DATASET
1,000Historical customers
Known behaviour + known churn outcome
MODEL 02 · AI CHURN INTELLIGENCE
AI-POWERED CHURN INTELLIGENCE
AI-Powered Churn Risk Intelligence
A machine-learning model that learns from historical customer outcomes to identify churn patterns and prioritize at-risk accounts.
01 · LEARN FROM OLD CHURNED CUSTOMERS
From historical customers to a validated churn model
50 / 50 SPLIT
TRAIN
500Model development
Three ML approaches learn from the training customers
TEST
500Unseen validation set
Held back until the winning model is selected
MODEL COMPETITION
3 models compete
Logistic Regression
Decision Tree
Random Forest
BEST ON THIS DATASET
Random ForestThe winning algorithm depends on the dataset. Different customer data may produce a different winner.
VALIDATE
Test on 500 unseen customers
93.2%Recall
95.1%Precision
0.988ROC-AUC
02 · UNDERSTAND WHAT IS CAUSING MAJOR RISKS
Historical churn patterns
Low License Utilization + Low Process Adoption
89.5%churn
1.79×baseline lift
152 accountssupport
Low License Utilization + Low Workflow Usage Trend
89.5%churn
1.79×baseline lift
143 accountssupport
Low Workflow Usage Trend + Low Process Adoption
84.3%churn
1.69×baseline lift
153 accountssupport
These learned churn patterns carry into current-customer scoring below.
03 · DEPLOY THIS MODEL ON YOUR CURRENT CUSTOMERS
200 current customers enter the trained model
200 current accounts Random ForestRisk prioritization
CHURN RISK
18
NEW churn risk accounts found
The model narrows the portfolio to the accounts that deserve the fastest CSM validation and intervention.
Investigate emerging signals
Monitor through normal cadence
Prioritized for Customer Success review
EXPLORE THE FULL MODEL
Open Google Colab See the workflow run step-by-step in Google Colab
Make a copy and upload your customer data to find your next churn risks.