# SEO Sxo > Search Experience Optimization — the SEO×UX×CRO overlap. Reads the SERP backwards to detect page-type mismatch, derives user stories from intent signals, scores a page from multiple persona perspectives, and fixes engagement/page-experience problems (dwell, pogo-sticking, CWV) that block ranking even on technically perfect pages. Includes a 0-100 SXO gap rubric with falsifiability checks and Next.js patterns. Use when the user says "SXO", "search experience", "page type mismatch", "intent mismatch", "why isn't my page ranking", "pogo-sticking", "dwell time", "engagement signals", "conversion-aware content", "persona scoring", or "SERP analysis". Triggers on: SXO, search experience optimization, page-type mismatch, intent mismatch, pogo-sticking, dwell time, engagement signals, persona scoring, SERP backwards analysis, conversion-aware SEO, page experience. - Skill: `pixartseu/seo-sxo` (Agent Skill) - Install (CLI): `npx skillmds@latest add pixartseu/seo-sxo` - Raw SKILL.md: https://api.skillmd.com/api/skills/pixartseu/seo-sxo/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: PIXARTSeu (https://skillmd.com/u/pixartseu) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pixartseu/seo-sxo --- # Search Experience Optimization (SXO) SXO sits where **SEO** (what the engine rewards), **UX** (what the visitor needs), and **CRO** (what the business needs) overlap. A page only "wins" when all three are satisfied in one pass: the searcher lands, immediately recognizes they're in the right place, gets the answer, and converts — without bouncing back to the SERP. Technical SEO asks "is the page healthy?". SXO asks a harder question: **"Does this page deserve to rank for this query, given what the engine is actually rewarding, and does it satisfy intent end-to-end so the searcher never returns to results?"** ## Core insight: the page-type trap A page can score 95/100 on technical SEO and still never rank because it is the **wrong page type** for the query. If the top 10 results are 8 product pages and 2 comparison tables, a blog post will not break through — no matter how clean its schema or how fast its LCP. Page-experience and content quality are necessary but not sufficient; *format-intent alignment* gates everything else. This is why SXO is **scored separately** from the technical SEO health score. A page can be 95 technical + 30 SXO: perfectly built, strategically misaligned. ## Two modes 1. **Audit** — score a page's search experience, detect mismatch, emit ranked falsifiable fixes (the methodology below). 2. **Implement** — apply the Next.js patterns to fix the engagement, page-type, and conversion-readiness gaps the audit surfaces. For meta/JSON-LD/feeds see `seo-technical`; for content depth & E-E-A-T see `seo-content`; for AI-search surfaces see `seo-geo`. --- ## The engagement loop (the signal SXO actually optimizes) ``` query → click → [land] → dwell? → satisfied? → task done │ │ └── pogo-stick ───────┘ (back to SERP, click next result) ``` - **Pogo-sticking** — user returns to the SERP within seconds and clicks a competitor. Strong negative signal. Causes: intent mismatch, slow/janky load, hidden answer, intrusive interstitials, wrong page type. - **Dwell time** — time between click and return. Long dwell + no return = task satisfied. Not a documented direct ranking factor, but a leading indicator of the satisfaction the engine *does* reward. - **Last-click wins** — the result that ends the session is the one the engine learns to trust for that query. SXO's job: be the **last click**. --- ## Methodology (audit) ### Step 1 — Acquire the target Fetch the **rendered** DOM, not raw HTML — search experience is about what the visitor actually sees, and JS often produces the above-the-fold content (use your own headless crawler / Playwright; force a render so above-fold analysis matches reality). Extract: page type, title, H1, meta description, heading hierarchy, word count, schema types, primary CTA(s), media (img/video/interactive), and the above-the-fold content block. If no keyword is given, infer the primary keyword from the title∩H1 overlap and validate it is non-empty. ### Step 2 — Read the SERP backwards Run the query (your SERP source of choice; note reduced precision if you only have generic web search). For the **top 10 organic** results record: - domain authority tier (brand / niche authority / unknown) - **page type** (see taxonomy below) - content format (long-form, listicle, how-to, comparison, tool, video) - depth estimate, schema signals, media signals And the SERP furniture (each is a free intent signal): - featured snippet format (paragraph / list / table / video) - People Also Ask — capture every question - ads top/bottom — count + copy themes (reveals commercial triggers) - related searches (reveals the journey before/after) - knowledge panel / local pack / shopping / AI Overview + its source types **SERP consensus**: dominant page type (>60% = strong, 40-60% = mixed, <40% = fragmented), depth norm (avg word-count tier), expected schema, media expectation. ### Step 3 — Page-type mismatch detection (the lead finding) Classify the target with the same