?" He recommends a three-stage measurement approach:

1. Input metrics – Is your content structured so that AI systems can understand and retrieve it?
2. Citation tracking – Is your content actually being cited in AI answers? In what position? In what context?
3. Business outcomes – What measurable business results (conversions, revenue) arise from AI referral traffic?

Eli Schwartz (Product-Led SEO) warns against being blinded by visibility metrics alone: "Stop celebrating LLM visibility scores as if they pay your bills." He demands that all metrics be consistently tied back to revenue – AI citations are only relevant if they provably bring in customers.

What Follows From This?  

The guiding question shifts: No longer "How many visits are we getting?", but instead "How often is our brand cited in AI answers?"

• New metrics: Citation Rate and Share of Voice instead of Click-Through Rate
• Active tracking: Regularly querying AI platforms yourself instead of relying on passive dashboards
• More indirect revenue connection: Brand influence in AI answers → subsequent direct search → conversion

2. AEO and GEO: Concepts, Distinction, Classification  

Answer Engine Optimisation (AEO) optimises content so that it is cited as a direct answer in AI-driven platforms – rather than merely appearing in traditional search result lists.

Generative Engine Optimisation (GEO) describes the overarching discipline: maximising visibility within AI-generated search results, meaning anywhere answers are synthesised from multiple sources.

Why Traditional SEO Still Matters: How AI Search Works Behind the Scenes  

The table shows: AEO/GEO pursues different goals than traditional SEO. But how does an AI decide who to cite? The answer is surprisingly simple – and explains why your existing SEO efforts are the fundamental prerequisite.

All major AI search platforms utilise Retrieval-Augmented Generation (RAG). The principle: The AI doesn't invent answers out of thin air. It first searches the traditional search index for the best sources – and then formulates an answer based on them. The consequence: If you rank poorly in the search index, the AI won't find you in the first place.

3. Straight from the Source: What the Platforms Say About AEO and GEO  

The industry is rife with new buzzwords. The platform operators themselves, however, speak with a surprisingly unified voice. The following compilation is based on Glenn Gabe's analysis from 3 March 2026 – and the core message is the same across the board.

Google: "It is SEO."  

Google's leading figures have positioned themselves repeatedly and unambiguously in 2025/2026:

Microsoft: SEO Fundamentals plus "snippable" Content  

Perplexity: Brand Building as the Key  

Platform Conclusion: Good SEO IS good AEO/GEO  

The message is clear: AEO/GEO does not replace traditional SEO – it is a subset of it. A targeted extension of proven practices, enriched with AI-specific optimisations. Those who practice solid SEO already have the best foundation.

4. What Still Changes: 6 AEO/GEO Optimisations That Make the Difference  

Good SEO is the foundation – but six areas of action differentiate between merely "being found" and actually "being cited":

Content Structure: The Inverted Pyramid Principle  

AI systems prefer to extract the first 1–2 sentences of a section. Therefore, structure your content following the inverted pyramid principle:

1. Direct answer (first 1–2 sentences) – this is what the AI extracts
2. Core facts & context (bullet points, data, quotes) – supporting evidence
3. Detailed explanation (background, methodology, case studies) – comprehensive coverage
4. Related topics (links to further content) – topic authority signals

Schema Markup: Numbers That Convince  

E-E-A-T: Trust is Mandatory  

AI systems prioritise verifiably trustworthy sources. Implement:

• Author profiles featuring credentials, experience, and social media links
• Source references citing authoritative studies and official documentation
• Visible update timestamps on every page
• HTTPS, privacy policy, legal notice (Impressum), contact information
• Original research: Your own data, case studies, expert quotes

robots.txt: Explicitly Allowing AI Crawlers  

Without access, AI platforms cannot index your content. You should be familiar with these bots:

5. TYPO3 Implementation: AEO/GEO in Practice  

The preceding chapters clarified the what. Now comes the how – with concrete code examples for TYPO3 v13 and v14 (v14 preferred) that you can directly apply to your project.

Required Extensions  

The following extensions are needed for AEO/GEO implementation in TYPO3:

robots.txt via Site Configuration  

Configure the robots.txt within the TYPO3 site configuration to grant access to AI crawlers:

# config/sites/main/config.yaml
routes:
  - route: robots.txt
    type: staticText
    content: |
      # Search engines
      User-agent: Googlebot
      Allow: /

      User-agent: Bingbot
      Allow: /

      # AI Search Platforms (Real-time citation)
      User-agent: GPTBot
      Allow: /

      User-agent: ChatGPT-User
      Allow: /

      User-agent: PerplexityBot
      Allow: /

      User-agent: ClaudeBot
      Allow: /

      User-agent: anthropic-ai
      Allow: /

      User-agent: Google-Extended
      Allow: /

      User-agent: CCBot
      Allow: /

      # Standard
      User-agent: *
      Allow: /
      Disallow: /typo3/
      Disallow: /typo3conf/
      Disallow: /typo3temp/

