Predict customer churn daily using ML or LLM models and notify via Slack/email
Daily churn analysis at 2 AM
Fetch active customer profiles
api.example.com
Fetch customer activity logs (30 days)
api.example.com
Fetch transaction history (90 days)
api.example.com
Merge customer and activity data
Engineer behavioral features
Call ML churn prediction model
ml-api.example.com
Score and classify churn risk
Route by risk level
Create retention campaign task
api.example.com
Store churn predictions in database
postgres
Generate churn analytics report
Filter at-risk customers
filter
Post churn alert to Slack
Email report to customer success team
smtp
Log analysis completion
Press enter or space to select a node. You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.
Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.