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Big data analytics for top & bottom line growth

Operator marketing, customer care, operations and network planning teams are under increasing pressure to deliver customer experiences that drive revenue growth, and keep subscribers happy while they keep costs under control. New big data analytics tools are now helping service providers do just that by uncovering valuable information, that links customers to the networks that serve them. Big data offers powerful new possibilities, impacting both the top and bottom line growth. One could say it’s the future of the business.

Ericsson has been helping our customers to develop new KPIs and use cases that define the hidden “red thread” that links customer behavior to network behavior. We believe that finding this thread is a game changer for thriving in today’s customer-driven telecom marketplace. Through previously invisible insights, operators will uncover rich monetization opportunities and act to sustain high performance at lower cost while delivering superior and tailored experiences to every individual.

 

Using analytics to grow top line revenue


Knowing how the network is behaving from a customer perspective is going to be especially important going forward. One example of top-line monetization is targeted marketing. Analytics can tell us what kind of behavior exists, what kind of response is possible, on what type of platform, at what time of the day, according to what user profile. This kind of information is very valuable for operators who want to target their campaigns towards certain areas or geographies, certain demographics, at certain times of the day, and even certain days of the week and times of the year.

With networks moving towards virtualization, analytics can play a very important role in presenting upsell opportunities for monetizing data—instantaneously and ubiquitously. By converting data into insights, and insights into actions, more satisfied consumers will be less likely to churn, and valued enterprise customers will enjoy the level of SLA management they have been seeking.

 

Using analytics to grow bottom line net income


Key components for successful big data analytics are machine learning and artificial intelligence. The combination allows operators to avoid service degradation and outages and take corrective action more expeditiously. By maintaining a consistent high level of performance, a robust data analytics system contributes to efficiencies that positively impact the bottom line.

A prominent example centers on our industry’s rapid progress towards autonomous networks, where the analytics engine is coupled with a learning engine to provide for closed loop automation. This enables the network to constantly discover new ways to achieve high performance. Ideally, it will use past data to predict where issues or weaknesses could occur, even down to the subscriber level, so that actions can be taken proactively to overcome faults or work around any performance deficiencies.

 

Summing it all up


Once you have the appropriate analytics tool that yields actionable insights, you need to take the “next best action” at scale. That’s where machine learning really shines. By adding a rules-based engine that can engage in deep learning, the system is able to continuously look at the data and develop then instantiate new or enhanced algorithms. In this way, artificial intelligence provides the ability to identify what is needed to operate more efficiently for sustained high performance and growth.

Thanks to rising demand and a rapidly changing business environment, finding a robust big data solution is not a luxury for service provides. The right analytics solution can help providers in myriad ways: To craft better customer experiences that generate happy subscribers; encourage the use of new services when subscribers are most likely to react; enhance customer care with shorter call durations, faster trouble resolution and proactive attention to usage issues; and make improvements and take actions that will make subscribers want to share their experiences.

 

A robust data analytics system for both top and bottom line growth


One solution that combines all the requisites just discussed is Ericsson Expert Analytics. This data mining platform eliminates the hurdles that operators face as they aim to take swift actions that positively impact both the top and bottom lines. Based on Ericsson’s deep telco domain experience supporting networks that serve over a billion subscribers, it goes beyond traditional big data analytics by incorporating that domain knowledge to make the product telecom-ready.

Add to that Ericsson’s primary research on consumers and users, Ericsson Expert Analytics offers the most advanced customer experience management capabilities ready to address a broad range of telecom use cases. This includes delivering actionable insights that drive new revenue growth, a new level of operational efficiency, more focused capital improvements and better customer experiences.

See how T-Mobile is using Ericsson Expert Analytics to gain actionable insights.

Read more about telco data analytics with Ericsson Expert Analytics.

Editor's Note: This blog was first published by Ericsson in Fierce Telecom, June 4, 2018.

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