CUSTOMER RETENTION · AI ANALYTICS

Understand churn.
Protect customer value.

Explore a customer profile and estimate the likelihood of churn from service, contract and billing details. Use the result as a starting point for informed retention decisions.

✦ Random Forest model ◉ Customer profile analysis ⌁ Instant risk estimate
01

Customer profile

Household and account background

Gender
Senior Citizen
Partner
Dependents
0 72
02

Service subscriptions

Services and add-ons linked to this account

Phone Service
Multiple Lines
Internet Service
Online Security
Online Backup
Device Protection
Tech Support
Streaming TV
Streaming Movies
03

Contract & billing

Contract, payment and recurring charges

Contract
Paperless Billing
Payment Method
✦

Churn risk assessment

Run the model to assess this customer

Submit the details to view the model's classification and estimated probability. Change the profile and run it again to compare scenarios.
i

Interpreting the estimate

Use the score as a guide, not a certainty

  • Likely to churn: the model predicts the customer may leave.
  • Unlikely to churn: the model predicts the customer is less likely to leave.

Review the customer's circumstances before taking action. A prediction alone cannot explain every reason a customer might leave.