OmniSci Team
Oct 12, 2020

Strategies for Reducing Churn Rate in the Telecom Industry

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Strategies for Reducing Churn Rate in Telecom Industry

Customer churn in the telecom industry poses one of the most significant risks to loss of revenue. The average churn rate in the telecom industry is approximately 1.9% per month across the four major carriers, but could rise as high as 67% annually for prepaid services*. Since the cost of acquiring new customers is up to 25 times higher than the cost of retaining them, fostering customer loyalty is key. 

*Arthur Hughes. “Churn reduction in the telecom industry.” DB Marketing, Database Marketing Institute, dbmarketing.com/2010/03/churn-reduction-in-the-telecom-industry/. Accessed 22 Jan. 2021.

With low switching costs and an abundance of alternative providers, customer satisfaction is the most effective means of reducing customer churn in telecom. And the most effective means of improving the customer experience is fully taking advantage of the vast streams of rich telecom customer data. 

In the interest of profitability and competitiveness, it is crucial for telecom marketers, customer service managers, analysts, data scientists, and executives to understand how to leverage the power of big data analytics to anticipate, identify, and rectify causes of high churn rates in telecom. 

Understanding how to calculate churn rate is simple. Apply the following churn rate formula: 

churn rate = ((users at beginning of period - users at end of period) / users at beginning of period) x 100

Some proactive, data-driven strategies for determining how to reduce customer churn rates in telecom include network optimization, customer complaint analytics, and CLV management. Read on to see how these strategies can help reduce your customer churn rate.

Network Optimization 

A key factor impacting the churn rate in the telecom industry is network reliability. One of the most common issues reported by customers is a slow, down, or spotty network connection. Despite a massive increase in network data and usage, customers still expect reliability and lightning speeds. 

Continuous telecom network traffic monitoring and measuring telecom network performance are essential, proactive measures that help telecom network analysts identify sources that affect network bandwidth and latency, such as poor infrastructure, poor security, incompatible or badly designed applications, and inadequate hardware. 

Accelerated network infrastructure monitoring tools enable telecom network analysts to instantly query and visualize billions of interactions and continuous flows of sensor data packets across entire telecommunications networks. With this data, analysts can rapidly discover actionable intelligence concerning network performance, signal strength, and download speeds, whereby they can optimize the network infrastructure and improve the overall quality of the telecom customer experience. 

Edge networking, a solution employed in 5G optimization, functions to conserve network resources by offloading network traffic, thereby reducing the strain on data centers, and reducing network latency and bottlenecks that would affect customer retention. 

Managing Customer Lifetime Value 

An effective strategy for reducing customer churn rates in telecom is anticipating future issues with predictive modeling. Applying churn rate predictive modeling to individual customers’ transaction data aggregated from data warehouses helps telecom providers predict how much value a telecom customer may add throughout their lifecycle, who is at most risk of leaving, and which customers to prioritize. 

Churn prediction models examine current and historical datasets for underlying patterns and calculate the probability of an outcome. Advanced algorithms can identify previously hidden variables and combinations of variables related to telecom customer behavior that correlates with customer churn. Providers can then use this information on an accelerated analytics platform, where they can rapidly process and visualize entire customer datasets, identify and better understand trends in their customers’ behavior, and ultimately increase the percentage of subscribers who remain happy and loyal. 

Churn rate prediction models also give providers insight into how much money each telecom customer is spending on a service, how often they are using a service, and when. These enormous streams of data regarding customer accounts, connectivity, demographics, usage, and geographies help telecom companies create micro-segmentations of customers, in which providers can develop a more targeted, personalized understanding of telecom customer behavior, such as insight into when customers are using more or less of a service, when to offer a more enticing package, which services to offer in the package, and how much their monthly bill should be. 

Customer Complaint Analytics 

The leading cause for customer churn in telecom is poor customer service experiences. Long wait times, ineffective self-service options, incompetent service agents, unresolved issues, and convoluted billing all contribute to customer dissatisfaction. 

Frustrated customers share negative sentiments regarding customer service experiences with friends and family, but also across social media channels, online forums, and even with the media. While this may sound negative, telecom customer complaints are in fact a valuable, strategic source of insight. An effective customer complaint management program includes complaint intake, complaint categorization, complaint routing and resolution, communication and tracking, and reporting. 

A combination of automated text mining tools, Natural Language Processing techniques, advanced customer analytics, and text mining algorithms can be used to apply sentiment analysis to public text, which structures input text, identifies patterns within the structured data, and interprets the results. This is useful in gauging public opinion regarding telecom services, which helps providers flag service issues that may lead to increased customer churn rate.

Advanced data science platforms provide exploratory data science dashboards and modeling capabilities that enable telecom analysts to access interactive spatiotemporal visualizations and deep behavioral analytics. These can identify and track telecom customer behavior patterns and sentiments, establish relationships, identify trends, sort and categorize textual datasets, and isolate and analyze only the most useful information regarding customer complaints.

Big Data Analytics Platforms for Reducing Churn

Harnessing the power and realizing the full potential of big data analytics for customer churn analysis is the key to reducing customer churn rates in telecom. A combination of accelerated insights, data accessibility, location intelligence, and limitless exploration on a GPU-accelerated converged analytics platform enables telecom providers to instantly access, visualize, analyze, and interact with telecom customer data. 

From this telecom customer data, analysts can identify trends and patterns, visualize previously unknown variable relationships, draw valuable conclusions regarding customer experiences, and drill down to the root cause of churn for each individual customer, which will lead to better, proactive, data-driven business decisions, a more satisfied customer base, and a reduced churn rate in telecom industry. 


OmniSci (formerly MapD) is the pioneer in GPU-accelerated analytics, redefining speed and scale in big data querying and visualization. The OmniSci platform is used to find insights in data beyond the limits of mainstream analytics tools. Originating from research at MIT, OmniSci is a technology breakthrough, harnessing the massive parallel computing of GPUs for data analytics.