In the face of shrinking revenues and declining operating margins from their core communications services, mobile operators are retooling their business models in pursuit of new sources of top and bottom line growth. One of most exciting areas is Big Data monetization – mobile operators have access to unparalleled amounts of customer and network data, and yet they are only just beginning to unlock the value of this information.
What is Big Data for the operators?
Historically, operators have assumed the roles of enablers for information flows, with surprisingly little visibility into the context and content of their captive data assets.
Utilized intelligently and creatively, this data holds the key to an intimate understanding of customer needs and preferences, and in a broader sense, the successful evolution of the role of operators at the center of the mobile communications eco-system.
Mobile operators are the ‘natural’ Big Data companies, given they have a unique view into the behaviour and preferences of millions of customers by virtue of the network data that they process on a daily basis.
Any activity which touches the wireless infrastructure – voice calls, data transmissions and app downloads – creates a digital footprint which can be analyzed and synthesized into valuable insights.
And with the average smartphone now being active on the network for twenty hours per day (as measured by global operator Teléfonica), it is clear that mobile devices are becoming deeply embedded into the lives of the majority of users.
What does Big Data consist of?
The diagram below outlines the diverse nature of the available network data:
Structured data is the accumulation of bulk transaction and profile records amassed by operators on a daily basis:
- Itemized calling and messaging records – CDRs/xDRs
- Electronic data records – Web logs, searches
- Geo-positioning data records – Location coordinates, time, duration
- Billing profile records – Gender, age, address, spend
Unstructured data reflects the detailed interactions between subscribers within the network represented by the exchange of textual, numeric and graphical content:
- Social media posts
- Web browsing
- Media downloads and streams
- E-books and newsreader content
- App usage and interactions
Inferred data comprises the patterns of behaviour derived from observed social media activity and point-to-point movements within the network service area
- Social graph and influence graph – relationships, personal interests, attitudes, sentiments
- Location-based activity and context – retail footfall, travel dynamics, social preferences
Data – a new asset class
With this wealth of data becoming accessible to operators, their attention is turning to the use of advanced data analytics in order to drive internal initiatives – such as customer loyalty programmes and service personalisation – as well as monetizing the latent value of this information with the development of new offerings for B2B markets.
The decreasing cost of data storage coupled with the availability of high performance computing applications now enables operators to create information-based offerings – not only for their own subscriber base, but also for third party organisations seeking a better understanding of their mobile users.
We identify a number of detailed approaches to monetize Big Data in our Monetizing Big Data page.
Over time subscriber data with this degree of specificity and richness has the potential to become a new asset class – with a corresponding balance sheet valuation – for network operators.