The telecommunications industry is a particularly competitive one, hallmarked by rapid technological advances and changing customer demand. Even 10 years ago, many of us evaluated our telecommunications providers in terms of the cost of cell phone minutes and network call coverage. In just a decade these priorities shifted — now we increasingly expect high-speed video, unlimited data and the latest devices.

Telecoms must meet and exceed these demands for the sake of customer retention, let alone growth. A company’s approach to data analytics can play a crucial role in how well it’s able to build its customer base, optimize its offerings and remain relevant in a changing market.

Here are four potential data analytics use cases for telecoms.

Reducing Customer Churn

Customer churn is downright costly. According to data from Bell Canada Enterprises and TELUS, it costs nearly 50 times less to retain an existing customer than it does to acquire a brand new one. As Computer Weekly notes about these findings, it would then seem practical for a telecom to focus primarily on giving current customers a reason to stay, rather than focusing primarily on drawing new ones away from the competition.

Thus, it pays to ask what constitutes a positive experience for telecoms customers today? Data from customer feedback and subscriber behavior can inform targeted actions to reduce churn by alleviating customer pain points and prioritizing what customers believe to be their strengths.

Optimizing Billing for Customers

Most of us have received at least one confusing phone or internet bill in our lives — whether it contained opaque charges, erroneous information or hidden “surprises” on pricing. It’s a frustrating experience all around, one requiring time and effort from both parties to resolve.

Forward-thinking telecoms are trying to optimize billing for customers, making it easier to understand usage and charges without requiring the help of a specialist.

British Telecom (BT) is one such innovative company that decided to embed communication analytics from ThoughtSpot directly into customer portals — giving subscribers the ability to ask questions and pull insights related to pricing and usage directly. This move allowed more than 1.2 million BT customers to analyze billing data and determine which service plans would provide the best balance between performance and cost.

Optimizing billing for customer understanding eliminates a common pain point and boosts loyalty.

Minimizing Network Outages & Downtime

It’s no simple task maintaining the telecoms infrastructure and equipment — and troubleshooting it quickly when something goes wrong. But even a few minutes or hours of downtime can be very costly for telecoms both in terms of lost business and tarnished customer perception of the brand.

In fact, research firm Heavy Reading found network outages cost mobile carriers approximately $15 billion annually, or 1.5 percent of their annual revenue on average.

Mining data helps companies understand the underlying reasons for downtime and predict incidents before they occur. AI-powered analytics can detect patterns, anomalies and relationships buried deep within data, then push these findings to human decision-makers who can take action.

Refine Product Offerings & Pricing

With access to self-service data analytics, teams can quickly pull insights related to product performance and profitability — breaking down this information by factors like location, customer segment, timeframe, marketing campaign and many more. This ability to refine offerings helps telecoms stay relevant in a changing market, appeal to potential customers and satisfy existing subscribers.

Data analytics has many potential use cases in the telecoms industry, including but not limited to cutting down customer churn, optimizing the billing process, minimizing network downtime and continually refining the array of products and plans offered to customers.