Types of SEO (The Complete Map)

“SEO” is a single label for at least twenty distinct disciplines. A local plumber, a B2B SaaS, an Amazon seller, an iOS developer, and a news publisher are all “doing SEO” and almost none of their day-to-day work overlaps. This module is the taxonomy — what each type means, who needs it, and which surface it targets.

TL;DR

• SEO is a family of disciplines, not one job. Twenty-plus types map to specific surfaces (Google, Bing, App Store, Amazon, AI Overviews, ChatGPT, Perplexity, YouTube) and specific business models. Specializing matters more than breadth above ~$5M revenue.
• The 2026 explosion is on the AI side. GEO, AEO, LLMO, and AISO are the new acronyms. The vendors disagree on definitions; the underlying work is the same — make your content easy to extract, cite, and trust.
• Hat color still matters in 2026. Black hat is faster but the half-life is shorter than ever — Google’s SpamBrain runs continuously, not in scheduled updates. Gray hat is shrinking. White hat is the only model that compounds.

The mental model

Types of SEO are like specialties in medicine. A general practitioner can handle 80% of cases — that is your generalist SEO. But you do not want a GP doing your heart surgery, your knee replacement, or your dermatology — those are e-commerce SEO, enterprise SEO, and local SEO, each with their own tooling, terminology, and failure modes.

The acronyms that look interchangeable usually are not. Local SEO is GBP optimization, citation building, and review management for a specific service area. International SEO is hreflang, ccTLD/subfolder strategy, currency, and CDN topology for a multi-region presence. They share zero tools beyond the analytics dashboard.

The other implication: picking the wrong specialty wastes your career. Strong programmatic SEO talent at a content publisher is wasted; strong content SEO talent at an Amazon seller is wasted. Match the type to the business.

Deep dive: the 2026 reality

The current taxonomy:

Type	What it is	Primary surface	Who needs it
White hat SEO	Compliant with search engine guidelines	All surfaces	Every legitimate business
Black hat SEO	Deliberate guideline violation for short-term gain	Google, mainly	Affiliate / spam operators
Gray hat SEO	Aggressive but not explicitly forbidden	Google	Risk-tolerant marketers
Local SEO	GBP, citations, NAP, reviews, local content	Google Maps, local pack	Service-area businesses
International SEO	Hreflang, ccTLDs, multi-language, currency	Google country domains	Multi-region brands
E-commerce SEO	Product schema, faceted nav, category architecture	Google Shopping, organic	Online retailers
Enterprise SEO	100K+ URL governance, internal SEO platform, dev coordination	Google, Bing	Fortune 1000, large publishers
Mobile SEO	Mobile-first indexing optimization, INP, page speed	Mobile Google	All sites (mobile-first since 2019)
Voice SEO	Conversational query optimization, FAQ, snippets	Assistants (Siri, Alexa, Google Assistant)	Local businesses, FAQ-heavy sites
Image SEO	Alt text, file names, schema, formats (WebP, AVIF)	Google Images, image carousels	Visual product, recipe, photography sites
Video SEO	YouTube optimization + on-site video schema	YouTube, video carousels	Creators, brands with video
News SEO	Google News, Top Stories, freshness, NewsArticle schema	Google News, Top Stories	Publishers, journalists
ASO (App Store Optimization)	App Store + Play Store optimization	iOS App Store, Google Play	Mobile app developers
Marketplace SEO	Amazon A10, eBay, Etsy, Walmart Marketplace algorithms	Amazon and peers	Marketplace sellers
Programmatic SEO	Templated pages from structured data	Google long-tail	Sites with combinatorial query patterns
Parasite SEO	Ranking via high-authority hosts (Medium, LinkedIn, Reddit)	Google	Affiliates, agencies (high risk in 2026)
Negative SEO	Attacks against competitors (toxic links, content scraping)	Defensive concern only	Anyone with valuable rankings
GEO (Generative Engine Optimization)	Optimizing for AI Overviews, ChatGPT, Perplexity citations	All AI surfaces	Modern publishers, brands
AEO (Answer Engine Optimization)	Concise answer extraction for snippets and assistants	Featured snippets, AI Overviews	FAQ-heavy, B2B SaaS
LLMO (Large Language Model Optimization)	Brand presence in LLM training data	ChatGPT, Claude, Gemini long-term	Brand-focused organizations
AISO (AI Search Optimization)	Umbrella term for GEO + AEO + LLMO	All AI surfaces	Same as GEO

The GEO / AEO / LLMO / AISO family is contested. Vendor blogs use them interchangeably; the academic GEO paper (Aggarwal et al., 2024) used “Generative Engine Optimization” specifically for citation rate inside LLM-generated answers. In practice, the work overlaps more than 80%. Pick one term internally and stick with it.

