 | Issue: | Asia-Pacific III 2015 |
| Article no.: | 5 | |
| Topic: | Big Data: Creating strategic advantage for Telcos | |
| Author: | Lokesh Dadhich, Vikram Gupta & Shinichi Akayama, | |
| Title: | Principal, Dubai / Manager, London / Principal, Japan | |
| Organisation: | Arthur D. Little | |
| PDF size: | 414KB |
About author
Shinichi Akayama is a Principal with Arthur D. Little in Japan. He specializes in ICT and Electronics Industry and acts as a core member of ADL’s global TIME practice and Big Data competence centre. He has strong expertise in business strategy to monetize several types of advanced technologies, such as Big Data, Internet of Things (IOT), Artificial Intelligence, semiconductor and robotics.
Prior to joining ADL, Shinichi worked for a major Japanese mobile operator where he was involved in launching various types of services linking wireless networks with cutting edge ICT technology. He has extensive experience in creating new services through collaboration with Silicon Valley start-ups and major financial institutions amongst others.
Shinichi holds a master degree from the University of Tokyo in Aerospace Engineering.
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Lokesh Dadhich is a Principal with Arthur D. Little in the UAE. He focuses on Telecom industry in the areas of corporate strategy development and Company Transformation, Customer Experience, Organization Design projects. Lokesh has significant experience on Strategy and Transformation projects with Telecom Operators in the Middle East where he has helped operators implement a layered operating model with a centralized product development concept covering core telco services, digital services and data & business intelligence (and Big Data) services
Prior to joining Arthur D Little, he worked with Tech Mahindra and Hewlett Packard in CRM and Marketing Analytics consulting.
Lokesh holds an MBA from Indian Institute of Management, Ahmedabad, and a Bachelor’s Degree in Chemical Engineering obtained from Indian Institute of Technology, Kharagpur.
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Vikram Gupta is a Manager at Arthur D. Little in the UK and leads the Big Data competence centre. He has a wide-ranging experience of Telecom sector from strategic, operational, financial and regulatory standpoints. Vikram has advised leading telecom operators globally in terms of successfully monetizing their core assets including internal and external data assets. He is also a regular speaker and chair at multiple Big Data conferences.
Prior to joining Arthur D Little, he worked with another leading consulting firm in their Telecoms and Media practice.
Vikram holds an MBA from Indian Institute of Management, Lucknow.
Article abstract
Even though Big Data is a new frontier and there is widespread excitement about mastering and executing something new, it is important to base any new investments in skills and technology on a strong business context. Telcos should aim to build upon small successes and then gradually roll out Big Data initiatives organization-wide.
Full Article
Big Data is an invaluable strategic lever for telecom operators to reverse the revenue decline trend affecting markets worldwide. The emergence of Big Data technologies enables telcos to capture, analyze and monetize enormous volumes of customer information and interaction data across multiple touchpoints in real time. This provides them with a unique strategic advantage to improve the performance of their core business by offering more targeted products and services and differentiated customer experience. Ownership of customer insight (e.g. location, online activity and behaviour, and billing and payment history), has long been touted as an invaluable competitive advantage for telecom operators, especially in the wake of competition from over-the-top (OTT) players. As customers widen their digital footprints, Big Data use cases are moving away from descriptive analytics towards not only predictive, but also prescriptive analytics that cover users’ behaviour across all digital touchpoints. Partnering and collaboration with players across digital ecosystems is a key trend for telcos to further broaden and enrich and monetize their data sets.
The most valuable customer insights can be monetized, and an array of enabling data and business intelligence services can be offered to companies in a range of industries.
In the APAC region, most of the operators have made initial progress on this emerging topic. Operators well advanced on this path implemented the requisite technical infrastructure, started developing skills and aimed at an organization-wide cultural transformation to adopt a data-driven mindset. Whilst most of the use cases targeted by these players are focussing on improving the performance of the core business, other enabling business models for enterprise and clients are also being explored.
For example NTT DOCOMO, the largest mobile operator in Japan, provides climate data from its sensors installed at base stations. Weather data, generated by 4,000 sensors installed at base stations, enables application providers to create unique application services, such as prediction of influenza season, or pollen allergy season. NTT DOCOMO also started a new business in DMP (Data Management Platform) domain, to help enterprise marketing activity. Although data is anonymized, it is still useful in several marketing activities, including new product development, marketing research, etc.
Furthermore, Softbank is also working in Big Data, with a unique hardware robot – ‘Pepper’, which Softbank started selling in 2015. Softbank has ambitions to collect Big Data from consumers, by providing a human-like robot with an approachable interface. By introducing Pepper to home and business environment, Softbank is trying to create a platform which has an intelligence to communicate with humans.
A critical point of debate in ascertaining the value and benefits of Big Data for telecom operators is the level of contribution from internal and/or external monetization. The ambition of telcos with wider ICT focus has been in favour of external monetization. However, the business potential of internal monetization use cases can be six to nine times more than the potential realized through external monetization.
