Anonymised client
PilotEnergy supplier, Romania
Customers leaving in a liberalised retail market.
Churn propensity scoring with explainable drivers and a four-tier retention playbook.
Customer Intelligence
Churn & Retention
Know who is likely to leave, why, and which action to take.
01 / The problem
The problem.
In competitive retail markets, customers leave quietly. By the time a cancellation arrives, the retention window has closed.
02 / What it does
What the agent does.
- Scores every customer's churn propensity monthly
- Explains the top drivers for each customer
- Assigns a four-tier risk level
- Triggers the retention action defined in your playbook
- Measures which actions actually worked
03 / How it is built
Built on ContextLab.
Ontology
Customer, contract, consumption point, interaction
Process
Retention playbook as an explicit workflow
Formal Knowledge
Offer rules, eligibility, regulatory constraints
AI Technologies
Gradient boosting with SHAP explanations
04 / Results
Typical KPIs we measure.
- Churn rate in treated segments
- Retention offer acceptance
- Model precision at top decile
05 / Integration
Integrations and deployment.
CRM · Billing · Contact centre
On-premise or private EU cloud
06 / Related case
Where it runs.
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View agent Let's look at one process together.
Bring one process that matters. We'll show you what it takes to make it AI-ready.