Analysis
How commercial intent develops in AI conversations
An analysis of 150,000 real AI conversations
Most assumptions about how users make purchase decisions in AI search are wrong. We analysed 150,000 real conversations to find out what actually happens when commercial intent enters an AI conversation.
Published March 2026 by CitationLab
The dataset at a glance
- conversations analysed
- 150,000
- with commercial intent
- 6.8%
- avg turns (transactional)
- 2.92
- single-turn conversations
- 48%
01
What do people actually use AI for?
Before examining commercial conversations, we mapped the full distribution of conversation types. Transactional conversations are a tiny minority of AI usage.
02
Seven key findings
6.8%
01Commercial conversations are a minority
The vast majority of AI conversations are about coding, writing, and information retrieval. Under 7% have commercial intent.
31%
02Price is the strongest buying signal
When a user asks about price, it triggers a cooling event 31% of the time. Price questions are buying signals that AI consistently fails to convert.
-69 points
03Price triggers sharp cooling
Average intensity drop after a price-related cooling event is -69 points. The user goes from ready-to-buy to back at square one.
91%
04Users fall back to TOFU after AI hedging
Over 90% of post-price cooling events result in regression to Problem Focus. The AI answer fails to close the decision gap.
41%
05Many buyers start at BOFU
Users don't follow a discovery arc. Over 4 in 10 already know what they want when they open the conversation.
4.4%
06The progressive arc is rare
Only 4.4% of transactional conversations follow the traditional TOFU-to-BOFU progression. The linear funnel is a myth in AI search.
2.92 turns
07Transactional conversations are short
Commercial conversations are brief and action-oriented. Nearly half are single-turn. Users want answers fast.
03
The intensity arc
Average commercial intensity across conversation turns. Note the sharp drop from Turn 1 to Turn 2, and the flat TOFU plateau from Turn 2 onwards.
04
The post-price collapse
Price questions signal buying intent. But the AI's answer typically fails to close the deal, sending the user back to square one.
What happens after a price-triggered cooling event?
05
Why does this happen?
The failure is not the price itself. It is the precision of the AI's answer. Three response patterns consistently trigger user regression.
Low closure
Vague estimate
AI gives a price range without helping the user navigate it.
'Prices range from X to Y depending on location and condition'
Medium closure
Hedged answer
AI answers correctly but adds a caveat that opens a new concern.
'It costs X, but you should check availability first'
No closure
Inspirational divergence
AI gives an answer that triggers the user to pivot to a larger, unrelated goal.
User asks about hiring cleaners → AI gives cost → user pivots to 'how do I start a cleaning business'
06
Real conversation examples
Three documented conversations from our dataset showing the price-to-cooling pattern in action.
01
Silent regression
User researching used cars asks about costs. AI gives vague estimates. User abandons the topic entirely.
02
Uncertainty cascade
User with a booked Genius Bar appointment asks about diagnostics. AI hedges. User loses focus and drifts to unrelated topics.
03
Scope expansion
User asks about day rates for window cleaners. AI gives a price range. User pivots from hiring cleaners to starting a cleaning company.
07
What you can do about it
The root cause of low decision closure is website content that forces AI to hedge. Four content patterns on source websites reliably cause poor AI answers.
Vague price ranges
→ Replace 'prices from X' with scenario-based pricing. E.g. 'For a standard 150m2 house: X. For 200m2: Y.'
Conditional language in main text
→ Move all disclaimers to a separate FAQ section. Lead with the concrete answer.
Missing structured data
→ Implement JSON-LD schema markup (Product, Offer, PriceSpecification) for all transactional pages.
No 'next step' content
→ Every service page needs a clear, machine-readable conversion path. Tell the AI what happens after purchase.
08
Frequently asked questions
Only 6.8% of the 150,000 AI conversations analysed had commercial intent. The vast majority cover coding, writing, and general information retrieval.
In this analysis, 66.8% of conversations fell into an 'other' category, while 12.6% were writing-related, 12.3% were coding-related, and 3.4% were information-retrieval. Transactional conversations represented only 1.8% of all conversations.
When a user asks about price, a cooling event follows 31% of the time — an average intensity drop of -69 points. Over 90% of these cooling events result in the user regressing back to Problem Focus (TOFU), meaning the AI's answer fails to close the decision gap.
No. Only 4.4% of transactional conversations follow the classic TOFU-to-BOFU progression. In fact, 41% of users open their conversation already at BOFU — they know what they want before the first message.
Transactional conversations average just 2.92 turns. Nearly half (48%) consist of a single turn. Users in commercial mode want direct answers, not lengthy back-and-forth exchanges.
Measure your own decision closure
This analysis is based on aggregated data. With CitationLab, you get brand-specific conversation analysis across ChatGPT, Gemini and Google AI Overview.
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