4 Reasons the Telecommunications Industry Needs Predictive Analytics

2 min read

Telecommunications is the backbone of the global economy—over the years, the telecom industry has evolved from being a provider of bandwidth, infrastructure, and capacity to being an enabler of information, communication, and interaction. As technology progresses, service quality improves, and prices decrease, the market also sees heightened saturation and competition. Therefore, telecom companies must explore new avenues—such as the abundant customer data available to them.

Telcos can get the best out of their customer information databases by leveraging predictive analytics. Predictive analytics software solutions provide valuable insights that allow companies to take data-driven decisions. The knowledge of customer needs and preferences gives companies in the telecom space a better understanding of market demand and allows them to operate more efficiently. These insights can also be shared with other industries to maximize profits as well as customer satisfaction.

Predictive analytics makes use of historical data to develop forecast models. By using the high-quality and historically abundant data available to telcos, these solutions can make advanced predictions about customer needs and preferences. Here are four reasons why telecom providers must consider adopting predictive analytics:

  1. To Fulfill Customer Expectations

Professionals who specialize in customer experience management often advise enterprises to measure how customers engage with them at every stage—beginning with interactions that take place even before they become customers and ending with them severing ties with the company. The goal of this process is to modify customer experience across the spectrum into something positive by anticipating demands and fulfilling them in order to keep customers happy, instead of reacting to problems as they arise. Predictive analytics solutions from companies like IBM and RapidMiner can accurately identify trends in customer interactions, allowing enterprises to alter their telecom services accordingly.

  • To Minimize Customer Churn

Cutting-edge predictive analytics solutions, such as those developed by Oracle and SAP, can provide recommendations that might help telecom companies reverse negative business trends—such as customer churn. An example of this can be found in the case study of Cox Communications, a pioneer in the telecom sector that leveraged predictive models in order to precisely and swiftly poll several million customer observations across hundreds of variables. This enabled them to identify the issues that led to churn. Cox leveraged these insights to personalize its offerings in over 25 regions, ultimately reducing customer churn.

  • To Detect Fraud

The telecommunications industry loses significant revenue to fraud each year. Efficient fraud detection solutions are the need of the hour in this space, and predictive analytics can help telcos reduce their losses. Fraud detection through predictive analytics relies on data mining algorithms to detect suspicious behavior and notify security professionals of fraudulent customers. Data mining techniques are effective in dealing with subscription fraud. Other analytic models—like those provided by SAS and Angoss—can help risk management teams detect other types of fraud as well.

  • To Make Up-selling and Cross-selling More Efficient

Predictive analytics solutions that track transaction histories and association rules can help telcos boost their up-selling and cross-selling activities. Analytics-driven up-selling and cross-selling campaigns, such as those powered by FICO or Tableau, have been shown to provide relatively higher returns by increasing stickiness and reducing the number of contacts required.

Takeaway

The telecom industry is known for being tech-savvy and having a robust technology prowess. Its customer databases are vast, and predictive analytics is just what this sector might need to disrupt the status quo, provide advanced actionable insights to other sectors and end-users, and maximize profits.

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