The Conversation Depth Benchmark: How Deep Do Users Actually Go?
Every AI product team I’ve talked to tracks conversation depth. Turn count per session is one of those metrics that feels good to measure because it’s easy to pull and seems inherently meaningful. More turns means the user is more engaged, right?
Not exactly.
I’ve seen an 8-turn conversation where a user got profound value from a coding assistant and shipped a feature they were stuck on for two days. And I’ve seen an 8-turn conversation where someone tried five different ways to ask a customer support bot the same question about a refund and gave up. Same number on your dashboard. Completely opposite outcomes.
The conversation depth metric isn’t bad. Its just wildly misread. And the teams who misread it build in the wrong direction.
^ product teams looking at their “healthy” average turn count without segmenting by resolution
What “average turns per conversation” actually tells you (hint: less than you think)
The raw number is almost useless without context. Average turns per conversation is a distribution, not a point. The average could be 7, but if half your conversations end at turn 2 (abandoned) and the other half run to turn 12 (stuck users), the average is hiding the most important signal in your product.
Depth without outcome context is noise. Depth with outcome context is one of the most predictive signals you have.
The key segmentation questions nobody asks: what is the average depth of sessions that resolved successfully? What is the average depth of sessions that were abandoned? Are those numbers the same? If they are, depth tells you nothing. If abandoned sessions consistently run deeper than resolved ones, you’ve just found where your AI is failing.
Benchmarks by product type (and why “more” isn’t always better)
These are patterns from across the industry and conversations with teams building in this space, not gospel truth. Take them as calibration, not targets.
Customer support and task bots: 2-4 turns is healthy. 8+ turns is a problem.
This one is counterintuitive until you think about it. The entire value proposition of a support bot is resolution speed. Users do not want a conversation, they want an answer. If your support bot is regularly hitting 8+ turns per session, something in your intent recognition or answer quality is broken. Long conversations in task-oriented AI arent a sign of engagement, theyre a sign of friction. The best support bot session is the one where the user never has to rephrase.
AI coding assistants: 5-10 turns for a focused task.
Coding assistants are context-rich by nature. A developer working through a gnarly bug or architecting something new will naturally go deeper. But the signal isnt the turn count, its whether depth correlates with task completion. A 10-turn session that ends with “okay it works, thanks” is perfect. A 10-turn session where the dev pastes the same error message three times is your AI failing to move the problem forward. The distinction is whether each turn is advancing the task or retreading the same ground.
AI companions: 10-20+ turns per session is healthy. Circular depth is not.
Character.AI users reportedly spend over 17 minutes per session on average, and active users hit sessions dramatically longer than that. High depth in companion apps generally is positive. But watch for what I call circular depth: the same topic, same emotional beat, same style of exchange repeating across turns without any evolution. A user asking their companion about their anxiety in turn 4 and turn 14 in almost the same way isnt deepening a relationship, its looping. That pattern predicts churn in companion apps more reliably than almost anything else.
AI tutors: 6-12 turns, but the direction of questions matters more than the count.
The single best signal in AI tutoring is whether the questions are getting harder. A student asking about Python loops at turn 3 and asking about generator expressions and lazy evaluation at turn 11 is learning. A student asking about the same concept three different ways is struggling, and the tutor isnt bridging the gap. Research on AI tutoring shows session length increases dramatically when richer interaction is enabled, but length alone doesnt predict learning outcomes. The cognitive depth of what was being asked at the end of the session predicts outcomes.
The 3 types of depth (and how to tell them apart)
Not all deep conversations are equal. Once you’ve accepted that, you need a framework for what you’re actually looking at.
Productive depth. Each turn advances the user toward their goal. Question, answer, clarification, deeper question. The AI is doing its job. This is what you want to optimize for.
Stuck depth. User repeating or rephrasing the same intent. The AI isnt resolving it. The user keeps trying. High turn count, low satisfaction, high abandonment. This is where your intent resolution rate goes to die. When we look at session traces on Agnost for products reporting high average turn counts, stuck depth is almost always the culprit. It looks like engagement on the surface. It isnt.
