Part 1 - The essential guide to use strategically analytics in the telecommunication market

Everyone talks about how Telecommunication operators can monetize big data and analytic by generating new business and new revenue streams.

I belong to the small group of professionals who believe that, as for telecommunication operators, the big data bet is not represented by a huge business growth opportunity, but that a dramatic improvement can be found in the area of optimizing and automating their business processes. Through the Big data and analytics, Telco operators can deeply change the way they operate: analytic – applied to Big Data - will be the intelligence which will operate the networks and most Telco processes. Thanks to analytic, manual processes will be automatized, make faster and more efficient.

Increasing efficiency and efficacy is a mandatory direction for all Telco operators who want to remain competitive in the future. Nowadays, communications companies (or Content and Service Providers - CSP) are facing a particular intense and disruptive period: they are squeezed by OTTs – which are successfully competing against Telco in the new services business – and by the increase in network managing costs, due to the exponentially-increasing bandwidth demand and network complexity, costs are not compensated by increasing revenues. Also if cost control cannot generate new revenues, process automation remains the best way for Operators to reduce their costs and sustain higher margins.In this scenario, big data and analytic will play a fundamental role, as they will be the key to dramatically increase Telco operational efficiency through process automation.

But devil is in the details: if the future presence of big data and analytic within the Telco business is clear, it is less clear what analytic cases should be implemented first, and how to define a successful adoption strategy. The market offers hundreds of analytic cases, but it is an extremely fragmented and siloes offer, without a vision on how to combine these use cases together. Having a clear strategy on analytic use cases deployment is key to be successful.

I don`t mean to draw a complete list or a taxonomy for analytic use cases in telecom industry. But I hope I can at least provide you with an overall picture, in which main analytic use cases are classified and described in terms of the problems they manage and of the integrated adoption paths proposed.

Let’s start with the classification. Big data and analytic uses in Telecommunication industry are classified in several ways: I have selected a classification based on cost saving for Telco:

1. Network/IT Optimization – all use cases related to optimize and/or automate network and IT processes: 
2. Marketing personalization and automation – all use cases related to automate and improve Operator’s marketing efficiency.
3. Security and Revenue protection – cases related to the use of analytic for revenue protection (f.e. fraud fighting, revenue assurance, credit control …) and for cyber security protection (f.e. hacker attach, cyber protection …)
4. New revenue stream generation – this area is dedicated to the use of big data and analytic to generate new revenue streams, as information brokering, mobile advertising, real-time location- based promotion ….

To easily identify a group, each of the use case belonging to a specific area is presented with the same color. Moreover, to reduce the number of the cases to draw, I have inserted only the most relevant.

To describe how to implement the different use cases and when, I have used a quadrant representation.

Please note that the maturity level of the use cases changes dramatically from one to another and in some situation it also depends on the evolution of Telco environment (f.e. Analytic for NFV). This means that the implementation of analytic has to be distributed over a longer period of time. I have represented the time needed to implement a specific use case, positioning it along a time line (X-Axis), called “time to adopt”; the complexity to implement is represented by the position along the “adoption complexity” axis (Y-Axis). The size of the use case indicates the economic impact of its introduction.

Use cases with similar goals (f.e. CEM) have been grouped, while same use cases applied to different Telco environments has been linked together using a dotted arrow line. The result is shown in the following picture.

The left-bottom quadrant is the “low hanging fruit” area. Here the use of analytic and related benefits is quite clear. This is the area of low-cost large-archive deployments (f.e large Hadoop implementation), Customer Experience Assurance (f.e. network assurance systems based on QoE KPI) and fraud systems. These cases have been already “explored” and implemented by most Operators.

The right-top quadrant is the “real strategic advantage” area. The first operators to reach this area will be much more competitive on the market, thanks to high efficiency and low operational cost. This is the area of high level of process automation: automatic network trouble management (systems automatically capable of identifying the root cause of QoE degration), automatic personalized marketing, automatic pattern detection of cyber-attacks, actionable network (Networks capable of discovering troubles and fixing them), etc. The use cases in this area are now in the experimental or PoC phase and they will be ready within 12/36 months.

The right-bottom quadrant is the “tactical” area. The analytic in this area are “tactical” advance for Telco. Most of the analytic for business-to-business (B2B) are in this area.

The left-top area is the “challenge” area. The benefits of the analytic cases in this quadrant are quite clear, but successful implementations are quite challenging.

Real strategic advantage Next Best Offer (marketing automation) Customer Care optimization Next best Action engine Automatic problem diagnostic Actionable network PCRF 2.0 and Full NFV Store of the future Cyber-attack prevention	Challenge IP Network usage analytic Personalized Automatic promotion Self-Customer care Advanced Churn management Subscriber preference information brokering Mobile advertising Subscriber Digital identity control Customer care optimization (Call reduction, sentiment analysis, ..) Content and Video recommendation
Low hanging fruit Archive optimization Customer Experience Assurance IP Intelligent routing Revenue Assurance and Protection	Tactical Analytic as a services M2M analytic WiFi indoor analytic Augmented reality