Parasite SEO in 2026 is much riskier than in 2023. Google’s March 2024 core update specifically demoted “site reputation abuse” — third-party content hosted on authoritative domains for SEO purposes — and the November 2024 explicit policy update made it a manual action target. Forbes Advisor, CNN Underscored, and similar operations were all hit.

Negative SEO is largely defanged for backlink attacks (Google’s Penguin 4.0 ignores most spam links rather than penalizing) but content scraping and brand impersonation attacks remain effective. The 2026 vector that works is prompt injection in content intended to manipulate AI Overviews — still emerging, still poorly defended.

Visualizing it

flowchart TD
  SEO["SEO universe"] --> WH["White hat"]
  SEO --> GH["Gray hat"]
  SEO --> BH["Black hat"]
  WH --> Trad["Traditional: technical, on-page, off-page, content"]
  WH --> Vert["Vertical: local, e-commerce, news, video, image"]
  WH --> Plat["Platform: ASO, marketplace, YouTube"]
  WH --> AI["AI surface: GEO, AEO, LLMO, AISO"]
  Trad --> Out["Sustainable rankings"]
  Vert --> Out
  Plat --> Out
  AI --> Out
  GH -. "shrinking window" .-> Risk["Risk of penalty"]
  BH -. "SpamBrain continuous" .-> Pen["Penalty likely"]

Bad vs. expert

The bad approach

A B2B SaaS hires a "generalist SEO" who applies the same playbook regardless of the business.
- Treats programmatic SEO as the answer for a 30-page documentation site.
- Optimizes for Google Maps even though there is no physical location.
- Ignores AEO because "AI is hype."
- Spends Q1 building "topic clusters" for keywords with $0 commercial intent.
Result: 6 months in, organic traffic is up 20% but pipeline is flat. The traffic landed on the wrong pages for the wrong intent.

This fails because SEO type was never matched to business model. Programmatic SEO works for combinatorial query patterns; a documentation site has no such pattern. Local SEO works for service-area businesses; a remote SaaS has no service area. Topic clusters built without commercial intent earn impressions, not pipeline.

The expert approach

# Vertical-matching SEO scope for a B2B SaaS company
business_model: B2B SaaS, $5M ARR, North America focus
seo_disciplines_to_run:
  primary:
    - on_page_seo: "money page optimization (pricing, /vs/, /alternatives/, /integrations/)"
    - aeo: "answer-engine optimization for buyer queries"
    - geo: "AI Overview citations on commercial-investigation queries"
  secondary:
    - content_seo: "topic clusters around buyer pain points"
    - off_page_seo: "digital PR, partner co-marketing, podcast appearances"
  excluded:
    - local_seo: "no physical location"
    - programmatic_seo: "documentation does not have combinatorial demand"
    - aso: "no mobile app"
    - news_seo: "not a publisher"
quarterly_focus_q1: "Money pages: pricing, /vs/, /alternatives/. AEO + GEO on top 50 commercial queries."

This works because the scope is explicitly bounded to the disciplines that map to the business. Energy is concentrated on commercial-intent surfaces and AI surfaces where buyers research. The disciplines that do not apply are explicitly named and excluded — preventing well-meaning generalists from spending budget on local citations for a remote SaaS.

Do this today

1. Write your business model in one sentence: who buys, where, on what device, after how much research. Save it at the top of your seo-mastery-log sheet.
2. Cross-reference the table above. Highlight the 2-4 SEO types that map directly to your business model.
3. For each highlighted type, find one vertical-specific tool: Local = BrightLocal or Whitespark; E-commerce = Helium 10 (Amazon) or Shopify SEO module; News = Google Publisher Center; Video = TubeBuddy or VidIQ; ASO = AppTweak or Sensor Tower; AI Surfaces = Otterly.ai, Profound, or AthenaHQ.
4. Open Google Search Console > Performance and segment by Search Appearance. Each appearance type (AI Overview, Web Light, Videos, Recipes, Job Postings) corresponds to an SEO type — note which already drive traffic.
5. Audit your robots.txt for AI crawler access: confirm GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended are not blocked unless you have a deliberate policy.
6. For each excluded SEO type, write one sentence in your log explaining why it does not apply. This stops future quarters from drifting into wasted-effort disciplines.
7. Read Module 7 (White Hat vs Black Hat vs Gray Hat) next to map the risk profiles within your selected disciplines.