GEO
GEO vs SEO: What B2B SaaS Teams Need to Know
By ChatLooker Team · Updated 2026-06-20
GEO and SEO both aim to make your B2B SaaS brand discoverable, but they optimize for different surfaces and success metrics. SEO targets ranked pages in search engine results; GEO targets brand mentions and recommendations inside AI-generated answers. For SaaS teams, the critical difference is competitive density — AI answers surface fewer brands per query, and the competitors named are often not the same ones ranking on Google.
You need both disciplines. SEO still drives traffic and domain authority. GEO protects consideration-set inclusion when buyers skip the SERP and ask ChatGPT or Perplexity directly. Treating them as interchangeable leaves a measurable visibility gap.
LLMO vs SEO vs GEO for B2B SaaS teams
LLMO vs SEO vs GEO is a three-layer stack, not a single replacement. SEO optimizes URL rankings in Google. GEO optimizes brand recommendations in generative answers. LLMO optimizes retrieval-friendly structure so models can select and quote your content. B2B SaaS teams need SEO for traffic, GEO for shortlist inclusion, and LLMO tactics (vector-friendly chunks, entity clarity) so content survives retrieval when browsing is enabled.
How are GEO and SEO goals different?
SEO success is measured in rankings, click-through rate, and organic sessions. The unit of optimization is a URL competing for a keyword.
GEO success is measured in mention rate, top-3 recommendation presence, and share of AI voice within a category prompt set. The unit of optimization is the brand entity competing for inclusion in a synthesized answer.
What SEO optimizes
- Page-level relevance for target keywords
- Backlink authority and technical crawl health
- SERP features (snippets, FAQs, sitelinks)
- Conversion from organic landing pages
What GEO optimizes
- Brand-category entity association in LLM knowledge and retrieval
- Coverage across high-intent buyer prompts in AI chat
- Competitive displacement when your brand is absent
- Consistency of brand description across cited sources
Why does Google leadership not guarantee AI visibility?
SERP dominance reflects page-level signals — content depth, links, and relevance for a specific query. AI recommendations reflect entity-level signals — how strongly a brand is associated with a category across the corpus models draw on.
A long-tail blog post can rank #1 for a niche comparison keyword while ChatGPT recommends three well-known incumbents for the broader category question. The ranking page and the recommended brand list answer different scopes of intent.
The competitor replacement problem
ChatLooker visibility data highlights a stark pattern: B2B buyers frequently see 8–18 competitor brands in a single category prompt set, while the target brand appears in only a fraction of high-intent queries. This competitor-replacement-rate dynamic is the GEO equivalent of ranking on page five — you are technically present in the market but absent from the shortlist buyers actually see.
SaaS teams accustomed to tracking five SERP competitors must now track fifteen or more AI-mentioned alternatives, many of which never appeared in their SEO competitive set.
Which metrics should B2B SaaS track for each channel?
Run parallel dashboards. SEO and GEO metrics should inform each other but not be collapsed into one score.
SEO metrics (unchanged)
| Metric | Why it matters |
|---|---|
| Keyword rankings | Traffic potential for target terms |
| Organic sessions | Pipeline contribution from search |
| Domain authority | Long-term ranking competitiveness |
GEO metrics (new)
| Metric | Why it matters |
|---|---|
| Mention rate | How often your brand appears in AI answers |
| Top-3 presence | Whether you are recommended, not just named |
| Competitor overlap | Who replaces you when you are absent |
| Mode variance | Default vs. web-search mention differences |
ChatLooker packages these into visibility checks so teams can benchmark AI share of voice alongside existing SEO reporting. Sources (June 2026): Semrush AI Visibility documentation and Ahrefs Brand Radar.
When should a SaaS team invest in GEO vs SEO first?
If you have zero organic foundation, start with SEO — AI models and retrieval systems still favor brands with authoritative web presence. If you already rank well but sales hears "I found you through ChatGPT recommending [competitor]," prioritize GEO immediately.
Signs you need GEO now
- Category prompts in ChatGPT omit your brand despite strong Google rankings
- Competitors with weaker SEO appear more often in AI answers
- Buyers reference AI-generated shortlists in sales calls
- Your comparison and alternatives content is thin or outdated
Signs SEO still needs attention
- Core product pages do not rank for branded or category terms
- Technical crawl issues limit indexation
- Content gaps exist for primary keyword clusters
How do GEO and SEO tactics overlap?
Several practices benefit both channels. Entity-consistent naming, structured data, authoritative comparison pages, and review-site presence strengthen SEO rankings and AI entity recognition simultaneously.
Shared foundations
- Clear product-category positioning on site and in schema
- Third-party citations (G2, Gartner, industry press)
- Comprehensive integration and use-case documentation
- FAQ and comparison content structured for extraction
GEO-specific additions
- Prompt-set monitoring across AI engines
- Content formatted for direct answer extraction (concise definitions, bullet comparisons)
- Tracking missing-prompt coverage where you should appear but do not
FAQ
Q: My brand ranks on Google but never shows up in ChatGPT answers — what am I missing?
A: Google rank optimizes URLs; ChatGPT shortlists optimize brand entities and training or retrieval signals. You may need stronger entity clarity, comparison content, third-party mentions, and prompt-level monitoring — not more keyword pages alone.
Q: Can strong SEO alone fix poor AI visibility?
A: Not always. SEO builds page authority; GEO requires entity-level association. You may need dedicated comparison content, citation building, and prompt-targeted pages even with strong rankings.
Q: Do AI engines use Google rankings to decide recommendations?
A: Not directly. Retrieval systems may surface highly ranked pages as sources, but the brand list in the answer is synthesized from multiple signals — not a repackaged SERP.
Q: Should GEO and SEO be managed by the same team?
A: Yes, ideally under one search visibility function. Shared content assets serve both, but GEO needs its own measurement layer and prompt intelligence.
Q: How often should we compare GEO and SEO performance?
A: Monthly at minimum. Run a fixed prompt set in AI engines and compare mention distributions to ranking reports for the same intent clusters.
Key Takeaways
- SEO optimizes URLs for rankings; GEO optimizes brands for AI recommendations.
- B2B buyers frequently see 8–18 competitor brands in a single category prompt set, while the target brand appears in only a fraction of high-intent queries.
- Google #1 does not guarantee ChatGPT #1 — track both channels separately.
- Shared entity and content foundations benefit SEO and GEO together.
- Competitor sets for GEO are typically larger than SEO competitive analysis.
- Start GEO when organic is healthy but AI shortlists exclude your brand.