Big Data and the Telecom Customer Service Experience

Like many industries, the telecom industry is facing challenges. With ubiquitous internet access to new services and extremely low switching costs, customers have more choices than ever. This may be great for customers—but it’s certainly not great for the communications industry, which has to handle rapid change and increasing competition.

That’s why big data analytics, and telecom use cases for big data like improving the customer experience, are more important than ever.

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The Challenges Telecoms Face Today
Industries in general are changing at breakneck speed, and the telecom industry is no exception. Service digitization and network commoditization drive increasing competition and continuous disruption. And both consumer and business customers devour data and utilize data services at a record pace.

Providers such as Google and Facebook are at the center of this disruption. Their ability to layer services over the internet frees them from typical network costs. This allows them to continuously expand their offerings, at a cheaper and faster rate.

This puts traditional operators in a difficult position as they try to keep up, all while upgrading network capacity and providing more service advantages. What they need now is big data analytics to uncover new opportunities and areas for improvement. One of the biggest areas to be tackled is the customer service experience.

Telecoms Improve Customer Service With Big Data
A recent E&Y global telecommunications study found that customer experience management was the top agenda item for 68 percent of senior industry executives.

That’s not surprising. After all, the clearest path to improving revenue for both traditional operators and newer providers is increasing customer loyalty, which in turn leads to higher wallet share. A better customer experience means improved profits.

Top-performing companies accomplish this through higher levels of customer intimacy and service combined with a superior service experience.

But how can big data help telecoms improve their customer service experience?

Big data analytics can deepen understanding and help telecoms with their customer service. Here are a few example use cases:

Improve customer retention. Telecoms can analyze customer complaints related to network issues and correlate it with CRM data to better understand which network problems have the most impact on their customers.
Proactive customer care and reduced truck rolls. Companies can use the data they already have to accurately predict which customer issues result in unnessary truck rolls and develop a system for them to allocate resources accordingly.
Consistent service experiences with accurate demand forecasts. Telecoms can determine exactly where to lay out new infrastructure by forecasting future growth and network demands.
And much more
The Big Data Analytics Solution for Telecom
Big data analytics plays a critical role in helping telecoms create a wider view of how to improve their service experience. It can deliver:

The ability to use more data in the analysis, which is critical to gaining deeper insights
A simplified process of integrating, blending and preparing diverse data for analysis
A rapid insight discovery process that dramatically reduces analytic cycle times
Correlating insights to outcomes to drive downstream business actions
How Telefonica Uses Their Big Data: A Real-Life Telecom Case Study
Telefonica wanted to create a customer-first atmosphere that requires drawing new ideas and conclusions from big data. This required a cultural change, as they started using their data to foster new ideas to keep their customers happy.

They worked to create an analytics environment that broke down silos of information for customer and operational insights. Their results:

An improved customer experience based on data-driven insights
Over 20 use cases discovered and acted upon
A single view of customer and network data
Conclusion
With big data analytics, you can take your service experience analytics to an entirely different level. The analytical results can reveal totally new patterns and insights you never knew existed — and aren’t even conceivable with traditional analytics. The possibilities are endless.