AEOflux

Inclusion Likelihood (Domain & Page)

Probability to be cited in AI answers – methodologically sound, measurable, actionable

Back to homepage Start free demo analysis

Inclusion Likelihood estimates how likely your content is cited in answer engines (Google AI Overviews, Bing Copilot, Perplexity). It derives from three core signals: a concise short answer (Answer@1), SERP readiness (structured signals), and a health share (HTTP/content baseline).

How do we measure Inclusion Likelihood?

Answer@1 (0–10): Is there an above‑the‑fold short, declarative answer to the core question?
SERP Readiness (0–10): Do structured elements exist (JSON‑LD, lists, tables, canonicals)?
Health share (0–10): HTTP 200, reasonable content length, no blockers.
Weighting: 60% Answer@1, 35% SERP, 5% Health → robust inclusion‑oriented signal.
Domain aggregate: Mean across all pages with trend (timeline by audit timestamps).

Page‑level checks (practical)

Above‑the‑fold answer paragraph (40–120 words) with clear definition/claim.
Structured data (FAQ/HowTo/Article/Breadcrumb) implemented consistently.
List or table present when suitable (process, comparison, specs).
Title/H1 consistent with the answer paragraph.
HTTP 200, sensible content length (not ultra short, not overloaded).

Domain‑level evaluation

Page averages yield domain Inclusion Likelihood.
Trend analysis per audit: improvements visible after editorial cycles.
Prioritize pages with strong SERP readiness but weak Answer@1 first.

Actionable steps (5‑step plan)

Add an answer block at the top (definition, claim, use case).
Add JSON‑LD (FAQ/HowTo/Article) – valid & minimalistic.
Add list/steps/table where suitable (snippet‑friendly).
Refine Title/H1; mirror query phrasing in H2/H3.
Check technical baseline: 200 status, readability, no blockers.

Improve Inclusion Likelihood now

Run an AEOflux analysis and get a prioritized list of pages with concrete editorial actions.

Start free demo analysis