Solutions / Churn signal

Churn-risk signal from comms

Every thread with every client is read for the things that precede a churn — slower replies, a champion going quiet, complaints that repeat — and the account manager is told while there is still time to act.

Customer success ML & analytics Professional services
2–4 weeks
of warning before the notice arrives
100%
of threads read, not a sample
Every signal
carries the messages behind it
Who it is for

Heads of Customer Success and account directors carrying 30+ named accounts, where losing one is a quarter-sized event and the warning signs are buried in correspondence nobody has time to reread.

Short answer

Every email, ticket and call transcript with a client is read for the patterns that come before a churn, and the account owner gets a ranked list with the actual messages attached. Risk becomes visible 2–4 weeks before the notice, across 100% of threads rather than the ones someone happened to reread.

The problem

Churn is never a surprise afterwards. It is only a surprise beforehand.

01

The signals were there, in writing

After a client leaves you reread the thread and it is obvious: the tone changed in March, the champion stopped being copied in April. Nobody saw it live because nobody rereads six months of email across thirty accounts.

02

Health scores measure the wrong thing

Logins, tickets and NPS say the account is fine right up until it is not. What actually predicts a churn is how the relationship reads, and that lives in language, not in usage counters.

03

The warning arrives with the notice

By the time a client says they are leaving, the decision was taken weeks ago and the conversation you get is a formality. Saving an account requires knowing before it is decided, not after.

How it works

From trigger to result, step by step.

01

Connect the channels

Shared mailboxes, the helpdesk, call transcripts and — where you use it — the client Slack Connect channel are read through their APIs. Nothing is copied out of the systems you already run.

02

Learn what your churns looked like

We take accounts that actually left and accounts that stayed, and derive the patterns that separated them in your business, rather than importing a generic churn model that was fitted to somebody else's clients.

03

Read every thread against them

Response latency, sentiment drift, who has gone quiet, escalation language, repeated unresolved complaints, contract and pricing questions appearing out of cycle — each account is scored daily.

04

Show the evidence, not the number

A flagged account opens onto the specific messages that moved it: the quotes, the dates, the person who stopped replying. An account manager can judge in a minute whether the machine is right.

05

Land it where the work happens

The at-risk list goes to the CRM and to a weekly digest in Slack or email, with the owner named and a suggested first move — a call, an escalation, a commercial conversation.

Before / after

What changes on the ground.

Today, by hand
×Churn is discovered when the client announces it
×Health scores are green until the day they are not
×Signals are spread across mailboxes nobody rereads
×The save attempt starts after the decision
With the automation running
›At-risk accounts surface 2–4 weeks earlier
›Risk is grounded in what the client actually wrote
›Every account is reviewed daily, not the loud ones
›The conversation starts while it can still change the outcome
What you get

Delivered, not demoed.

›A daily risk score for every named account, with reasons
›A model derived from your own won and lost accounts
›Evidence links from each signal back to the source message
›Risk written to the CRM plus a weekly digest for the team
›Documentation and a handover session — the system is yours
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

LLM (replaceable) Gmail API Microsoft 365 Zendesk Intercom Slack API HubSpot Postgres n8n
Time to production5–8 weeks
Build priceFixed quote
First stepFree mini-audit
Honest limits

When this is not the right solution.

·Under roughly 30 accounts you can read the correspondence yourself, and a manager who knows the clients will beat this. We will say so in the mini-audit rather than sell you a model.
·If most of the relationship happens on calls that are never recorded, there is little text to read. Recording and transcription come first, and that is a policy decision, not a technical one.
·Self-serve products where clients never write to you at all are a usage-signal problem, not a communications one — a different and cheaper build.

Questions we get about this one

It reads your correspondence in place, through the APIs of the systems that already hold it, and we derive the patterns from your own history of accounts that left and stayed. Nothing is sent to a third party for training, and the model can run against an endpoint you nominate if that matters to your contracts.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.