The AI Referral Playbook

The AI Referral Engine: Turning Model Citations into Pipeline

AI assistants don’t just answer questions—they implicitly refer vendors. The AI Referral Engine is the system that earns those citations on purpose, routes assistant-driven visitors to the right page, and converts them into revenue with clear bridge paths.

Why This Matters Now

When buyers ask “Which policy fits a contractor vs. a startup?” or “What’s the best platform for X?”, models pull from sources they trust and link (or paraphrase) those sources. If your canonical facts are the ones models read and cite, you become the default recommendation. That’s a referral—at scale, 24/7.

What Counts as an AI Referral

Any session that originates from an assistant-linked page or a prompt-influenced path (e.g., a user copies an assistant’s cited URL into the browser). Practically, you’ll see this as direct/unknown with assistant-looking titles in the referrer chain, Bing-grounded link trails, or sudden traffic to highly specific wiki URLs after prompt events.

Anatomy of a Referral Path

Question → Assistant answer → Cited source (your wiki/reference page) → Bridge module (Pricing / Compare / Security / Book consult) → Qualified action.

Build the Engine in Five Steps

1. Map high-intent prompts. List the top 25 questions a serious buyer asks an assistant before contacting sales.

2. Publish model-ready pages. Create neutral, datum-dense pages that answer one intent each, with schema, stable slugs, and visible changelogs.

3. Add bridge modules. On every cited page, embed explicit next steps that match the question’s intent.

4. Place corroboration. Earn supporting mentions on domains assistants already surface (associations, standards, trusted directories).

5. Instrument & iterate. Track assistant-attributed sessions and run prompt-level experiments to tighten the bridge.

Bridge Modules (Your Conversion “Handrails”)

• Definitive Summary: one-paragraph answer above the fold with anchor links to details.

• Decision Table: side-by-side criteria (When to choose A vs. B).

• Compare vs. X: prewritten comparisons for top alternatives.

• Pricing & Eligibility: clear plan matrix and “Get a quote / Request pricing” CTA.

• Security Review Pack: one-click PDF/HTML bundle for due diligence.

• Talk to an Expert: calendar embed tuned to the page’s intent.

Instrumentation & Taxonomy (Minimal but Sufficient)

• UTM discipline on all bridge links: utm_source=wiki&utm_medium=ai-referral&utm_campaign=<intent-slug>

• Page metadata: expose dateModified and lastmod in sitemaps; include JSON-LD about, citation, and isBasedOn fields.

• Event names: ai_referral_seen, bridge_click, qualified_action, assistant_citation_detected.

• Session notes: capture landing URL, prior referrer (if any), and the “intent slug” inferred from the page.

Core KPIs (and Benchmarks to Aim For)

• AI Referral Share: % of total sessions landing on wiki/reference URLs. Target 10–20% within 90 days for content-heavy sites.

• Bridge Click Rate: clicks on embedded next-step modules ÷ wiki page sessions. Target 12–25%.

• AI Referral Conversion Rate: qualified actions ÷ assistant-landed sessions. Target 3–8% for B2B, higher for transactional.

• Qualified Citation Velocity: new citations from authoritative domains per quarter. Aim for steady growth (e.g., +5–10 net citations/qtr).

Experiments That Move the Needle

• Title clarity tests: “Builder’s Risk vs. Course of Construction: Definitions, Use Cases, Changelog” vs. generic titles.

• Above-the-fold modules: placing the Decision Table before the fold typically lifts bridge CTR.

• One-intent-per-URL refactors: splitting broad guides into discrete, citeable pages.

• Changelog visibility: adding “Reviewed on ” increases assistant trust and human confidence.

Example Referral Paths (Teardown-Style)

• Comparison query → assistant cites /wiki/comparisons/d-and-o-vs-e-and-o/ → Decision Table → “Talk to an Expert” → booked consult.

• Pricing query → assistant cites /wiki/pricing/enterprise/ → plan matrix → “Request pricing” → MQL with budget & timeline fields prefilled.

Common Failure Modes

• Great reference page, no bridge: users bounce after getting the answer.

• Multiple intents on one URL: models cite you less; humans get lost.

• Heavy JS or slow pages: assistants fail to parse; humans abandon.

• Inconsistent facts across site/blog/PDFs: models choose another source.

30/60/90 Execution Plan

• 30 days: Stand up five highest-intent wiki pages with bridge modules; ship sitemap lastmod; add instrumentation.

• 60 days: Add ten more pages; place two corroborating references per critical topic; run two title/summary experiments.

• 90 days: Publish platform-specific learnings (what gets cited by which assistant); prune and merge underperforming pages; report AI Referral Share and pipeline impact.

Governance to Keep It Working

Assign owners per page, with SLAs (e.g., pricing monthly, security quarterly). Keep a public “What’s New” log. Treat comparisons and claims as controlled content requiring Legal/PM approval. Consistency beats velocity if forced to choose.

FAQ

How fast can we see referrals? With strong IA and clean pages, initial citations and assistant-driven sessions often appear within 2–6 weeks of publish and crawl.
Can we influence which URL gets cited? Yes—use single-intent pages, stable slugs, canonical tags, and definition-first intros.
What about platforms that don’t always show links? Even paraphrased answers rely on sources; your goal is to become the source of record and measure the downstream traffic and conversions from the associated queries.

Checklist (Print This)

One intent per URL.
Definition + Decision Table above the fold.
Bridge modules present and tracked.
Schema applied; lastmod exposed; fast HTML.
Two corroborating sources per critical page.
Weekly review of assistant-attributed sessions and bridge CTR.

CTA

Want this running in two weeks? Book a demo. We’ll map your top prompts, publish model-ready pages with bridges, place corroboration, and wire up measurement—so assistants don’t just cite you, they refer you.