Case Study|Data & Analytics
Caystard built a centralized analytics platform for a telecom operator, unifying customer, network, and billing data to reduce churn prediction error by 35%.
A regional telecom operator struggled to get a unified view of customer behavior across billing, network performance, and customer service data, limiting its ability to predict and prevent churn. Caystard designed and built a centralized data analytics platform.
Customer, billing, and network data lived in separate systems maintained by different teams, making it nearly impossible to build accurate churn models or understand the real drivers behind customer attrition. Existing churn prediction tools relied only on billing history, missing critical signals from network quality and support interactions.
Caystard built a unified data lake that consolidated billing, network performance, and customer service data into a single analytics environment, then developed machine learning models that incorporated all three data sources to predict churn risk with far greater accuracy.
Caystard's data engineering team first built ETL pipelines to bring together data from the telecom's billing system, network operations center, and customer service platform into a centralized data lake, resolving inconsistencies in customer identifiers across systems along the way.
With unified data in place, Caystard's data science team developed churn prediction models that factored in network quality metrics (dropped calls, latency, outages in a customer's area) alongside billing and support interaction history — signals the previous, billing-only model had missed entirely.
The platform included a self-service analytics layer for the telecom's marketing and retention teams, allowing them to explore customer segments and churn risk factors without needing to write queries themselves. Retention campaigns targeted using the new model showed meaningfully better response rates than previous billing-only targeting.
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