Use cases: internal monetization
Telcos will be able to increase revenue by improving their core and non-core product offerings. They can achieve this through targeting specific customer needs, with deep customer insight on one hand and enhancing the bottom line through greater efficiency in network planning, sales, customer care, etc. on the other. Some of the most interesting internal use cases resulting in significant quantifiable impact include:
Reducing churn through real-time call data record (CDR) analysis. One operator’s detailed analytics of CDRs revealed that customers experiencing more than ten dropped calls in a month had eight times greater propensity to churn. Furthermore, the operator could pinpoint the exact cause of the issue, from the tower to the customer’s handset. This insight, available in real time, was used to design a churn prevention action tailored to the individual customer. Results: This approach helped the operator reduce churn significantly, especially in its high-value segment.
Event-based churn management. Big Data analytics enables operators to conduct sophisticated ‘event-based churn’ modelling, through which operators can predict churn due to specific competitive actions and develop their own anti-churn campaigns accordingly. Results: Such efforts have improved the efficacy of one such operator’s anti-churn campaigns by a factor of three.
Proactive approach to customer experience management. An operator leveraged Big Data analytics to improve the uptake of data plan renewal offers. An analysis of customers’ renewal patterns indicated a greater propensity for take-up when the customers were closer to reaching the fair-use cap. The complexity of analytics in this use case involves several hundred parameters, such as customer usage, device used, when to contact the customer, and how much data to offer at what price. In fact, there are more than 100,000 possible combinations of these variables, which the operator can analyze to design the renewal offer. Results: With the right formula enabled by Big Data analytics, the operator’s conversion rate jumped to more than 40% from 1–2%.
Intelligent communication – using the ‘voice of the customer’. A telecom operator monitors Twitter and social network activity and then tailors marketing campaigns using customers’ colloquial terms, rather than traditional marketing or technical lingo. Results: By interacting with customers in their own language, the click rate for one such operators’ online marketing campaign increased by 30%, and the conversion rate by 20%.
Use cases: external monetization
Telefonica Dynamic Insights, and Orange Datavenue are some of the pioneering initiatives that telcos have undertaken towards external monetization. Some interesting examples of telcos successfully monetizing their Big Data capabilities through external use cases include:
Applications for third parties to optimize branch locations. A leading telecom operator uses anonymous and aggregated mobile network data to provide third parties with the best ways of optimizing their store locations and layouts. This application could be used by multiple players:
o Retailers to better understand footfall, tailor product promotions, and determine locations and formats for new stores.
o Town councils to measure impact on visits after implementation of new markets, parking spaces, etc.
o Sporting arenas to manage traffic and crowds during large events such as concerts and sporting events.
o Emergency responders to improve emergency response planning by developing an understanding of people movement at different places and times.
Smart targeting – Another operator analyzes web and location logs to develop micro-targeted campaigns by micro-location, lifestyle, type of handset owned, etc.
Optimization and targeting of billboards – Operators use their network data to help advertisers best position and measure advertising effectiveness.
Big Data operating model for telcos
Big Data analytics provides an immense opportunity for companies to rediscover the scientific way of working – hypothesis driven and based on rigorous data analytics. Implementing the most optimal operating model is key for organizations striving to maximize the benefits of Big Data. Critical aspects of best practice operating models are advanced data and business intelligence capabilities, a clear outline of the accountabilities across the data and business intelligence value chain, and organizational emphasis on the data and business intelligence function.
For operators, there is a range of options for operating models. At one end of the spectrum is the fully distributed model, in which the roles and responsibilities across the data and business intelligence value chain are dispersed across the organization. On the other end is the centralized model, in which a central function takes ownership from strategy to implementation of Big Data initiatives, and acts as a service provider to internal as well as external customers on data, insight and advanced analytical offerings. An operating model for Big Data should be selected based on some key considerations:
Maturity level of data and business intelligence processes – operators with lower maturity of traditional business intelligence functions should aim for greater centralization.
Ambition with respect to third-party monetization – operators targeting significant focus on third-party monetization should aim for greater centralization
Availability of advanced analytics skill sets in the organization – operators with scarce resources should aim for greater centralization to build capabilities sustainably in the long run.
The Big Data operating model should reflect a clear definition of responsibilities in the data and business intelligence value chain, with complete centralization of advanced analytics, business intelligence and information management activities to foster standardization and transparency of information on one hand, and organization-wide dispersion of end-user activities on the other.
Telecom companies should embrace the role of chief data officer (CDO). A CDO is dedicated to mining, analyzing and managing data, and coordinating its use throughout the organization across use cases. The CDO is in charge of the central data and business intelligence function, which should be the engine to deliver the analytics products roadmap, implement the chosen business models for internal and external monetization, implement robust data governance and promote a data-driven mindset in the organization.
Recommendations for decision-makers
Organizations striving to maximize the potential of Big Data need to implement the most optimal operating model. Critical aspects of best-practice operating models are advanced data and business intelligence capabilities, a clear outline of the accountabilities across the value chain, and organizational emphasis on the data and business intelligence function.
Even though Big Data is a new frontier and there is widespread excitement about mastering and executing something new, it is important to base any new investments in skills and technology on a strong business context. Telcos should aim to build upon small successes and then gradually roll out Big Data initiatives organization-wide. Once the value of Big Data analytics becomes visible, company-wide rollout and adoption become much easier to accomplish. This is especially true with something as vast and dynamic as Big Data, as it is pertinent that the business does not get ‘drowned’ in the huge amount of secondary information that will be inevitably generated.
