Remember when SEO was all about ranking on the first page of Google? For years, marketers obsessed over blue links, click-through rates, and domain authority. Those metrics made sense in a world where every query produced a list of results and your success depended on where you appeared in that list.
Fast forward to 2025. That world has changed. Generative AI search tools like ChatGPT, Google Gemini, Perplexity, and Microsoft Copilot now answer billions of questions each month. Googleâs AI Overviews appear in 84% of search results. Users donât always click through to websites anymore. They ask a question and get an answer in an AI chat interface.
Personally, I've been in this "internet" age since AOL dial-up and once again things evolve. Now, the metrics that are considered traditional, well they donât capture the reality of AI search. We need new ways to measure visibility and influence.
In this article, weâll explore why old SEO metrics no longer tell the full story, introduce emerging KPIs designed for AI search, and propose a framework for tracking your brandâs presence across generative engines. Weâll also show how Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) fit into this new realityâand how SaaS companies can stay competitive.
Why Traditional SEO Metrics Are No Longer Enough
How AI Changes the Nature of Search
Classic SEO focused on search engine results pages (SERPs) rankings, organic sessions, CTR, backlinks, domain authority. Success meant being in the top 3 spots and getting clicks.
But AI search doesnât operate the same way:
- Zero-click answers: AI chatbots now answer directly, reducing the need for users to click on any site.
- Invisible influence: Your content might influence AI answers without generating trafficâor even being visibly cited.
- Citation over ranking: AI engines pull from a mix of sources and credit them in different ways. The goal shifts from âranking firstâ to âbeing cited.â
These changes require a new measurement mindset.
Are Traditional Metrics Useless Now?
Not at all, but they tell only part of the story. Traffic, bounce rate, time on site, backlinks. They all still matter. But as AI-generated answers become the primary way users interact with information, new metrics must be added to the mix.
Emerging AI-Native Metrics
Reddit discussions, SEO leaders, marketing researchers and we at HookLead, have proposed new performance categories.
- AI Citation Rate
- What it is: How often your brand or content is mentioned in AI-generated responses across platforms like ChatGPT, Gemini, Claude, and Copilot.
- Why it matters: Being cited = being seen. AI treats citations like rankings.
- How to track: Use AI monitoring tools, scrape outputs across platforms, and track branded queries.
- How to optimize: Publish accurate, well-structured content. Use schema, cite authoritative sources, and update content frequently.
- AI Brand Share of Voice
- What it is: Your relative presence in AI-generated answers for your industryâs most important queries.
- Why it matters: If AI assistants skip your brand when naming top tools or companies, you're invisible.
- How to track: Analyze AI outputs for keywords like âbest [SaaS tool] for [use case]â or âtop CRM alternatives.â
- How to optimize: Own topical authority clusters. Use consistent brand language and showcase differentiators.
- AI Search Funnel Attribution
- What it is: The role AI engines play in moving users through your marketing funnelâfrom awareness to consideration to conversion.
- How to measure: Combine GA4 with surveys, session replays, and tools like FullStory to identify AI-referred visits and attribution gaps.
- Why it matters: You may be winning attention via AI but losing attribution clarity.
- Content Retrieval & Performance Across AI Models
- What it is: Which specific pieces of your content are being cited, quoted, or recommendedâand by which AI platforms.
- Why it matters: Not all platforms pull from the same sources. What performs well in ChatGPT may not in Gemini.
- Optimization tip: Track semantic density (richness of topic relationships), citation frequency, and which document types are favored.
Extended KPIs Worth Adding
Borrowing from recent industry discussions (like Duane Forresterâs KPI list), here are AI-specific KPIs you may want to track:
- Chunk Retrieval Frequency: How often specific paragraphs or sections appear in AI responses.
- Vector Index Presence Rate: What % of your content is embedded in AI-searchable vector databases.
- Embedding Relevance Score: How closely your content matches query vectors.
- Retrieval Confidence Score: How confident AI models are when choosing your content to show.
- Zero-click Surface Presence: Your brandâs exposure in AI answers that donât link out.
AEO vs. GEO: Where Your SaaS Needs to Show Up
What is AEO?
Answer Engine Optimization is optimizing your content to appear in AI-powered direct answersâthink featured snippets, People Also Ask, and AI Overview responses. AEO is heavily driven by structure, schema, and clear formatting.
