What data do you need to train the model?

Minimum 50–100 churned customers over 1–2 years with churn dates and their pre-churn activity. More data = higher accuracy.

Can the agent send discount emails automatically?

No. The agent only recommends (call, email, discount). You approve the action in Slack before execution. Keeps you in control of spend.

How do I know if a save attempt worked?

Agent tracks: did we send the discount, did we call, how did the customer respond (returned/stayed/left). Events log to CRM.

Which payment and CRM systems work?

Stripe and Chargebee are built-in. PayPal, Recurly, others via Zapier or custom integration. Works with Salesforce, HubSpot, Pipedrive.

What's the ROI on churn prevention?

Typical: save 5–15 % of at-risk customers annually. If average customer LTV is $10K, retaining 10 lost customers = $100K saved. ROI often > 5x in year one.

AI-powered churn prediction

Churn warning: find at-risk customers before they leave

The agent analyses your CRM data — purchase frequency, deal size, support tickets, logins — and flags customers with 70%+ churn probability. Posts risk scores to Slack with save-recommendations (discount, call, upsell). Catch 95 % of customers about to cancel. Save $1K–100K+ per retained customer. $19/mo.

Launch AI-powered churn prediction

366k+⭐ OpenClaw on GitHub

<5minutes to launch

Sound familiar?

What's eating your time

Customer churn feels sudden — no early signals to intercept departures

Monitoring 100+ customers manually for disengagement signals is impossible and inaccurate

Signals live scattered across CRM, payments, support — no single view of customer health

By the time you spot a warning sign, it's often too late to retain them

Capabilities

What your AI agent can do

Real-time disengagement signal detection

Agent monitors: drop in purchase frequency, long inactivity gaps, rising support complaints, late payments, low login activity — all in one feed.

Churn-risk model trained on your history

Learns from your past customer losses: which patterns predicted departure. Assigns a 0–100 churn risk score to every customer.

Automated save recommendations

When risk is high, suggests: which discount to offer, which upgrade to propose, when to call, which case study to share (for B2B).

Real-time alerts and dashboards

Slack/Telegram notifications for high-risk accounts. Weekly report: top 10 at-risk, customers saved this month, revenue retained.

Integration with win-back automation

Triggers email sequences, SMS reminders, Slack nudges. Logs actions (sent discount, made call) and outcomes (returned/left).

Works with your tools

Salesforce

HubSpot

Stripe

Slack

Gmail

How it works

Get started in a few steps

Connect CRM and payment data

Agent reads from Salesforce or HubSpot (deals, contacts, activity) and Stripe (charges, disputes, events). Encrypted and visible only to the agent.

Learn from your churn history

You mark lost customers in your CRM. Agent analyses their pre-churn journey and builds a predictive churn-risk model.

Daily monitoring and scoring

Each day, the agent rescores all customers, updates activity feeds, and recalculates save recommendations.

Alerts and action recommendations

If risk > 60%, Slack message: 'John Smith at risk (75%). Recommend 20% discount + call from Sarah'. You approve or suggest an alternative.

Report and measure impact

Weekly dashboard: customers saved, which interventions worked, lifetime value of retained cohort, win-back ROI.

FAQ

Frequently asked questions

Documented accuracy: 85–92 % for 30+ days ahead. Higher for B2B (less noise), lower for SMB with growth/seasonality.

AI Assistant for Sales Representatives customer-success-manager Automated CRM updates from sales calls 24/7 lead qualification and lead scoring

Want OpenClaw — without the DevOps?

OpenKlo is managed hosting for the original OpenClaw. Same agent, live in 3 minutes.

Create your AI agent in 3 minutes

Cancel anytime · Top models included · Upgrade anytime