Customer retention, or minimizing churn, has been a central concern for telecom companies since their inception. It remains a sizable issue, even with today’s relatively low churn rates. More than 97% of the 260+ million adults in the US have a cell phone. Given current churn rates, that means around 200,000 US wireless phone customers churn every month. Effectively, telecom companies are locked in a monthly competition to minimize their own churn while also picking up an outsized share of the customers that are leaving their competitors.

To reduce churn, telecoms need a sophisticated approach that goes beyond generalities and dives into individual customer experiences and competitive offerings. Advanced AI/ML technologies and deep analytics will unearth hidden patterns and insights to reveal the root causes of churn, enabling a more personalized strategy for customer retention.

Shifting from surface-level analysis to a comprehensive approach is critical to improving customer retention. Telecoms increasingly need to match customer-specific experience problems with key contributing factors, identify the unique pain points and challenges faced by individual customers, and proactively address those concerns before they escalate to the point of departure. Adopting a larger scale AI/ML iterative modeling approach and incorporating a diverse set of metrics allows for a more accurate and predictive model for customer retention.

The Overlooked Importance of Customer-Level Analytics

While macro-level customer experience metrics are valuable, it is essential to incorporate the often-overlooked customer-level analytics. Datapoints that provide profound insights into the factors driving churn include individual customer interactions with customer service or self-help on the website, device and usage preferences, data and voice experiences, localized competitive environments, service optionality, device age, friends and family moving to a different provider, and other key causality drivers will.

Telecoms also need to rethink how they track the relationship between network performance and individual customer experiences. Companies do an excellent job of monitoring their network performance, including identifying and tracking issues that impact multiple customers. However, an individual customer may be having a horrible experience on a network component operating within specifications, which is a good reminder of the importance of understanding the outliers across various metrics. Theses service outliers might be caused by issues related to the customer’s device, their location, the apps enabled on their phone, or a previously undetected localized network issue.

Starting with a historical monthly dataset and leveraging an iterative multi-variable linear regression and machine learning model, enterprises can explore different combinations of variables to determine the combination that most reliably predicts which customers will churn. The proper modeling and cross-function inputs and interactions will include seamless and secure access, visualization, and collaboration across teams, workloads, clouds, and datasets. This allows telecoms to derive actionable campaigns and reduce churn. It will always take trial and error to model the historical dataset and metrics and determine which outliers (and how many) create the final impetus for customers to take their business elsewhere.

The Cost Advantage of Customer Retention

Retaining existing customers is not only a strategic imperative but also a cost-effective strategy. Acquiring new customers is resource-intensive, making customer retention a financially prudent approach. Allocating resources efficiently to keep existing customers contributes to long-term profitability. Prioritizing customer satisfaction and addressing their concerns fosters loyalty and positive word-of-mouth recommendations and contributes to sustained growth.

Using an iterative modeling approach, we worked with a client to accurately identify approximately 70% of churning customers in the historical dataset. Those findings allowed the client to proactively identify and target at-risk customers to generate an incremental $60M in annual revenue.

While outcomes like this are impressive in and of themselves, data-driven customer retention will be further supercharged as GenAI capabilities mature. Effective utilization of GenAI will enhance customer interactions by providing personalized, context-aware responses, leading to improved customer satisfaction and loyalty.

The Customer Retention Imperative

The traditional approach to customer retention will need to be revised in today's competitive landscape. The telecom industry needs a data-driven approach, combining deep analytics, AI/ML modeling, and diverse metrics to gain comprehensive insights into customer churn. By proactively identifying and addressing customer concerns, telecom companies can enhance satisfaction, foster loyalty, and drive success.

Retaining existing customers is more than just a strategic move; it is a financially prudent one. It’s critical to prioritize customer retention and leverage the power of deep analytics and AI/ML modeling for lasting success in the telecom sector.

About the Authors

Bijoy Chelora
Global Lead for Platforms and Solutions – Data, Analytics and Intelligence
Bijoy leverages nearly 20 years of industry expertise in the services and product sectors to carve out innovative new solutions and offerings that span technical and business domains. He now leads GTM for platforms and solutions within Wipro’s data analytics and intelligence practice. He has played critical roles in winning large transformational deals and ensuring their success.

Mike Ayres
Partner, Technology Infrastructure and Mobility, CAS Group

Mike is a seasoned telecom executive with broad experience across a variety of operational areas. Throughout his career, he has solved problems by digging into the operational drivers that reveal innovative new solutions.