Why is Predictive Analytics a Must-have in the Telecom Industry?

Jun 2, 2018

telecom industry

The telecom industry is one of the fastest-growing sectors in the world. Companies in the telecommunications industry have shifted from being mere providers of infrastructure, bandwidth, and capacity to enablers of communication, information, and interaction. As technology advances, service and pricing plans evolve, and the market becomes more saturated, telecom companies face increasing competition for customers. But the good news is that there is an abundance of customer data that is available to telecom companies today. Players in the industry can get the best out of this data with the help of advanced capabilities such as predictive analytics. By using predictive analytics, companies in the telecom industry can learn more about their customers’ preferences and needs, which will eventually make them more successful in this highly competitive industry.

Speak to an analytics expert to learn more about the role of predictive analytics in telecom.

Satisfy Customer Expectations

One of the guiding principles of customer experience management is to look at how customers are engaging at every stage with the organization. This includes interactions before they sign on as customers, all the way through the end of their engagement with the company. The goal is to understand the customer’s experience and taking measures to shape it in the most positive way possible. In other words, it’s about anticipating needs and delivering services that keep customers happy, rather than reacting to problems. With the help of predictive analytics, telecom companies can accurately identify the trends in customers’ needs. This will help providers to alter their services accordingly and improve the customer experience.

Predict and Prevent Customer Churn

Did you know that certain predictive analytics software even recommends ways to reverse trends such as churn? This can be taken into account when companies in the telecom industry are devising strategies to reduce or avoid churn. For instance, Cox Communications, a leading player in the telecom industry had built predictive models that enabled them to quickly and precisely poll millions of customer observations and hundreds of variables to identify issues including the likelihood of churn. They then personalized offers across 28 regions. By acting upon the insights and recommendations, the provider was able to reduce its customer churn.

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Fraud Detection

Fraud is a key root cause of lost revenue in the telecom industry. Efficient fraud detection systems can help telcos save a significant amount of money. Fraud detection systems depend on data mining algorithms to identify and alert telcos to fraudulent customers and suspicious behavior. While data mining techniques help only in the areas of subscription fraud, it is useful to remember that there can be several methods of fraud, requiring other analytic models to aid detection. Risk management teams are the largest users of fraud management systems.

Cross-Selling and Up-Selling

Cross-selling and up-selling activities can be supported by predictive analytic in the telecom industry by tracking association rules and transaction histories. Analytics-driven cross-selling and up-selling campaigns are known to provide comparatively higher returns. By moving beyond financials, they also increase stickiness and reduce the number of contacts required for cross-selling and up-selling.

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