taxonomy and compare to consensus. If a mismatch exists, **lead with it** — it dwarfs every other fix. **Page-type taxonomy (classify by dominant signal):** | Type | Tells | |------|-------| | Informational / blog | prose, explanatory H2s, no purchase CTA | | Comparison | matrix/table of N options, "vs", "best X for Y" | | Product / PDP | single SKU, price, add-to-cart, specs | | Category / listing | grid of items, filters, faceted nav | | Tool / calculator | interactive input → output | | Landing / service | one offer, lead CTA, proof blocks | | Local | NAP, map, hours, location signals | **Mismatch severity & fix:** | Target | SERP expects | Severity | Fix | |--------|-------------|----------|-----| | Blog | Product/Category | CRITICAL | Build a dedicated product/category page; keep blog as supporting link | | Blog | Comparison | HIGH | Restructure as comparison + decision matrix | | Product | Informational | HIGH | Add an educational/explainer layer above the buy block | | Landing | Tool/Calculator | HIGH | Build the interactive tool component | | Service | Local pack | MEDIUM | Add location signals + LocalBusiness schema (`seo-technical`) | | Match | — | ALIGNED | Compete on depth, page-experience, and conversion clarity | If the SERP is **fragmented** (no dominant type), that's a differentiation opportunity — the format is up for grabs. ### Step 4 — Derive user stories from SERP signals Every SERP element encodes a need. Convert clusters into stories (3-5, covering ≥2 journey stages — awareness/consideration/decision). Each story **must cite the signal that produced it** (no invented personas). ``` As a [persona from signal], I want to [goal from query intent], because [driver from ad copy / PAA tone], but I'm blocked by [barrier from PAA / related searches]. ``` | Signal | Reveals | |--------|---------| | PAA questions | knowledge gaps, objections | | Ad copy themes | commercial triggers, value props | | Related searches | the journey (before/after) | | Featured-snippet format | expected answer shape | | AI Overview | what the engine treats as definitive | ### Step 5 — Gap analysis → 0-100 SXO score Score the target across 7 dimensions (lower total = larger gap). Give **specific evidence** for each — never a bare number. | Dimension | Compare | Pts | |-----------|---------|-----| | Page-type fit | target type vs SERP dominant | 0-15 | | Content depth | word count, heading depth, topic coverage vs norm | 0-15 | | UX / above-fold | does the answer + intent confirmation appear in the first viewport? CTA clarity, mobile layout | 0-15 | | Schema | present vs expected structured-data types | 0-15 | | Media richness | images/video/interactive vs SERP norm | 0-15 | | Authority (E-E-A-T) | author, credentials, social proof, citations | 0-15 | | Freshness | last-updated, date signals, recency | 0-10 | **Total = SXO Gap Score /100** — reported alongside, never merged into, the technical SEO health score. ### Step 6 — Persona scoring Derive 4-7 personas by clustering PAA by theme, segmenting ad copy by audience, and mapping related searches to journey stages. Score each persona on 4 axes (25 pts each): - **Relevance** — does the page address this persona's need? - **Clarity** — can they find the answer in ≤10 seconds (the dwell test)? - **Trust** — enough proof for *this* persona to believe it? - **Action** — is there a clear, persona-appropriate next step? Output one card per persona; **sort fixes weakest-persona-first** (biggest lift). ### Step 7 — IST/SOLL wireframe (only on request) Generate a current-state (IST) outline from the parsed DOM and a target-state (SOLL) outline matching SERP consensus + gap + persona findings. Use **ultra-concrete placeholders**, never vague ones: - NO: "add a CTA here" - YES: "add pricing CTA with annual-savings badge below the hero, linking to `/pricing#enterprise`" Emit as a semantic HTML section outline with annotations. --- ## Falsifiability — how would we know each fix failed? SXO recommendations are hypotheses about behavior. Every fix ships with a **leading indicator** (moves in days/weeks) and a **failure condition**. If the indicator doesn't move, the hypothesis was wrong — revert or rethink, don't pile on more changes. | Fix | Leading indicator | Failure condition (revert/rethink) | |-----|-------------------|------------------------------------| | Resolve page-type mismatch | impressions appear for the query cluster within 2-4 wks (GSC) | still zero impressions after re-index + 4 wks → wrong type or topical authority gap | | Lift answer above the fold | scroll-to-answer depth ↓; SERP return-rate ↓ | bounce/return-rate flat → answer wasn't the blocker (intent mismatch?) | | Cut INP / fix layout shift | INP <200ms, CLS <0.1 (field, p75) | field metrics unchanged after 28-day window → lab-only win, real users unaffected | | Add proof for weak persona | conversion rate for that segment ↑ | CR flat → trust wasn't the barrier; re-score relevance/clarity | | Strengthen CTA clarity | CTA click-through ↑ | CTR flat → wrong offer or wrong page-type, not wording | | Tighten title/meta to match snippet format | organic CTR ↑ in GSC | CTR flat/down → snippet