Monetizing Telco Big Data: Location, Location, Location

This post is based on article with same name by Dr Sambit Sahu, a manager and senior research scientist at the IBM T.J. Watson Research Center. His current research focuses on Cloud and Big Data analytics for Telco and Smarter Cities.

As I read the above mentioned article, I felt that future has come to present in case of broadcasting!

Broadcasting is more than a century old,  starting towards end of 19th century, with data services offered by stock telegraph companies and “the theatre phone”, a telephonic distribution system that allowed the subscribers to listen to opera and theatre performances over the telephone lines.   The business model for broadcasting has been limited to very few ways or a combination of these, like funding, subscription, paid programming or the most popular one, advertising. There has been obviously tremendous growth in Radio and Television broadcasting industry in the past few decades. From the typical broadcast, with advent of internet and mobile, Radio and TV has option to become interactive too; yet, the traditional plain broadcast is still the most popular.

In the advertising world, contextual advertising is gaining ground. Though I am not aware of any studies which suggest effectiveness of this mode of advertising but logically it does seem that this will be likely the most effective. Contextual advertising is a form of targeted advertising where, the advertisements themselves are selected and served by automated systems based on the context of the user. For example, per Wikipedia, if the user is viewing a website pertaining to sports and that website uses contextual advertising, the user may see advertisements for sports-related companies, such as memorabilia dealers or ticket sellers. Contextual advertising is also used by search engines to display advertisements on their search results pages based on the keywords in the user’s query.

That was a lot of ‘context’ 🙂 to the article; coming to Big Data and monetizing Telco Big Data; Telcos have been the one industry which has bloomed exponentially in the last 2 decades; growing their susbscriber base through landlines, mobile telephony, internet and broadcasting services. This kind of provisioning in Telco world is called Triple play. Per Wikipedia, In telecommunications, triple play service is a marketing term for the provisioning, over a single broadband connection, of two bandwidth-intensive services, high-speed Internet access and television, and the latency-sensitive telephone.  This implies telcos have huge volume of data which can give ‘context’ to acitvities of their subscriber. This could lead to some indications of ‘behavior’ of the subscribers. This information along with location of the subscriber can potentially open lock to a huge treasure of knowledge.

Before we move forward on the monetizing, we need to also keep in mind the security and privacy issues related to usage of this data. The good news is that there exists tested and proven IBM technologies, which can surely give solutions in these areas. That, I am not covering in this blog post; but surely something to not just ponder but even decisive criteria.

In order to monetize the huge treasure of data in context of location; it needs to be converted into information which can be judiciously used to not only derive contexual advertising but also ensure that the efficiency of the advertisement peaks. Dr Sambit Sahu mentions two use-cases which is team has prepared and are going to demonstrate them in the ongoing Mobile World Congress. Below are the use-cases he mentions in his article

 In the first, the analytics platform is being used to create targeted Internet Protocol Television (IPTV) advertisements based on a customer’s profile. So, instead of every TV watcher seeing the same ad at the same time, during the same program, opt-in participants would see tailored advertisements that best match their profiles. Even individual family members would see different advertising based on knowing who is at home via cell phone location data, in conjunction with what programming they’re watching and their specific profile attributes.

The second use case, seeks to explore hyper-local targeted advertisements that will be delivered to mobile phones. In this use case, targeted advertisements and coupons will be delivered to a customer based on a better understanding of their profile, as well as current and predicted locations.

The play of the analytics technology is extremely core to these use-cases. Its the big data analytics technology which can from understanding of customer’s profile, infer intent, current and predicted locations or even predict future behaviors! This move from infering intent to predicting future behavior is which can lead to much better levels of effectiveness of the advertisement.

The model of Clow and Baack speaks of the six steps of objectives of an advertising campaign; Awareness, Knowledge, Liking, Preference, Conviction, Purchase. With real contextual advertising, telcos can move forward from branding methods of Awareness, Knowledge, Liking & Preference, closer to actual selling and sales revenues by Conviction, Purchase!

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