# Improve onboarding, see where users drop off · ClosedLoop AI

> Activation drop-offs surface with quotes, accounts named, and the one step worth fixing first. Stop guessing where new users stall.

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[Skip to main content](#main-content)Outcome

# Find where new users quietly drop off .


**Activation dips and nobody knows which step.**ClosedLoop AI names the leverage node: the one step that, fixed, resolves most downstream friction.

Every onboarding insight, from activation tickets to in-app chat to first-week escalations, is read and traced into a causal chain, so you fix the cause instead of the symptoms.
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Causal chain Onboarding Step 1 Step 2 · leverage node Step 3 Step 4 Fix the leverage node and the cascade resolves.
Fills from your own new-user conversations: which step causes the next, and which one to fix first.
The problem

## Where onboarding actually stalls.


Four patterns we hear from every team grappling with this outcome.
P-01
You have a number, not a reason

Activation drops quarter over quarter. You don't know which step. You don't know whether it's nine customers or ninety.
P-02
Quotes are scattered

CS hears one version on chat. Support hears another in tickets. The new-user interviews you ran last month covered something else entirely.
P-03
No leverage node

You ship onboarding improvements every quarter. Some help. Most don't. Without knowing which step causes the most downstream pain, you optimize randomly.
P-04
Drop-offs are silent

The new user gives up. They don't file a ticket. They don't reply to the welcome email. You only see them again when they don't renew, if they had a contract at all.
How it works

## From scattered conversations to a Monday-morning answer.


Same source data as the rest of the platform. A different surface for this outcome.


- 01

#### Read every onboarding insight


Activation tickets, in-app chat, welcome-email replies, first-week support escalations. Every "I can't" clustered into a theme.
- 02

#### Map the causal chain


The agent traces which step causes the next. Labels unclear on step one leads to skipped fields on step two, an empty dashboard on step three, frustration on step four.
- 03

#### Find the leverage node


One step in the chain accounts for the majority of downstream pain. Fix that one and the cascade resolves on its own. The brief tells you which.
- 04

#### Close the loop with new users


When you ship the fix, Ship-notify reaches out to the customers who stalled at that step. Dormant trials get a real reason to come back.
Features for this outcome

## The three you'll live in.


You don't need every feature on day one. These three carry the outcome.
[Any time Ask anything
"Where are new users dropping off?" A cited answer, with the quotes behind it.
Read more](https://closedloop.sh/features/ask-anything)[Monday Weekly brief
The Monday brief surfaces drop-off spikes the moment they emerge.
Read more](https://closedloop.sh/features/weekly-brief)[Before you ship Synthetic customer
Test a new onboarding flow against a stalled-user persona at 11pm.
Read more](https://closedloop.sh/features/synthetic-customer)Proof

## Proof gets measured, never manufactured.


Two revisions are open for this page. Each lands here only when it clears the standard below: real, attributable, approved. An empty slot beats a manufactured one.
Rev A · Activation metric In verification
An attributed activation lift after fixing a named leverage node, from a named deployment. Real and recomputable, or nothing.
Accepts when Attributed to a named deployment and independently recomputable. Rev B · Customer quote Awaiting approval
A verbatim quote from a team that found its drop-off step, with a real name, title, and company. Approved, or nothing.
Accepts when Named person, title, company, and written consent on file.

## See it work on your data this week.

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## More Information

- Website: https://closedloop.sh
- Documentation: /docs
- Pricing: https://closedloop.sh/pricing
- Contact: https://closedloop.sh/contact