Customer Success Customer Retention Manager

An AI analyst that spots churn before it happens

Watches customer behavior and warns you before they leave. Scores churn risk, alerts your team with context, and tracks save outcomes.

Capabilities

What your Customer Retention Manager worker does

01

Monitors product usage drops, support ticket spikes, and NPS declines daily

02

Scores accounts on churn risk, low, medium, high, critical

03

Alerts the CS team with context on why the risk was triggered

04

Tracks save actions and outcomes to improve the playbook

05

Reports monthly on churn patterns and cohort analysis

06

Connects churn signals to specific product or service issues

Goal example

"Flag 90%+ of churning accounts at least 30 days before renewal. Save rate above 40% on flagged accounts."

That's the entire setup. No prompts. No workflows.

Differentiators

What makes this different

Multi-signal detection

Not just usage drops. Your AI analyst monitors support tickets, NPS scores, engagement patterns, billing issues, and stakeholder changes, combining them into a holistic risk score.

30-day warning

Churn signals appear weeks before cancellation. Your AI analyst catches them early enough for your team to intervene, not three days before renewal.

Actionable alerts

Not just "Account X is at risk." Every alert includes what changed, why it matters, and recommended actions based on what's worked for similar situations.

Save playbook learning

Your AI analyst tracks which save actions work, discounts, feature demos, executive calls. Over time, recommendations get smarter based on what actually prevents churn.

Same team. Different output.

Before

  • Churn surprises at renewal time
  • Risk assessment based on gut feel
  • No systematic tracking of save actions
  • No visibility into churn patterns or common causes
  • CSMs reacting to cancellation requests, not preventing them

After

  • Churning accounts flagged 30+ days before renewal
  • Data-driven risk scoring across multiple signals
  • Save actions tracked with outcome data
  • Monthly churn analysis reveals patterns and root causes
  • CSMs intervening proactively with the right approach

FAQ

Common questions

It monitors every customer account for churn signals, usage drops, support spikes, NPS declines, scores risk, and alerts your CS team with context and recommended actions. It also tracks which save actions work.

Signals typically appear 30-60 days before cancellation. Your AI analyst catches declining usage patterns, increasing support tickets, and sentiment shifts well before the customer decides to leave.

Your CSMs can mark alerts as false positives, which improves the risk model over time. The AI learns what's a real churn signal for your specific product and customer base.

They gave you a tool. We'll give you a team.

Your first Shadow Worker is ready in 30 seconds. No contracts, no workflows to build, no AI to babysit.