6 ways extreme analytics will transform APAC telcos

Joseph Lee / Kinetica
18 Jul 2018
00:00
News
Features

Data has become the new currency for telecom operators – the big data-driven telecom analytics market is expected to grow at a compound annual growth rate (CAGR) of nearly 49% between 2015 and 2020, accounting for $7.6 billion in annual revenue by the end of the period. Zooming into Asia Pacific, the region will be a key market driving this growth as it accounts for almost two-thirds of new subscribers globally by 2020, according to GSM Association’s The Mobile Economy Asia Pacific 2018 report.

In today’s digital era, the number of users, devices and things being hooked online has grown exponentially. What this means is that mobile networks have become the common thread that links these digital relationships together. From booking a ride on a ride-hailing app, to sharing documents with colleagues over mobile cloud systems, to texting your friends, the proliferation of mobile apps like WhatsApp has transformed the way things work in the telecommunications industry.

In addition, emerging technologies such as 5G provide a new digital highway for transmitting more data than ever before. These technologies have led to a staggering amount of data exchanged on the mobile and Internet networks, and thus enabling enterprises to capture incredible volumes of information about customer interactions.

With this increase in data sources and complexity of analysis, the key question for operators is: how can you leverage this extreme data to retain customers, improve and expand your business operations?

To successfully navigate in today’s extreme data economy, here are six key ways operators can leverage extreme analytics to help them gather, act on customer insights, and improve their bottom line.

  1. Enabling network and infrastructure optimization

Instantaneous connections have become the norm today. To meet customers’ demand for connectivity, telco operators will need to be able to track and visualize the real-time usage and status of their networks so they can gauge performance levels and flag any bandwidth or maintenance issues.

Extreme analytics will allow operators to perform real-time, predictive analytics and in turn, allowing them to identify equipment before it fails for lower service disruptions and lower maintenance costs. Mobile phones can also be “polled” on demand to determine the health of the mobile network.

  1. Monitoring capacity and usage

In the face of relentless data volumes and complexity, a traditional relational database management system (RDBMS) is no longer sufficient as it is unable to process vast volumes of complex and streaming data in real-time.

With extreme analytics, operators will be able to collect and visualize real-time data. This includes having the capability to identify the periods of heaviest network usage, forecast network capacity, and help operators plan for potential network outages or short-term surges.

  1. Analyzing call detail records and network usage

According to the GSM Association’s The Mobile Economy Asia Pacific 2018 report, mobile data traffic in the region will surge more than seven times from 2.1GB per subscriber per month in 2017 to 15.3GB by 2023. With this burgeoning volume of streaming high-cardinality data, there is an urgent need for operators to seek for alternative solutions to manage and process the extreme data. Vectorized columnar data store, for instance, can allow the operators to identify and swiftly act on network issues, better understand usage patterns of their subscribers, and more.

  1. Reducing customer churn

The two main limitations that can negatively impact revenue for telecom are if insights are not timely and response time to customers is delayed.

To identify declining usage and determine the customers who are the most likely to defect, companies should hence look at combining call detail records, social media feeds, call detail records, and network performance data. Companies can then address customer satisfaction and take the correct steps to minimize customer churn.

  1. Detecting and preventing fraud

With the recent technological advancements, operators can now collect and analyses real-time data to detect any anomalous behavior. Through the analysis of usage data, location-specific data, and customer account data, operators can model baseline normal behaviors which then allow them to develop predictive models to identify and proactively avert any fraudulent activities.

  1. Monetizing subscriber data

Telco operators are also investigating options for collecting, packaging, and selling anonymized subscriber data to other vertical markets such as retail, advertising, public services, financial services, and healthcare – Digital TV Research, for instance, estimated there will be 234 million subscription video on demand subscribers in Asia Pacific by 2022, up from 91 million in 2016. By combining various data collected, for instance location-based data and other subscriber data, companies can gather cross-reference insights on what advertisements interest subscribers the most, based on where they live and at what time of the day.

With these exciting opportunities on the horizon, the telecommunications industry needs to take the leap to stay competitive and achieve profitable change in today’s extreme data economy.

Joseph Lee is vice president of Asia Pacific Japan at Kinetica

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