Exploratory depth. User meanders without a specific goal, using the AI as a thinking partner, a creative sounding board, a companion. Neither productive nor stuck. It’s genuinely open-ended. Most common in companion apps and AI tutors when the relationship is working. The signal here is novelty: is the user exploring new territory each session, or circling back to the same territory repeatedly?
^ me every time a team tells me their high turn count proves strong engagement, before we look at the resolved vs abandoned breakdown
How to actually segment this correctly
Stop looking at average turns per conversation.
Start looking at these cuts instead.
Depth of resolved sessions vs depth of abandoned sessions. If your abandoned sessions consistently run deeper than your resolved ones, your AI is failing users who are trying hardest to get value. Fix those intents first. This is probably the single highest-leverage analysis a product team can run on their conversational data.
Depth by user tenure. New users who have shorter, successful sessions signal your onboarding is working. Retained power users who go deeper over time means your product is genuinely getting more valuable to them. If retained users arent going deeper, your product has a ceiling problem.
Depth by intent category. Not all intents are equal. Some are inherently short (fact lookups, quick commands). Some are inherently long (debugging complex problems, working through emotional topics). Anomalously deep sessions in typically short-turn categories are your broken intent list. That’s where your model is underperforming on the intents users care about most.
The metric worth building a dashboard around: average depth of successfully resolved conversations, tracked over time. As your AI improves, this number should trend down for task-oriented products (faster resolution) and up for companion and creative products (deeper engagement unlocked sooner).
What the best teams actually use depth to answer
Teams who’ve figured this out stop asking “are our conversations deep enough?” and start asking three specific questions.
“Is our AI getting better at resolving intents quickly?” For support and task bots, average depth in resolved sessions should decrease over time as your model and prompts improve. If its flat or rising, your AI isnt getting smarter at understanding user intent, its just being used more.
“Are our power users getting more value?” For companion and creative tools, depth for your top retention cohort should increase quarter over quarter. If retained users plateau in session depth around month two, you’ve hit a product ceiling. They’ve found the limits of what the AI can do with them.
“Which intent categories are breaking?” Run depth by intent category, filtered to abandoned sessions only. The categories with the highest abandoned-session depth are your broken intents. Thats the list you hand your AI team on Monday morning.
The depth pattern that actually predicts retention
Here’s the one that keeps coming up across different product categories.
New users who complete a genuinely deep first conversation — call it 5+ turns, ending in resolution — within their first three sessions retain at significantly higher rates 30 days later.
Think of it as your depth activation threshold. These users aren’t just using the product. They’re experiencing what the product is capable of when it works. That first “oh wow, it actually got me” moment almost always happens in a multi-turn exchange where the AI showed it could hold context, understand nuance, and deliver something the user couldnt have gotten in a single shot.
^ the PM who finds their depth activation threshold and rebuilds onboarding around it
The playbook from here is straightforward. Find your depth activation threshold by segmenting D30 retained users and looking at what their early sessions looked like. Then build everything in your onboarding to engineer that first deep, resolved session as early as possible. Reduce the friction to getting there. Surface the prompts that naturally invite depth. Remove the paths that lead to shallow, unresolved early sessions.
This is one of the things Agnost was built to make fast to find. Segmenting conversation depth by outcome, by user cohort, and by session number is the kind of analysis that takes a data team days if you’re building it in SQL. With Agnost, teams pull this analysis in an afternoon and rebuild their onboarding the same week. If you’re tired of flying blind on what your conversations are actually doing, it’s worth a look.
Wrapping it up
Conversation depth is real signal. But raw average turns per session is a lazy proxy for it. The teams building durable AI products have learned to ask why conversations are deep, not just whether they are.
If your support bot averages 9 turns, thats almost certainly a problem. If your AI companion’s power users average 18 turns per session and that number is growing, thats a product working. The same number on a different product tells a completely different story.
Find your depth activation threshold. Segment resolved vs abandoned. Track depth by intent category. Stop looking at the raw average like it means something on its own.
The teams who crack this move from “our AI is being used” to “our AI is working.” Those are very different statements, and they have very different business outcomes.
TL;DR: High conversation depth is not inherently good or bad. Segment by resolution outcome, user tenure, and intent category. Find your depth activation threshold, then build onboarding around engineering that first deep, successful session as early as possible.
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