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Retain / Save

Churn-risk early-warning outreach

Most customers who leave never tell you they're leaving. The agent reads the early signals and opens the conversation while there is still something to save.

Get aheadNext best thingRisk triggerBehavioral triggerPushEmailChatIn-appSubscriptionHealth & Telehealth

What it does

By the time a customer hits the cancel button, the decision is mostly made. The real churn happens earlier and quieter: logins taper off, the feature that hooked them goes untouched, tracking lapses, or a support chat carries the tell-tale language of someone comparing alternatives. Most of these customers never contact support at all; they simply stop showing up, and the first signal the business sees is the cancellation. The agent watches for the wobble instead — engagement decay, churn language in live conversations, frustration and sentiment shifts — and reaches out at that moment to understand what broke and fix it, before cancel intent ever forms.

How it works

  1. 1
    Trigger. a churn-risk signal fires. It can be behavioral (an engagement-decay rule such as three days without tracking after a steady streak, a key feature untouched, a missed milestone), conversational (the customer says "found a cheaper option" mid-chat), or a sentiment score crossing the frustration threshold.
  2. 2
    Decision. the agent weighs the signal against the customer's memory: plan, tenure, past issues, what they originally came for, and recent context that explains the dip. It picks the lightest intervention that addresses the likely cause, and a judge gates every unprompted message for relevance, tone, and policy before anything sends.
  3. 3
    Action. for an in-session signal the agent intervenes in the conversation itself; for a decay signal it reaches out on the customer's channel (push, email, or in-app) with a specific, non-generic opener tied to what changed. The aim is a diagnostic conversation, not a discount blast.
  4. 4
    Follow-through. the customer's answer is written back to memory and to the lifecycle flag; unresolved frustration escalates to a human with full context. If the customer re-engages on their own before the message goes out, the stale outreach is cancelled. Saves are measured against a holdout group, so the lift reported is real.
Example
AgentYou'd been tracking every evening for weeks, and then it went quiet. Anything getting in the way? If the evening reminders were landing at a bad time, I can move them.
CustomerHonestly I've been looking at a cheaper app.
AgentFair. Before you decide: most of your plan has gone untouched — you really only use the sleep program. I can switch you to the lighter plan, which costs less and keeps your history. Want me to set that up?

Configuration

How the agent is wired for this use case.

Triggera churn-risk signal — a behavioral decay rule, churn language in a live conversation, or a sentiment score crossing the frustration threshold — sourced from the product event stream and conversation transcripts
Tools & actions
  • Product analytics / event stream · read engagement and usage events and evaluate the churn-risk score or decay rules
  • Messaging channel · intervene in-session, or reach out on push, email, or in-app with a specific opener
  • Billing platform · read subscription state and apply the matched fix, such as a switch to a lighter plan
  • CRM · write the answer back to the lifecycle flag
  • Memory store · read plan, tenure, original goal, and past issues; record the outcome
Autonomysignal evaluation and the lightest matched intervention run unattended behind judge gating for relevance, tone, and policy; plan changes are money-moving and confirmed by the customer within a bounded billing-action policy
Channelspush · email · chat · in-app
Escalationunresolved frustration escalates to a human with full context

What you need

The inputs this use case runs on. Your channels stay yours; the agent supplies the judgment.

Signals

engagement and usage events (logins, sessions, key-feature use, tracking entries), churn-risk score or decay rules, conversation transcripts for churn-language and sentiment detection

Data

customer memory (plan, tenure, original goal, past issues), subscription state, channel consent

Guardrails

judge gating on every unprompted message; frequency caps per customer; stale-outreach cancellation on re-engagement; human escalation on unresolved frustration; holdout assignment for honest measurement

Metrics it moves

  • churndown, by intervening at the wobble moment instead of the cancel screen
  • save-rateup, measured against a holdout so saves that would have happened anyway don't count
  • ltvup, as rescued accounts keep their history and tenure instead of restarting from zero elsewhere

Related use cases

Retain / Save

Cancel-flow save conversation

the downstream save when the early warning was missed

Retain / Save

Drop-off-window cadence coach (GLP-1)

the same decay pattern in a clinical program

Prevent / Care

Preventive CX — reach out before the user arrives angry

the get-ahead reflex applied to service trouble

See it on your own customer journey

Bring one drop-off, one churn cliff, or one silent segment. We will show you what a proactive agent with memory and judgment does with it.

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