For teams starting with retention analytics

Churn Risk Foundation

Turn scattered app events and customer records into a dependable churn definition, baseline model and practical risk segments.

Typical timing: 4–6 weeks
Discuss a suitable scope
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Best suited to

Malaysian apps establishing their first predictive retention workflow

We begin by aligning the prediction target, available evidence and operational decision. The work is then shaped to the quality and depth of your data—not forced into a fixed technical package.

What your team receives

  • A business-aligned churn definition
  • Data quality and instrumentation review
  • Baseline predictive model
  • Prioritised risk segments
  • Handover workshop for your team

Responsible and explainable by default

We favour the simplest model that produces reliable, useful separation. Data access is limited to the agreed purpose, outputs are documented and business users are shown both the model's strengths and its limits. No model can guarantee that a customer will churn or that an intervention will retain them.

Begin with a fit check

Tell us about your app, customer lifecycle, existing event and account data, and the retention decisions you want to improve. We will identify feasibility, likely gaps and an appropriate next step.

Arrange a fit check →