Attribution for AI-Assistant Buyer Journeys
Executive Summary
A practical measurement framework for GEO, AEO, and AI SEO in 2026
Assistants now sit between your buyer and your site. Clicks still matter but influence often happens off your website. Seer’s work shows CTR drops when AI Overviews appear, so “organic” no longer maps cleanly to pipeline.
Measure three things:
Captured demand: sessions, leads, revenue you can count.
Assisted influence: proof you were in the mix without a visit.
Answer layer: where you’re cited and whether it’s correct.
How discovery works now
A buyer asks an assistant for options.
The assistant answers and cites a few pages.
The answer gets pasted into Slack, email, or a doc.
Later, someone searches your brand, visits direct, or clicks a retargeting ad and your reports miss the assistant.
Google now surfaces links inside AI Overviews and AI Mode, changing the path from question → exploration → evaluation.
Why old attribution breaks
Fewer clicks. AI Overviews lower CTR on many informational queries. Same “visibility,” fewer sessions.
Dark influence. AI answers spread inside teams with no referrer.
Messy sources. Tow Center for Digital Journalism testing found high error rates in AI search citations for news. Track presence and accuracy.
Goal: tie work to pipeline across a mediated journey, not only last-click.
The Attribution Stack
1) Captured (count it)
Q: How much demand reached owned properties and revenue from AI surfaces?
Signals: assistant referrers, assists where assistant traffic appears earlier, branded/direct lift tied to AI visibility work.
2) Assisted influence (validate it)
Q: Is the assistant shaping preferences without a click?
Signals: HIHYBU mentions of assistants, CRM/Sales notes marked “assistant-influenced,” win-loss notes citing summaries/shortlists/comparisons.
3) Answer layer (watch it)
Q: Are you cited, correct, and in the right slot?
Signals: citation share on priority queries, mention quality (category, positioning, claims), consistency across assistants and Google AI features.
Count what you can. Validate influence. Monitor representation.
Step 1: Capture assistant traffic in GA4 (no rebuild)
Create a custom channel group: AI assistants.
Add referrer rules for major assistants (start strict, widen with real data).
Build a simple dashboard: sessions, engaged sessions, key events, pipeline, revenue.
Add a view for AI-assistant landing pages; these often feed future answers.
Expect assistant visits to sit in Referral or Unassigned until grouped.
Step 2: Treat citations like distribution
Search now shows inline citations and source panels:
Primary source quoted
Supporting source listed
Deep link clicked during evaluation
Run a weekly citation log on 20–50 priority queries. Record: are you cited, which page, accuracy, and category. Tie shifts to branded search, forms that mention assistants, and sales speed on influenced deals.
Step 3: Match measurement to AI features
Skip the dream of a single “AI Overview report.” Instead:
Group queries by job: discovery / comparison / implementation.
Track impressions / clicks / CTR by group.
Expect lower CTR where AI answers satisfy early intent.
Step 4: Add two fields to surface assisted influence
HIHYBU on high-intent forms with options: ChatGPT, Gemini, Copilot, Perplexity, Google AI summary/Overview, traditional search, peer, analyst/community, Other (free text) for later coding.
“Influenced by AI summary?” in CRM (sales-owned): one checkbox + notes when buyers reference a summary, shortlist, or AI comparison.
Now you can see share, pipeline, and win rate for assistant-touched deals.
Step 5: Build a 2026 scorecard
Captured (GA4/CRM)
AI-assistant sessions (custom channel)
Conversion rate (key events → demo → SQL)
Pipeline with assistant touches (first touch, assist, or noted influence)
Influence (self-report + sales)
% of inbound forms that mention assistants
% of opportunities marked “assistant-influenced”
Win-rate gap: influenced vs not (directional)
Answer layer (visibility + quality)
Citation share on the priority set
Accuracy rate (right category + correct claims)
Competitive displacement (where rivals replace you)
Presence alone can mislead; Tow Center found high citation error rates.
Step 6: Prove contribution with experiments
1) Content lift (90 days).
Pick 10–20 discovery/evaluation pages. Improve structure, clarity, and references. Track citation presence, brand lift, assisted conversions, and pipeline quality. Google’s “succeeding in AI search” guide points to helpful, distinct content that supports follow-ups.
2) Market holdout (geo or segment).
Apply updates to one region or vertical. Hold another steady. Compare branded demand, CAC, conversion rate, and sales-cycle length.
You don’t need perfect tracking. Apply consistency and a clear hypothesis.
Notes for CMOs
Revenue. Expect fewer early-intent clicks even with strong visibility. Focus on influence → branded demand → higher conversion, not raw sessions.
Ops. Standardize the basics: one GA4 channel group, two CRM fields, one weekly citation log, one monthly scorecard. Make these the default reports; stop one-off debates.
Risk. AI answers misstate brands and sources. Track accuracy and context alongside exposure. Set an owner, fix wrong mentions, and recheck.
30-day plan
GA4: Add the “AI assistants” channel group and a dashboard.
CRM: Add the assistant picklist + the sales influence checkbox.
Content ops: Start the weekly citation log for 20 priority queries.
Exec reporting: Publish the monthly stack scorecard.
Experiment: Launch one 90-day content lift tied to pipeline.
What to do next
Run a 90-day pilot: one attribution dashboard + one citation log + one content lift test. At the end, answer: “Are assistants helping us win deals, and where?”
Last updated 01-07-2026
