Case Study: How Household Insights Help Telecoms Keep Customers and Grow Revenue

Executive Summary

A telecom company had limited information about its subscribers and their households. First-party subscriber data alone did not show how many people lived in a household, how they used services, or which households were at risk of leaving. Mobilewalla helped enrich this data with household-level insights and predictive features. This gave the telecom a clearer picture of its subscribers, helping it retain customers, increase revenue, and target households more effectively.

Client Profile

• Industry: Telecommunications
• Region: Markets where household insights matter for retention and growth
• Challenge: Limited subscriber data; could not see household structure or usage patterns; hard to predict churn or target upsell opportunities

Key Challenges

• Subscriber-level data did not show household size or usage across multiple devices
• Difficult to identify high-value households for retention or upsell
• Hard to predict which customers were likely to leave
• Limited ability to create personalized campaigns without household insights

Solution Overview

Mobilewalla delivered a solution that:

• Combined the telecom’s subscriber data with external data to create complete household profiles
• Generated insights and features about household composition, behavior, and value
• Allowed the telecom to use these insights to improve customer retention, target high-value households, and increase revenue

Implementation Details

• Data Sources: First-party subscriber data plus Mobilewalla’s external household and behavioral data
• Feature Building: Creating simple measures to understand household structure, usage patterns, and likelihood to leave or upgrade
• Modeling: Developing predictive models to identify valuable households and those at risk of churn
• Integration: Adding model outputs into marketing and customer management systems so insights could guide campaigns and decisions

Results & Impact

• Better identification of high-value households for upsell and cross-sell, boosting revenue
• Reduced churn by targeting at-risk households with personalized offers
• More efficient acquisition of new subscribers by focusing on households likely to subscribe or upgrade
• Improved customer segmentation for smarter marketing and retention strategies

Key Learnings & Takeaways

• Household-level insights give a more complete view of subscribers than first-party data alone
• Simple predictive features about household size, behavior, and value help improve retention and targeting
• Integrating enriched data into marketing and customer management can increase revenue and reduce churn

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