Predict Churn Risk & Alert Team Ai Agent Workflow Blueprint

Retention Stage Customer Success Agent
Aggregates account data, analyzes churn risk with AI, alerts teams about high-risk accounts, and provides recommended intervention actions.

Who this Ai Workfow Is For

Go-To-Market Pros

You care about improving the entire lifecycle of buyer and customer journeys. And you want inspiration for areas where Ai may help.

Ai Builders

You want inspiration for real-world ways to help people as you're building your Ai product.

Pros Focused on the Retention Stage

You're focused on the Retention stage in your daily job and want to find ways for Ai to help you do more with less.

How this Ai Workflow Helps

This workflow is designed for customer success and retention teams who need to proactively identify accounts at risk of churning before they show explicit signs of leaving. By leveraging AI to analyze multiple data points, teams can prioritize their outreach efforts and intervene with the most vulnerable accounts first.

The process begins by aggregating comprehensive data for each customer account, including product usage metrics, support ticket history, NPS/CSAT scores, billing information, and renewal dates. This data is then processed through an AI model that evaluates patterns and indicators to produce a churn risk score. The model identifies concerning combinations of factors, such as declining usage paired with unresolved support issues or approaching renewal dates.

For accounts that exceed the defined risk threshold, the workflow automatically generates detailed alerts with AI-summarized risk factors and recommended next steps. These alerts are delivered to the appropriate team members via their preferred channels (email, Slack, CRM notification), enabling swift intervention. By identifying at-risk accounts early, teams can implement targeted retention strategies before customers initiate cancellation discussions.

Ai Workflow Example as Inspiration for More

Does this AI agent workflow rely too much on AI and not enough on human know-how? Or the reverse? Is it missing steps or tools?

Note that this Ai workflow is presented as inspiration for what's possible. Adjust the amount, type and quality of the data inputs. Adjust how much or how little your human team mates (or you), AI and fully autonomous agents contribute.

And test it! Learn what works and what doesn't.

Don't forget! In the end, it's not just about efficiency. It's about delivering great experiences for your customers and customers-to-be.

1. Trigger: Schedule Risk Assessment
Trigger. Initiate the churn risk assessment workflow on a regular schedule (weekly or monthly) to evaluate all active accounts.
2. Aggregate Account Data
Tool Call. Aggregate data for each account on a schedule (usage, support tickets, NPS, renewal date, etc.) and feed it into a churn risk model.
Data
Data Aggregation Salesforce HubSpot Segment
3. Evaluate Churn Risk
LLM Call. Use AI to evaluate the data and produce a churn risk score (e.g., High Risk if usage has declined significantly and multiple support issues exist).
Report
LLM OpenAI Anthropic Google Gemini
4. Alert on High-Risk Accounts
Tool Call. For accounts above the risk threshold, automatically generate an alert with an AI-generated summary and recommended next steps.
Direct Message
Notification Slack Zapier Salesforce

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