promised something the page doesn't deliver | Rule: **one change per hypothesis where feasible**, so a moved (or unmoved) indicator is attributable. --- ## Page experience as a Core Web Vitals problem (current thresholds) Page experience is the UX leg the engine can measure directly. Field (CrUX) p75 targets — **INP replaced FID in March 2024**: | Metric | Good | Why it's an SXO lever | |--------|------|-----------------------| | **LCP** | < 2.5s | slow hero → pogo-stick before the page even paints | | **INP** | < 200ms | janky taps after load → frustration, abandon | | **CLS** | < 0.1 | content jumping → mis-taps, lost trust | CWV are a *tiebreaker among relevant results*, not a substitute for relevance — fix intent first, then page experience. Implementation lives in `seo-technical`; SXO just demands the field numbers as a gate before declaring an experience "good". --- ## Next.js patterns (implement) ### Put the intent-confirming answer in the first viewport (RSC, no client JS) The single biggest dwell lever: the searcher must confirm "right page" instantly. Render the answer block server-side, above any heavy/interactive content. ```tsx // app/[locale]/[slug]/page.tsx — RSC, answer-first layout export default async function Page({ params }: { params: Promise<{ slug: string }> }) { const { slug } = await params; const page = await getPage(slug); return (
{/* Above the fold: directly satisfies the query, no scroll, no JS needed */}

{page.h1}

{/* The "answer in 10 seconds" block — the dwell/pogo-stick defense */}

{page.answer}

{page.primaryCta && ( {page.primaryCta.label} )}
{/* Defer heavy/interactive depth below — never block first paint */}
); } ``` ### Defer non-critical interactivity to protect LCP/INP Keep the above-fold static; lazy-load comparison tables, calculators, embeds. ```tsx import dynamic from "next/dynamic"; const ComparisonMatrix = dynamic(() => import("@/components/ComparisonMatrix"), { loading: () =>
, }); ``` ### Eliminate CLS on hero media (intrinsic dimensions + priority) ```tsx import Image from "next/image"; {hero.alt}; ``` ### Conversion-aware metadata: make the snippet a promise the page keeps CTR is an SXO signal too — but only if the title/description match what the page delivers, in the snippet format the SERP rewards. Mismatched promises raise CTR then spike pogo-sticking, which is worse than a lower CTR. ```ts // app/[locale]/[slug]/page.tsx import type { Metadata } from "next"; export async function generateMetadata({ params }: { params: Promise<{ slug: string }> }): Promise { const { slug } = await params; const p = await getPage(slug); return { title: p.metaTitle, // mirrors the H1 promise description: p.metaDescription, // states the concrete payoff, no clickbait alternates: { canonical: `/${slug}` }, openGraph: { title: p.metaTitle, description: p.metaDescription }, }; } ``` ### Match the featured-snippet format with structured markup If the SERP rewards a list/table snippet, give the answer that shape and back it with the matching schema (`FAQPage`, `HowTo`, `Product`) — see `seo-technical` for the JSON-LD helpers. The format must exist in the DOM, not just the schema. ### Don't sabotage experience with interstitials Intrusive interstitials (full-screen popups on load, especially mobile) are a documented demotion signal and a direct pogo-stick cause. Gate consent/marketing modals so they never cover the above-fold answer on the first interaction. --- ## Output format (audit) ``` ## SXO Analysis: [URL] — keyword: [keyword] 1. SERP landscape — dominant type ([confidence]%), features, depth norm, schema norm 2. Page-type alignment — your type vs expected → ALIGNED | MISMATCH (severity) + impact 3. User stories (3-5, each citing its source signal) 4. Gap analysis — SXO Gap Score XX/100 (7-dimension table with evidence) 5. Persona scores (4-7 cards, weakest first) 6. Page-experience gate — LCP/INP/CLS field p75 vs thresholds 7. Priority actions — mismatch first, then weakest-persona gaps; each with a leading indicator + failure condition 8. Limitations — what couldn't be assessed; data-source precision note ``` ## Cross-skill handoffs | Finding | Hand off to | |---------|-------------| | E-E-A-T / depth gaps in scoring | `seo-content` | | Missing/format-mismatched schema | `seo-technical` (or `seo-schema`) | | Local intent in the SERP | `seo-geo` / local handling | | CWV / crawl / index issues during fetch | `seo-technical` | | AI Overview as dominant surface | `seo-geo` | ## Quality checklist - [ ] Target fetched as **rendered** DOM (above-fold matches what users see) - [ ] ≥5 SERP results classified with the taxonomy - [ ] Mismatch severity rated and **led with** if present - [ ] Every user story cites a specific SERP signal - [ ] Persona scores include concrete, persona-specific fixes - [ ] SXO Gap Score labeled **separate** from technical SEO health - [ ] CWV field thresholds applied as a gate (LCP<2.5s, INP<200ms, CLS<0.1) - [ ] Every fix carries a leading indicator + failure condition - [ ] Limitations section present and honest > Parts adapted from [claude-seo](https://github.com/AgriciDaniel/claude-seo) (MIT, © 2026 agricidaniel).