By Yiannis Giokas
As we enter the era of big data, there are several opportunities for telecommunications operators to utilize the many data points that are now available to them to realize their near and long-term business objectives -- provided they can adopt a modern approach to the collection, organization and analysis of information. In doing so, telecom companies can optimize their operational expenditure, enhance customer experience and potentially create new revenue streams.
More importantly, they can avoid being a mere pipeline for OTT players (including the likes of Google, Amazon, Facebook, Netflix, etc.). I foresee a huge opportunity for telecom operators to shift their model from a mere data "connectivity" to data "behaviors aggregation" business, especially given my past experience in other industries that have been similarly disrupted. For example, for many years, security was a static analysis of logs against known patterns or rules, but given the evolution of the threat landscape, security products had to become smarter and more contextually aware -- and thus, they had to consume tons of logs and feeds in order to identify and even predict threats. That is exactly what we were doing at Crypteia Networks (of which I was founder and CEO), and our activities are what triggered our acquisition in 2014 by PCCW Global, my current employer.
To make this shift, the first thing telecom operators must have in place the right model to collect data. Data privacy is an increasing concern; the "data party" that started with the first browser cookie downloaded is coming to an end. Newly proposed FCC regulations will certainly impact telecom operators' efforts in this area. They will need to allow users opt-out rights (and opt-in consent before information can be shared with third parties), clearly share privacy policies with consumers and absorb the costs of greater reporting and recordkeeping, to name a few changes.
But telecom operators have a key advantage here. While they already have a number of solutions in place for tracking user behavior, traffic and transactions -- both for the purpose of complying with regulations regarding IP violations, illegal betting, censorship, etc. as well as optimizing service offerings -- compliance costs are a major consideration -- telecom operators also have the advantage of getting users to consent to some degree of data sharing in order to maintain their access to services, perhaps even in exchange for free services.
With that in mind, telecom operators that do succeed in creating the right model for data collection can start using the data collected from their users behavior both online (time, application, service, etc.) and offline (location, who else is in that location, what time etc.) to start building behavioral datasets that can be used in a number of applications, such as:
- Credit scoring based on user's contract history, how well he is doing with payments, if he is using a prepaid or post-paid plan, data and minutes usage, which locations he visited, etc. This is especially valuable outside the U.S. in countries that lack standards for scoring consumer's creditworthiness, or as an alternative means of credit scoring for people with little to no credit history.
- Content preferences via monitoring OTT services delivered to the customer, even from multiple providers (Netflix, HBO, YouTube), in order to create an overall preference profile for more accurate recommendations and advertisements.
- Shopping preferences online (by monitoring online shopping behavior across all providers) and shopping preferences offline (by device tracking via triangulation, public Wi-Fi, Bluetooth, in context with who else is there from your communication network, time, etc.). Information about buying behaviors online and off would be attractive to many retailers looking to target promotions more deeply.
- Restaurant, bar, coffee shop and other localized recommendations could be made based on consumers' spending profile for pricing, popularity during week/day, number of devices connected to that location and other measurable data points.
Those are just a few ideas, of course, but I think such data collection and analysis could lead to a line of products for all types of businesses ranging from existing OTT telecom operators, marketing/advertisement agencies and team, retailers, financial services providers and more.
Given that data science teams have already started popping up in a number of places, including at telecom operators, it might be interesting for industry leaders to start investing some time and resources into understanding what type of data they have access to as well as what they can do with it outside of the current uses in customer relationship optimization, capacity planning, fraud, security and recommendation engines etc. At PCCW Global, we are already utilizing such technologies and tools in the areas of cybersecurity, voice fraud identification and recommendation engines in our OTT offerings and sales analytics, and we have a number of R&D incentives in order to expand our capabilities in this domain.
In parallel, telecom operators might have to start thinking how this paradigm change in privacy laws will push them to alter the way they position themselves. As technological change accelerates (and privacy regulation with it), big data and analytics have swiftly moved from mere trends to a set of capabilities that need to be deeply embedded across functions and operations, enabling companies to have a better basis for understanding markets and making business decisions. For telecommunications operators, the opportunity is too great to ignore.
Yiannis Giokas is a serial Entrepreneur with domain expertise in Cybersecurity, Data Analytics and Telecoms; currently he is the Vice President of Research & Development at PCCW Global. He is also an active member of the Startup Community where he acts as angel investor, mentor & advisor in the areas of Analytics, Big Data, Cybersecurity & FinTech.