What is GEO?
Generative Engine Optimization focuses on becoming a preferred source in generative engines like ChatGPT, Gemini, Claude, and Copilot. GEO emphasizes citation authority, semantic density, and training-data discoverability.
Why You Need Both
AEO gets you visible in classic Google answers. GEO ensures your brand survives in an AI-first search future.
Your content should:
- Include FAQ sections
- Use clear H2s/H3s
- Cite trusted external sources
- Structure ideas with bullets, step-by-step instructions, and comparisons
- Include internal evidence (case studies, original data)
If your SaaS blog content is structured this way, you increase your chance of citation and retrieval.
Targeting Natural Questions: AEO & GEO Strategy for SaaS
To align with both Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), SaaS companies should start structuring their content around natural language questions â the kind users ask in Google, ChatGPT, Gemini, and Perplexity.
These questions serve two purposes:
- They boost your chance of being cited in AI search results by matching how AI parses and retrieves information.
- They improve your SEO structure by aligning with âPeople Also Ask,â voice search, and featured snippets.
Below is a list of general, evergreen questions that most SaaS companies can target in their blog posts, product pages, or help center content â regardless of industry.
General Natural Questions Any SaaS Can Target
- How does this type of SaaS product solve [pain point]?
- What are the benefits of using [type of SaaS tool]?
- How does [product name] compare to other SaaS tools?
- What features should I look for in a [vertical-specific SaaS]?
- Whatâs the ROI of using a SaaS tool for [function]?
- Is [your SaaS product] secure or compliant with [industry regulation]?
- How do SaaS free trials typically convert into paid customers?
- What integrations are essential for [type of SaaS]?
- Why is [problem] a bottleneck for [target persona]?
- What is the best way to measure success using [your type of software]?
These types of questions build semantic density into your content and help AI models âunderstandâ the context and intent behind your content â improving the chances of citation and answer inclusion.
Industry-Specific Examples
Of course, the best questions will depend on your industry, ICPs, and product use case. Here are 3 examples of how that might look for different SaaS verticals:
HR & Recruiting SaaS
- How can HR software reduce time-to-hire?
- Whatâs the difference between ATS and CRM in recruiting?
- How do I improve candidate experience with automation?
Fintech SaaS
- Is [product] compliant with SOC 2 / GDPR?
- Whatâs the best way to track recurring revenue?
- How can fintech platforms automate invoice reconciliation?
HealthTech SaaS
- How can SaaS improve patient intake workflows?
- What integrations do EHR systems need?
- How do you ensure HIPAA compliance in SaaS?
AI Search Analytics Framework (Funnel Model)
Organize AI search impact like a funnel:
1. Awareness Layer
- Metrics: AI mention frequency, share of voice, brand sentiment.
- Tools: AI mention scrapers, social listening tools, branded query audits.
2. Consideration Layer
- Metrics: Traffic from AI chat links, bounce rate, content citation breakdown.
- Tools: Google Analytics events, FullStory, sentiment surveys.
3. Conversion Layer
- Metrics: Lead quality from AI-attributed sessions, CRM tagging, revenue per AI path.
4. Retention Layer
- Metrics: Long-term impact of AI visibility on retention, loyalty, NPS, and upsells.
Best Practices to Boost AI Search Visibility
- Structure Content for Retrieval: Use clear headers, short paragraphs, bullet lists.
- Add Schema Markup: Especially Q&A, Article, How-To schemas.
- Maximize Authority Signals: Reputable sources, credentials, factual clarity.
- Cross-Platform Optimization: Tailor format for Gemini, Claude, ChatGPT, Copilot.
- Set Up AI Alerts: Monitor mentions, inaccuracies, and respond.
- Treat GEO Like CRO: A/B test AI-friendly content and track performance.
Future-Proofing Your SEO Strategy
Traditional SEO metrics no longer capture how AI search engines work. Brands need new KPIsâfrom citation frequency and semantic density to machine-validated authority. If youâre not being retrieved and cited in AI engines, youâre not visible.
HookLead helps SaaS companies build AI-first marketing strategies. We build systems that track your visibility, optimize your content for retrieval, and position your brand as an authoritative source in the AI era.
Ready to find out how visible your brand is in AI search? đ Schedule a free strategy call and letâs build your AI search advantage.