      Sitemap: https://example.com/sitemap.xml

Schema.org with EXT:schema – FAQPage via Fluid  

{namespace schema=Brotkrueml\Schema\ViewHelpers}

<schema:type.fAQPage>
    <f:for each="{faqItems}" as="faq">
        <schema:type.question -as="mainEntity" name="{faq.question}">
            <schema:type.answer -as="acceptedAnswer" text="{faq.answer}" />
        </schema:type.question>
    </f:for>
</schema:type.fAQPage>

Article Schema with Author via Fluid  

{namespace schema=Brotkrueml\Schema\ViewHelpers}

<schema:type.article
    -id="https://example.com/article/{article.uid}"
    headline="{article.title}"
    description="{article.teaser}"
    datePublished="{article.crdate -> f:format.date(format: 'c')}"
    dateModified="{article.tstamp -> f:format.date(format: 'c')}"
>
    <schema:type.person -as="author"
        name="{article.author.name}"
        url="{article.author.profileUrl}"
    >
        <schema:property -as="sameAs" value="{article.author.linkedIn}" />
    </schema:type.person>

    <schema:type.organization -as="publisher"
        name="{settings.siteName}"
        url="{settings.siteUrl}"
    >
        <schema:type.imageObject -as="logo" url="{settings.logoUrl}" />
    </schema:type.organization>
</schema:type.article>

Organization Schema via PSR-14 Event  

<?php

declare(strict_types=1);

namespace Vendor\SitePackage\EventListener;

use Brotkrueml\Schema\Event\RenderAdditionalTypesEvent;
use Brotkrueml\Schema\Type\TypeFactory;
use TYPO3\CMS\Core\Attribute\AsEventListener;

#[AsEventListener(identifier: 'site-package/add-organization-schema')]
final readonly class AddOrganizationSchema
{
    public function __construct(
        private TypeFactory $typeFactory,
    ) {}

    public function __invoke(RenderAdditionalTypesEvent $event): void
    {
        $organization = $this->typeFactory->create('Organization')
            ->setProperty('name', 'Your Company')
            ->setProperty('url', 'https://example.com')
            ->setProperty('logo', 'https://example.com/logo.png')
            ->setProperty('description', 'Brief description for AI understanding')
            ->setProperty('sameAs', [
                'https://www.linkedin.com/company/yourcompany',
                'https://github.com/yourcompany',
            ]);

        $event->addType($organization);
    }
}

Content Freshness with SYS_LASTCHANGED  

# Visible "Last updated" date
lib.lastModified = TEXT
lib.lastModified {
    data = page:SYS_LASTCHANGED
    strftime = %d.%m.%Y
    wrap = <time datetime="|" itemprop="dateModified">Last updated: |</time>
}

FAQ Content Block with Automatic Schema  

# ContentBlocks/ContentElements/faq-accordion/config.yaml
name: vendor/faq-accordion
typeName: faq_accordion
title: FAQ Accordion
description: FAQ with structured data for AI search
group: common

fields:
  - identifier: faq_items
    type: Collection
    labelField: question
    fields:
      - identifier: question
        type: Text
        required: true
      - identifier: answer
        type: Textarea
        enableRichtext: true
        required: true

<!-- Frontend.html -->
{namespace schema=Brotkrueml\Schema\ViewHelpers}

<section class="faq-accordion">
    <schema:type.fAQPage>
        <f:for each="{data.faq_items}" as="item">
            <schema:type.question -as="mainEntity" name="{item.question}">
                <schema:type.answer -as="acceptedAnswer">
                    <schema:property -as="text"
                        value="{item.answer -> f:format.stripTags()}" />
                </schema:type.answer>
            </schema:type.question>

            <details class="faq-item">
                <summary>{item.question}</summary>
                <div class="faq-answer">
                    <f:format.html>{item.answer}</f:format.html>
                </div>
            </details>
        </f:for>
    </schema:type.fAQPage>
</section>

llms.txt: Two Methods  

ddev composer require web-vision/ai-llms-txt

# config/sites/main/config.yaml
imports:
  - resource: 'EXT:ai_llms_txt/Configuration/Routes/RouterEnhancer.yaml'

6. How Do I Know My AEO/GEO is Working?  

In Chapter 1, we illustrated why traditional analytics fail. Here is the antidote: a concrete set of KPIs you can deploy right now – ranging from a free spreadsheet to enterprise tools.

KPIs in Three Stages  

Citation Rate & Share of Voice: How to Measure Them Concretely  

No tool, no budget necessary. A Google Sheet and 60 minutes a month are enough to get started.

What exactly is the Citation Rate?

The Citation Rate measures how often your brand appears as a source in AI answers – relative to the number of queries tested. The core question: If someone asks a question crucial to my business – do I get cited?

