Churn Prediction Platform Achieving 88% Accuracy Across 30M Subscribers for a Mobile Operator
A mobile operator losing subscribers to competitors needed better churn prediction. We built a Databricks customer analytics platform — achieving 88% churn prediction accuracy across 30M subscribers.
About the Client
The Challenge
A mobile operator losing subscribers to competitors needed better churn prediction. We built a Databricks customer analytics platform — achieving 88% churn prediction accuracy across 30M subscribers. The organization had reached an inflection point — network monitoring relied on legacy NMS tools with high alert fatigue — operators couldn't distinguish genuine issues from noise. Customer churn was analyzed quarterly in spreadsheets, not predicted in real-time. BSS/OSS systems designed a decade ago couldn't support modern service launches.
FCC regulations, CPNI privacy requirements, and carrier-grade SLA standards added complexity that generalist technology vendors consistently underestimated. Previous initiatives had stalled because the technology partner didn't understand these constraints — delivering solutions that technically worked but failed compliance review or didn't fit operational workflows.
The executive sponsor set clear expectations: measurable impact within one quarter. They needed a partner with both data engineering expertise and telecommunication domain knowledge — someone who could deliver quickly without creating compliance risk or workflow disruption.
Our Approach
We designed a phased approach optimized for speed-to-value:
Assessment & Architecture (Weeks 1-2)
Cataloged source systems, volumes, and FCC regulations, CPNI privacy requirements, and carrier-grade SLA standards requirements. Designed data platform with medallion architecture and governance.
Ingestion Pipelines (Weeks 2-5)
Built automated data pipelines with error handling, retry logic, and lineage tracking.
Transformation & Quality (Weeks 3-7)
Data quality checks at each medallion layer. Industry-specific business logic and domain models in Gold layer.
Analytics & Consumption (Weeks 5-9)
Connected to Power BI semantic models with row-level security and certified datasets for telecommunication users.
Governance & Handoff (Weeks 7-10)
Deployed governance framework with classification, lineage, and access policies. Trained internal team.
Solution Architecture
Platform: Lakehouse with medallion layers and governance framework
Ingestion: Automated pipelines with lineage tracking
Consumption: Power BI with RLS and certified datasets
Results
Technologies Used
Key Takeaways
If your organization is facing a similar challenge, here's what we learned:
Telecommunication domain expertise eliminated the learning curve. Understanding FCC regulations, CPNI privacy requirements, and carrier-grade SLA standards and operational workflows from day one meant we delivered in 8-12 weeks — not the 6-9 months that generalist vendors typically require for telecommunication projects.
Compliance-first design prevents costly rework. We built FCC regulations, CPNI privacy requirements, and carrier-grade SLA standards requirements into the architecture from week 1 — not as a post-deployment audit fix. Every design decision was validated against regulatory requirements before implementation.
User adoption requires workflow-native design. Telecommunication professionals won't change how they work to use a new tool. We designed the solution to integrate into existing workflows — the system met users where they already worked, achieving 80%+ adoption within 30 days.
Measurable outcomes sustain executive support. We defined success metrics before building anything. When the sponsor presented quantified results to leadership within one quarter, budget for the next phase was approved immediately.
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We deliver data engineering solutions for telecommunication organizations — typically within 8-12 weeks.