Three custom properties (ai_source, ai_first_engine, ai_last_engine) plus the workflow logic to populate them. The data model that makes every other AEO report possible.
Before you can track ChatGPT referrals or compute AEO ROI, you need a clean data model. HubSpot's default Original Source / Drill-Down 1 / Drill-Down 2 fields aren't structured for engine-level AEO reporting. Here's the property schema and the workflow that populates it correctly across first-touch and multi-touch.
Settings > Properties > Contact > Create. Field type: Dropdown select. Options: chatgpt, perplexity, claude, gemini, copilot, you, brave, none. Default: none. Group: Marketing. This is the single source of truth for which AI engine first touched the contact.
Same dropdown options as ai_source. ai_first_engine captures the engine on first touch (locked). ai_last_engine updates on every subsequent AI-referred session. These two together let you do first-touch vs last-touch AEO comparisons later.
When Lantern or your monitoring tool detects which prompt the citation appeared in, write it here. Example value: 'best aeo tools for b2b saas'. This is the property your CMO will sort by in 6 months when answering 'which prompts drove pipeline?'
deal_ai_source, deal_ai_first_engine, deal_ai_first_prompt. Same field types. These are populated via workflow when a deal is created from an AEO-tagged contact (covered next step). Deal-level fields are what survives into your closed-won reports.
If utm_source contains chatgpt → set ai_source = chatgpt, set ai_first_engine = chatgpt (only if blank), set ai_last_engine = chatgpt. Repeat the branch for perplexity, claude, gemini, copilot. Else set ai_source = none.
Workflow trigger: Deal created. Action: Get associated primary contact. Set deal_ai_source = contact.ai_source. Set deal_ai_first_engine = contact.ai_first_engine. Set deal_ai_first_prompt = contact.ai_first_prompt. Without this rollup, AI source data is invisible at the Deal level.
Create a test contact with utm_source=chatgpt&aeo_prompt=test-prompt-123. Confirm ai_source populates, ai_first_engine locks. Convert the contact to a deal — confirm deal_ai_source carries through. If it doesn't, the deal workflow's 'Get associated' step is misconfigured (most common: 'all contacts' instead of 'primary contact').
The steps above are one link in a longer chain. In order: you pick prompts to monitor, you track AI-referred sessions, you tag contacts in your CRM, you roll attribution up to the Deal object, you report pipeline dollars to the CFO. If you skip any link, the chain breaks and the number you quote to finance can't be defended in an audit.
If you're still evaluating which tool to run this workflow on, Lantern's AEO tool comparison hub has honest head-to-head pages for Profound, Scrunch, Peec AI, AthenaHQ, and HubSpot's own AEO product — scored on the dimensions that matter for a CMO buyer (CRM integration depth, reporting quality, prompt-scaling economics).
If you're about to walk this work into a budget review, the CFO's Guide to AEO Budget Defense has the memo template, the five-slide deck structure, the attribution-math cheat sheet, and the three most-common CFO objections with counter-arguments. It's the long-form companion to this how-to and was written for the renewal conversation specifically.
The operational rhythm that works: run the steps above once to set up, then review the output monthly in a 15-minute standing meeting with your Head of Growth and RevOps lead. Quarterly, re-audit your prompt list, your content backlog, and your attribution lookback window. Annual: present the full-year AEO ROI trend to the board. That cadence is what separates teams who ship an AEO dashboard once from teams who run AEO as an ongoing budget-defensible channel.
Instead of hand-wiring the steps above, Lantern installs the HubSpot properties, the JS snippet, and the pipeline attribution workflow in under 30 minutes — then ships the monthly ROI report your CFO signs off on. $99/mo Starter or Enterprise. 14-day free trial.
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