Should I include AEO-attributed lost deals?

Yes — separately. Closed-won interviews tell you what worked. Closed-lost interviews (a different script) tell you where AI mentioned you but the post-citation experience failed. Both are valuable; do them as separate cohorts.

Will customers remember which AI engine they used?

Often no. They remember 'an AI tool' or 'I was asking AI'. Don't push too hard for engine specificity; the prompt and the context are what matter. Your data already tells you which engine — the interview enriches the why.

Can the AE / CSM run these interviews instead of marketing?

Yes — and arguably better. The AE/CSM has trust with the customer. Brief them on the 5-question script. Marketing then synthesizes patterns across the call notes.

How does this connect to AEO budget defense?

These verbatim quotes are gold for the CFO conversation. 'Customer X said: I was asking ChatGPT for AEO tools and Lantern came up' is more emotionally compelling than any attribution chart. Pair them in the renewal deck.

How to interview closed-won deals for AEO insights (2026)

Quantitative AEO attribution (citation → session → contact → deal) tells you what happened. Qualitative interviews tell you why. The combination is more powerful than either. Here's the five-question script that works in a 15-minute call appended to a kickoff or QBR.

Required tools

HubSpot for the closed-won deal list with ai_source data
A scheduling tool (the QBR slots are usually already in calendar)
Recording + transcription (Otter, Fathom, Granola) — with consent
30 minutes per quarter to compile patterns across 5–10 interviews

The steps

Identify the interview cohort: last 30 closed-won deals where ai_source != 'none'

Filter HubSpot Deals: stage = Closed Won, closedate within last 90 days, deal_ai_source is any of the AI engines. Pull contact name + deal champion. Aim for 5–10 interviews per quarter; more isn't necessary, fewer means small-sample bias.

Schedule the interview: 15-minute slot appended to existing kickoff or QBR call

Don't ask for a separate call — that drops accept rate to <30%. Append 15 minutes to a call that's already scheduled. Frame: 'We're studying how customers found us; would you be open to 15 minutes of how-you-found-us research at the end of our kickoff?' Accept rate typically 70%+.

Question 1: 'Walk me through the moment you first heard of [our product]'

Open-ended on purpose. Capture verbatim what they say — was it ChatGPT, was it a peer, was it a search, was it an ad. The way they spontaneously describe it tells you which channel anchored the memory. Many will say 'I was asking ChatGPT about X and your name came up' even if your data shows them as direct traffic.

Question 2: 'What were you searching for / asking about when our name came up?'

This is the prompt-discovery gold. Customers will name specific queries that aren't in your tracked prompt list. Add them. This is the most reliable source of new prompts to monitor — way better than SEO keyword tools.

Question 3: 'Did you compare us to other AI-recommended options? Which?'

Surfaces competitor co-citation. If your customer says 'I asked ChatGPT and you came up next to [competitor]', you now know which prompts you need to win the head-to-head on. This is also the data that justifies your /compare/ pages.

Question 4: 'After the AI mentioned us, what was the next 3 things you did?'

Maps the post-AI buyer journey. Did they go to your homepage? Read your reviews? Click into your /compare/? Their answer reveals which post-citation pages need to convert better. This is where revenue capture optimization lives.

Question 5: 'What almost stopped you from buying us?'

The objections you didn't address in your AEO content. Patterns across 10 interviews surface the missing FAQ on your AEO-cited pages. Most teams discover that 30–40% of objections are easily preemptable in content.

Common mistakes

Asking 'how did you hear about us?' as a single question — too generic, customers default to 'someone at my company mentioned you'. The 5-question structure pulls out the AI-specific story.
Not recording the calls — you'll miss verbatim language that becomes the next quarter's content.
Skipping the question on competitors — you lose the most actionable AEO competitive intel.
Doing only 1–2 interviews and generalizing — you need 5+ to see patterns reliably.

Where this fits in the AEO pipeline attribution stack

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.

How to interview closed-won deals for AEO insights