- Jul 17, 2025
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From SEO to GEO and Back Again
How Magnolia lets you own both sides of the modern search coin
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Jan SchulteHead of Group Consulting, Magnolia
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Experience Magnolia's key features firsthand in an interactive product tour.
Take a tour now“This RFP feels oddly familiar …”
When a global retailer sent over their 120-question RFP, every line mapped to Magnolia features with surgical precision—a 100% match that almost never happens.
On our demo session call I asked, “Did you draft this just for Magnolia?”
The lead architect chuckled: “No—we listed our requirements and had ChatGPT generate the RFP. Now we just need you to confirm everything lines up.”
That moment confirmed what many of us suspected: prospects now let AI prequalify vendors before human conversation even starts. Search isn’t one game anymore—it’s two.
1. The search stack has split in two
Apple’s decision to bake Perplexity, Claude, and other AI-native engines directly into Safari is the first real dent in Google’s distribution moat. At the same time, Andreessen Horowitz’s “GEO over SEO” thesis nails the new reality: visibility now hinges on getting cited inside the answers large-language-model (LLM) search engines generate.
Yet classic SEO is anything but dead. Google still drives 63% of U.S. web traffic and rolls out more than a dozen ranking tweaks every single day. Even Google’s own Search Central blog reminds us that the fundamentals behind blue-link success also feed its new AI Overviews.
Takeaway: Marketing leaders suddenly have two performance layers to optimize:
Search Engine Optimization (links)—win the click-through.
Generative Engine Optimization (language)—win the citation.
2. Where GEO and SEO overlap—and where they diverge
Here’s a quick snapshot of where the two disciplines converge—and where you’ll need distinct tactics.
| Works for both | Unique to SEO | Unique to GEO |
|---|---|---|
| High-quality, fact-checked content | Core Web Vitals & page speed | “Extractable” paragraphs, bullets & TL;DR blocks |
| Clear information architecture & canonical URLs | Optimized <title> & meta description | Brand consistency in model training data |
| JSON-LD/Schema.org markup | Backlink authority & internal linking | Reference-rate analytics |
| E-E-A-T signals (author bylines, transparent sourcing, regular content updates) | Conversational Query Analysis (addressing conversational queries from user interactions) | |
| Entity clarity and consistency (consistent definitions and references) | Multi-Engine Visibility Testing (ensuring visibility across AI tools) | |
| Ethical GEO (ensuring unbiased and accurate information) |
3. Why Magnolia gives you a head start on both fronts
Atomic structured content: The Stories App stores every headline, blurb, or fact box as its own node, making it easy for both Google’s crawlers and LLMs to understand and reuse your content.
AI-Accelerated SEO Optimization: The AI-Accelerator enables you to generate SEO metadata with one click for your pages
Traditional & Hybrid Headless delivery: Static-rendered pages satisfy speed-sensitive SEO metrics, while the same content is simultaneously exposed as clean JSON endpoints for LLM ingestion.
AI Accelerator hooks with RAG: Retrieval-Augmented Generation taps your approved content to auto-generate short “In summary” sections and Q&A snippets, ensuring factual accuracy while giving models quotable material—without altering the source copy.
Governance that prevents hallucinations: Versioning and four-eye workflows ensure only approved, accurate facts reach both Google and GPT-4o, protecting brand trust on every surface.
Knowledge Graph Integration: Magnolia helps build structured knowledge graphs, positioning your content as the definitive source directly fed into LLMs.
Proactive Disinformation Mitigation: Magnolia’s built-in versioning and four-eye approval workflows proactively protect brand accuracy, correcting potential misinformation before it spreads via AI.
Ethical GEO Governance: Magnolia ensures content is ethically optimized—free from bias and misinformation—through structured approval workflows, supporting responsible AI optimization.
4. A twin-track optimization playbook you can start this quarter
| Step | What to do in Magnolia | SEO win | GEO win |
|---|---|---|---|
| 1. Componentize long pages | Break hero guides into reusable sections | Cleaner crawl, richer sitelinks | Granular snippets for LLM answers |
| 2. Layer structured data | Add Article, Product & FAQ schema via YAML config | Rich results/knowledge panels | Supplies ground-truth facts to models |
| 3. Generate TL;DR blocks | Call AI Accelerator to propose 100-word summaries. | Improves dwell time & UX | Ready-made quote boxes for LLMs |
| 4. Conduct Conversational Query Analysis | Analyze conversational queries from user reviews, support logs, and internal search | Improved content relevance and organic engagement | Content directly answers conversational AI queries |
| 5. Perform Multi-Engine Visibility Testing | Regularly test and optimize visibility across AI tools like ChatGPT, Perplexity, Gemini, and CoPilot | Broader audience reach and consistent branding | Consistent and accurate brand presence across AI |
Performance Measurement Framework
Discovery Stage Metrics
Traditional: Organic sessions, SERP click-through rates
AI-Era: Model reference rates (percentage of relevant prompts citing your content)
Brand mentions and visibility within zero-click experiences
Consideration Stage Metrics
Traditional: Time on page, bounce rate optimization
AI-Era: Citation depth and prominence within generated responses
Conversion Stage Metrics
Traditional: Goal completion tracking
AI-Era: Attribution analysis for AI Overview and chat interface click-outs
Attribution of conversions resulting from AI-generated overviews and interactions
Strategic Recommendations for Magnolia Clients
Core Principle: Treat Google and GPT as equal optimization partners in 2025.
Magnolia's composable architecture enables organizations to:
Preserve existing SEO investments and expertise
Develop GEO capabilities without platform migration
Scale optimization efforts across both search paradigms
Next Steps: Contact your Magnolia Customer Success Manager to implement the twin-track optimization framework—or ask ChatGPT which solution architect can help you master both sides of the modern search equation.
5. Conclusion: The Evidence-Based Future
The prospect who trusted ChatGPT to build their vendor shortlist never requested traditional sales collateral. They followed AI-generated evidence and recommendations.
Prospects increasingly rely on AI-generated evidence rather than traditional collateral—Magnolia DXP uniquely positions your content for maximum visibility and accuracy across both traditional and generative search.
With Magnolia DXP, you don't face a binary choice between ranking well and being the definitive answer. The platform's architecture delivers both outcomes simultaneously.
See you in the prompts.
Jan
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Magnolia’s AI Accelerator is a collection of generative AI features that speed up content creation, automate tasks, and improve content and design efficiency.