What exactly is Share of Voice?

Share of Voice (also "Share of Model") measures your portion of all citations compared to your competitors – not whether you are cited, but how large your share is. According to KnewSearch, market leaders achieve 31% Share of Voice, while the top 3 in a category collectively hold 67%.

Step-by-Step: Manual Measurement with a Spreadsheet

You need a Google Sheet (or Excel) and 60–90 minutes per month. Mike King (iPullRank) offers a free template with a Looker Studio dashboard – or you can set up your own sheet.

Step 1 – Create a Query List. Compile 25 questions that your target audience would ask an AI – natural, conversational questions, not SEO keywords. Distribute them across 5 categories, 5 queries each:

Step 2 – Test Systematically. Enter each of the 25 queries into 5 AI platforms – this produces 125 data points per month. Always use Incognito mode, ensuring you are logged out of all accounts.

Step 3 – Record the Results. For each Query × Platform combination, note in your sheet:

Step 4 – Calculate KPIs. From the raw data, use basic spreadsheet formulas to calculate:

Citation Rate       = COUNTIF(Cited,"Yes") / COUNTA(Cited) * 100
Share of Voice      = Own_Citations / (Own + Competitor) * 100
Query Coverage      = Queries_with_Citation / 25 * 100

Transfer the results monthly into a trend sheet. Clear patterns will emerge after 3 months. Important: According to Otterly.AI (1 million+ data points), only 30% of brands maintain their visibility from one AI answer to the next – regular measurement is therefore vital.

Practical Example: TYPO3 Agency Measures Its AI Visibility

A concrete example: The fictional TYPO3 agency "AlpineWeb" from Salzburg wants to know if they appear in AI answers when potential clients ask about TYPO3 services.

Query List (Excerpt – 5 of 25):

Results After Testing on 5 Platforms (Excerpt):

KPI Calculation for AlpineWeb (Month 1):

With 25 Queries × 5 Platforms = 125 Data Points, AlpineWeb observes the following results:

What AlpineWeb Deduces from This:

• Immediate Action: Expand expertise content – the accessibility articles are cited well, so create more of them (e.g. TYPO3 security, TYPO3 performance)
• Weakness: Seldom found for brand queries ("TYPO3 agencies Austria") → supplement Organization schema with areaServed, set up llms.txt
• Tracking: ChatGPT almost never cites them → verify if content is allowed for GPTBot via robots.txt

Benchmark Data by Industry  

Monitoring Tools Compared  

Tools such as Semrush, Brand24, Otterly.ai, Gauge, and SE Ranking support AI visibility measurement.

Tracking AI Referral Traffic (Matomo & GA4)  

7. TYPO3 AEO/GEO Checklist  

All measures at a glance – ordered by impact, linking back to the respective sections.

Extensions & Configuration  

Schema Implementation  

Content & E-E-A-T  

Monitoring & Measurement  

8. Traffic Magnets: Why Micro-Tools Are the Best AEO/GEO Strategy  

All previously mentioned optimisations make you visible in AI answers. But they don't solve the core problem: The click through to your website still doesn't happen. The AI delivers the answer – why should anyone bother clicking your link?

The answer: Because your website offers something that no AI can replicate. An interactive tool, a calculator, an analysis, a quiz – something you have to do, not just read.

At webconsulting.at, we deploy this strategy systematically: Wherever it makes thematic sense, we embed specialised tools into our most important expert articles – no download, no registration, directly usable within the article. Not every article needs a tool, but where an interactive element offers genuine added value, the results are evident: higher dwell times, more backlinks, and more AI citations compared to pure text content.

Examples From Our Practice  

Deepfake Analysis Tool – Examine images and videos across 4 forensic levels (metadata, C2PA, signal, semantics). No registration, no installation required.

Screenshot

Directory of Public Administration Websites – 5,600+ Austrian public administration websites searchable by category, federal state, and domain.

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Content Optimisation Checker – Optimise texts according to three principles: Concise, Scannable, Objective. Original and optimised version in direct comparison.

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AI Content Estimator – Submit your own estimate on the share of AI content per industry and instantly compare it with study data.

Screenshot

AI Compendium with Downloads – 100 Q&As on AI featuring chapter downloads (PowerPoint, PDF, ZIP), videos, quizzes, and flashcards.

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Conclusion: SEO Remains the Foundation – AEO/GEO Sharpens the Focus  

The message from the platform operators is unmistakable: Those who practice solid SEO possess the best foundation for AI search visibility. AEO and GEO are not a revolution – they are a targeted extension involving schema markup, E-E-A-T signals, content freshness, and granting technical access to AI crawlers.

The critical difference lies in the objective: Not just being found, but being cited. And not just being cited, but winning back traffic – through interactive content that AI answers cannot possibly replace.