Analyze This

18 April 2015 | Jet Info, Speaker: Alexander Gusev

Today’s telecom operators usually earn their income by selling direct services. In this paper we’d like to demonstrate how these companies can generate additional revenue by using information stored in their systems.

Ever-increasing mobile data traffic

As early as in 2010 Steve Jobs, when asked whether mobile gadgets such as tablets could replace stationary personal computers predicted the arrival of a post-PC era. Time has shown he was right. As we can see from current mobile data traffic forecasts (Fig. 1), the consumption of information will exceed 17 exabytes a month by 2017. Mobile data traffic will increase by over 60% a year or by a factor of 13 from 2012 to 2017. Actual developments appear to be in line with these forecasts.

Fig. 1. Global increase in mobile data traffic from 2012 to 2017
Global increase in mobile data traffic from 2012 to 2017

The number of mobile internet users, now at about 2 billion, is also expected to reach 5 billion in 2017 (Fig. 2). Note that the current world population is 7.1 billion.

Fig. 2. Expected growth in the number of mobile Internet subscribers
Expected growth in the number of mobile Internet subscribers

How much data is generated by Internet users, including mobile ones? Here are a few examples. In 2012 we created over 2.5 quintillion bytes of information a day. Every minute over 70 hours of video are uploaded to YouTube, more than 100 million new emails are sent, almost 350 Gb of data are uploaded to Facebook and almost 600 new websites are launched (Fig. 3).

Fig. 3. Data generation today
Data generation today

Almost any person today has a mobile number. Some tablet users have even two or more. Of course, certain subscribers would just throw out a promotional SIM card once the balance drops to zero (or even a debit balance of a few dollars) but they are of no interest for the current discussion.

Sensible data management

What kind of information is available to a mobile operator? Most of them have already deployed DPI (Deep Packet Inspection) systems that can provide an enormous wealth of data. User traffic statistics indicate subscribers’ preferences as well as their current interests (such as travel planning, choosing a car or looking for a new restaurant near the place of work).

BSS systems are another important source of data about the subscriber’s gender, age, creditworthiness and service preferences. Moreover, they can provide valuable information on participation in social networks and geolocation (it it always useful to know the base station closest to the subscriber once he or she is online).

These data can well translate into an extra profit for the operator. Take the simplest case of revenue generation by offering additional services. Say, the DPI system indicates that the client is looking for a vacation package or international air tickets. Accordingly, we can offer additional services, e.g. the Planet Zero international roaming tariff with free incoming calls as of the second minute. If the subscriber has a history of ever using data roaming, as suggested by his/her bills, a relevant package could also be offered.

Here is another case. Imagine a not-so-distant future where subscribers would be free to change operators due to the MNP (Mobile Number Portability) service. Heavy users of social networks can be rewarded for their loyalty with a «carrot», i.e. a free sms package or free voice calls within the operator’s network. This policy would result in positive feedback from social networks (in fact, free advertising) and possibly attract new clients that are friends/subscribers of the user.

The above information can also be used for targeted advertising in cooperation with outside agencies. While this is a major separate topic, we would just give a few examples. Say, a person looking for an air ticket from Novosibirsk to Barcelona may be offered a taxi service from his/her home to the airport, and from the airport to the hotel in the destination city. In this context chances that advertising would work are pretty high. Also, advertising neighborhood restaurants or shops based on the subscriber’s location (work or home) may also be quite effective.

Anonymous user/group profiles can be sold at a profit. For example:

  • When planning to launch new retail outlets, chain stores estimate the number of potential clients using information purchased from the Russian statistical agency (Rosstat). These data are not very precise because the number of actual residents in a neighborhood may differ from that of dwellers registered with the police, A mobile operator, on the contrary, can supply highly accurate information on the number of people that are present at a certain time on a given working day or holiday in a certain area.
  • BTL advertisers may be interested in information on target groups of subscribers.
  • Public transportation agencies may take advantage of data on passenger movement to plan new routes and optimize existing ones.
  • As LTE networks expand their coverage and become more popular, OTT data traffic analysis may be used to offer paid services such as certain tariff plans, information on new movies in categories previously preferred by the subscriber and so on.

Even today mobile operators enjoy access to a vast amount of information that can be used to generate extra revenue by offering a variety of additional services to customers and/or selling certain data to interested third parties.

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