- Churn prediction scores every wireless and broadband subscriber for risk of leaving, with reason codes that show what is driving each score.
- Churn risk combined with expected lifetime value tells you which subscribers are worth a retention offer and which need a network fix or a care callback instead.
- H2O Feature Store keeps the features your churn and offer models share, so training and scoring use the same definitions.
- The models run in four deployment options: on-premises, air-gapped, private cloud, and cloud, so subscriber data stays in your environment.
A churn list nobody acts on
You probably have a churn model already. It produces a list each month, and the list goes to a team that cannot call everyone on it. The subscribers who leave are often the ones who were flagged, and the ones who stay were sometimes given a discount they did not need.
Churn in telecom has many causes: a contract ending, a competitor's device offer, repeated dropped calls, a billing dispute that went badly. A score without a reason leaves your team guessing which action to take, so every at-risk subscriber gets the same offer.
Timing decides the outcome. By the time a subscriber calls to port out, the decision is made. The signal was there weeks earlier in usage, network experience, and care contacts.
How telecom churn prediction works
Your team builds explainable churn models on your own subscriber data, ranks subscribers by risk and value, and sends the right action to the channel that can act on it.
Step 1: start from the subscriber record
Join usage, billing, plan and device history, network quality, and care contacts, including the reasons behind each care call.
Step 2: build features once and reuse them
H2O Feature Store keeps the features your churn and offer models share, so training and scoring use the same definitions.
Step 3: predict who is likely to leave
H2O Driverless AI trains classification models for churn in each product line, with automatic feature engineering on trends such as falling usage or rising dropped-call counts.
Step 4: see why, subscriber by subscriber
Reason codes built on Shapley values show what drives each score, so your team knows whether the fix is an offer, a device, a network ticket, or a care callback.
Step 5: prioritize by value and trigger the action
Combine churn risk with expected lifetime value, then send each subscriber's action to your care, retail, or campaign systems. Enterprise h2oGPTe drafts the outreach message from the reason for your team to approve.
Step 6: measure against what you run today
Compare each new model with your current churn model on a hold-out group before it goes live, and track model drift as behavior changes.
Keeping your channels in place
Scores feed the care, retail, and campaign systems you already run. If you already have a churn model, you do not need to replace it: build a challenger model on your own data, and keep whichever model keeps more subscribers.