Hello everyone, welcome back to our video series on churn prediction and prevention. I'm Piyush Dewan, a product marketer at Mobilewalla, a global consumer intelligence company. In previous episodes, we explored how telcos can leverage third-party data to predict churn and integrate it effectively into predictive modeling processes. In this episode, we'll dive into a real-world case study involving a telco based in Southeast Asia.


This Telco faced high churn rates and struggled to identify at-risk subscriber segments, lacking sufficient data on external lifestyle and mobility factors influencing churn decisions. By integrating third-party data from Mobilewalla, the telco developed a hybrid churn model. This model incorporated mobility insights, affluence indicators, travel behaviors, and device-based segments.


As a result, the telco improved churn prediction outcomes by up to 15%. They tailored effective retention campaigns specifically targeting younger age groups, affluent segments, and highly mobile subscribers deemed at high risk of churn. Key insights from the data revealed that iPhone users exhibited higher churn rates compared to Android users. Affluent customers, especially those frequenting high-value retail outlets, were also identified as high churn risks, likely due to higher service expectations. Leisure travelers, particularly those staying at upscale hotels, constituted 30% of all churners, indicating a need for improved roaming and travel-centric telco solutions.


This case underscores the critical role of third-party data in churn modeling, demonstrating how telco companies can gain a competitive edge by uncovering hidden churn signals and deriving actionable intelligence. To explore similar insights and benefits for your company, speak with a data expert from Mobilewalla. Let us help you acquire and utilize the right data efficiently to enhance your predictive modeling efforts. Thank you.