Using big data to get ahead in today’s mobile industry

The competition in telecommunications is at an all-time high, especially in the developed countries. In the US today, there are around 80 mobile network operators including four that reach nationwide, whereas in Europe there are over 35 mobile network operators, the four biggest of which serve 60 percent of the European Union subscribers. For operators desperate for a competitive edge, big data could be the solution to accessing new untapped customers and retaining existing ones.

Few industries have as much available customer data as the telecoms sector. Networks carry data about where customers are, where they like to shop online, what sites they browse, what services they’re looking for, who they call and more.

But as rich as the consumer dataset is, there are challenges to making sense of the data, namely

  1. Coping with the volume and velocity

The data in the digital universe is doubling every two years and by 2020, it will reach 44 zettabytes. To put that in perspective, it would take a single computer over 5 million years to download that amount of information, even on today’s fastest internet connection.

With most of the digital world being powered by the internet, it is fair to assume that this will also be indicative of the data passing through mobile networks every day.

  1. Data loss due to device usage habits

If the objective is to use users’ mobile devices to ascertain their location in a crowd, the footfall reported can be less than the actual number as everyone may not be using their devices at the same time.

  1. Delays in real-time transmission

For collecting location updates in real-time, network outages and latency can be a challenge, creating the occasional time-out and eventual under-reporting of actual crowd footfall numbers.

  1. The problem of oscillation due to physical environment or network traffic

In collecting customer location data from cell towers, networks face the unique problem of oscillation whereby a mobile phone intermittently switches between cell towers instead of connecting to the nearest cell tower. This creates a challenge because the mobile device’s true location is not known.

Overcoming these challenges opens up opportunities for operators to streamline internal operations and also in identifying new revenue streams.

Streamlining internal operations

According to a report from the global strategy consulting arm of PricewaterhouseCoopers, big data can help operators form a complete picture of their customers, offering new ways for them to innovate their own services and monetise that data.

For example, understanding customer density in wifi and 3G coverage areas such as department stores can help telco operators educate customers on wifi services and recommend data packages to offload 3G traffic and use wifi efficiency. They can identify the precise source of problems for customers who live in blind spot locations and improve the data usage experience on 3G networks.

With information on device usage, operators can analyse customer behaviour which leads to handset migration from 2G to 3G and 4G devices or even downgrades and communicate to the right audience in a specific place, improve accuracy in marketing communication and generate cost savings.

Creating new revenue streams from big data

For many operators though, the key question is how to monetise this big data. In the 2014 Industry Survey Report, 60 per cent of respondents said they believed it was more important for service providers to harness the power of big data to drive new revenue streams externally than it was to turn it to the advantage of their own internal operations.

This means turning the data that customers are happy to share with their service providers into meaningful insights. Combining geolocation information with customer data, such as browsing history, demographics like age, gender, education, would provide information that could be invaluable to serving up timely and appropriate advertising to customers.

If a customer has been searching for things to do while on holiday in Beijing, for example, location data can reveal when that customer actually arrives in the city. Service providers can then use that information to serve up targeted ads offering reduced museum ticket prices or restaurant deals.

Even the data usage experience of enterprise customers can be analysed to identify peak usage times, poor experience spots and commercial interaction to improve experience and increase enterprise mobile subscriptions.

Demonstrating the big data advantage

With the help of advanced data scientists and consultants, DataSpark has investigated the challenges in understanding the raw location data that service providers work with, through validation methods to improve cellsite-to-location mapping, market representative extrapolation methods, oscillation resolution and time-counting methods.

These techniques have allowed operators in the region overcome the key challenges associated with taming big data. AIS, a mobile operator in Thailand was a beneficiary of DataSpark’s work, especially in extending privileges for high value outbound roamers on their travels.

Firstly, insights into users’ store visits and device types allowed AIS to expand and optimise their stores for an increment in device and non-voice sales. Secondly, the knowledge of travel patterns of inbound and outbound roamers and their shopping and dining habits in malls prompted AIS to provide good network experience and extend privileges, recommend and customise packages through cross-selling or up-selling to high value customers, thereby increasing revenue and customer preference.

It is both daunting and exciting for service providers to realise the wealth of possibilities offered by big data. But as the world gets ever more connected, the right insights from big data could turn those possibilities into profit.

For more on how data analytics is disrupting the mobile communications industry, get in touch with DataSpark today.