Top 3 Use Cases For Big Data Analytics in Telecom

The telecommunications industry today is more competitive than ever, with companies doing all they can to acquire customers and turn them into loyal subscribers. Increasingly, what sets organizations apart from competitors is their ability to use stored data to drive effective decision-making.

Does your organization currently have the tools to conduct a telecommunications industry analysis? Do your employees have the ability to analyze company data, produce business intelligence reports and share their findings easily? How your business approaches big data analytics makes or breaks the insights you receive—which are key in helping you compete in such a fast-paced industry.

Here are the top three use cases for big data analytics in the telecom industry.

  1. Provides Insight into What Customers Want
    The driving force of any telecommunications business is its product offerings. This underscores how important it is to have a firm grasp on the pricing plans and models you sell. What are people buying? What are they willing to pay more for? Where are additional opportunities to upsell?

    Search-driven analytics empower employees to query data directly, as simply as using any online search engine. Queries like “[revenue growth] [by plan name] [model] [last 30 days]” take just seconds to crunch—returning automatically in the form of a best-fit chart.

  2. Drives More Targeted, Effective Marketing
    Marketing teams can use telecommunications analytics to segment customers by demographics, usage data and social media posts. This helps them optimize campaign performance. An example of a marketing-based query would be something like “[net new subscribers] [by campaign] [last 60 days].”  

    With this information, marketers can drill down into audience data to make sure the right messaging is reaching the right groups of prospective customers. In one real-world case study, a major communications company used ThoughtSpot to reduce waiting time on marketing reports from 30-60 days to instantly.

  3. Helps Reduce Customer Churn
    Customer churn is the enemy of monetization. But without insight into customer complaints and network issues, it will be very difficult to reduce churn.  

    Search-driven analytics can help here, too—as can AI-driven analytics, which uses algorithms to uncover insights automatically without even requiring a specific query. With this data in hand, telecommunications customer service teams can better serve subscribers to boost loyalty.

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