Telecom Analytics: Powering Decision Makers with Real-Time Insights

Transcription

1 Telecom Analytics: Powering Decision Makers with Real-Time Insights Anindito De, Practice Manager - Industry Solutions, CXO Services, Advanced Technologies & Solutions Subhas Mondal, Head - Wireline R&D practice, Global Media & Telecom

2 Table of Contents Introduction... 3 Real-time Analytics enabling operational decision-making in Telecom... 3 A few stumbling blocks... 4 Molding the future of Telecom... 4

3 Introduction Seven billion people and 6 billion mobile phone users the mobile phone has become ubiquitous in the truest sense 1. Just like the last decade belonged to the Internet, this decade is likely to belong to the mobile phone. Thanks to the increasing number of users, wide coverage and introduction of innovative services, telecom companies are seeing their voice and data usage volumes skyrocket. At the same time, competition is rife and players are jostling each other for market share. In such a scenario, waiting for weeks, or even days, for information to take decisions can be detrimental to business, and operational decision makers in customer service, marketing and network operations need intelligence that is current and instantly accessible. The effect of this need has been directly felt in the telecom analytics space driving the transition from batch oriented to real-time analytics as focus shifts from big data at rest to big data in motion. Further, some telecom equipment now come with inbuilt real-time analytics capabilities. For instance, routers are equipped to make dynamic decisions. These developments are going a long way in enhancing customer experience. Real-time Analytics enabling operational decision-making in Telecom Real-time big data analytics in telecom involves basically three steps. Network data is collected and stored. Predictive algorithms are used to continuously analyze and model the data to discover trend and correlation patterns in subscriber behavior in terms of usage, location and presence, quality of experience and even network performance like faults, capacity utilization etc. Meanwhile, the real-time data is streamed in from network switches, probes, OSS/BSS systems, business events are detected; they are matched with the predictive patterns to understand what is going to happen next (examples - prediction of subscriber churn, location change, new usage behavior, commute behavior or device failure, overload) and then a real-time decision is taken to respond in an optimal way (examples - offer for new service package, coupon for the nearby store matching subscriber preferences, family data share plan with other subscribers who are possibly related, pre-emptive alerts for impending outages etc.) Real-time correlation of network data with information about customer location, profitability and behavior boosts the decision-making capabilities of operators. Accordingly, the network support team can easily monitor subscribers and access real-time information on provisioning and configuration issues. They can use predictions or immediate alerts based on the alarm patterns to set right faults or reroute traffic to avoid service degradation. For instance, repeated call drops on a particular route could have multiple adverse effects in the form of higher support costs, unsatisfactory experiences and possibly even migration given the ease with which customers can switch providers today. With real-time analytics, the support team is alerted to this issue immediately. Network support executives can also detect fraudulent call patterns near real-time and prevent call back scams perpetuated by deceitful international operators that could have caused substantial losses to subscribers

4 Similarly, in the event of a blackout, telecom companies can give precedence to those networks that carry the traffic of the company s most valuable customers while notifying other subscribers of the problem and providing an estimated timeframe for resolution. Also, executives can use the intelligence gathered to prioritize service tickets on time. With speech analytics, telecom companies can not only gather insights to improve customer satisfaction at the organizational level, but also power customer center executives with the right intelligence to solve the issue, say bad user experience, by matching the data with previous interactions, billing history, current network performance and so on. Data from diverse sources is thus brought together and correlated to enable meaningful, intelligent and real-time interaction. These are just a few use cases of networks delivering relevant insights to operational decision makers in real time or near real time. Telecom companies should remember that precision in intelligence delivery is key as a wrong offer or a mistaken fault prediction would lead to poor customer experience and revenue loss, as well as diminish brand loyalty. Real-time predictive analytics supported by real-time data integration is the best solution for enhancing accuracy of insights. A few stumbling blocks The telecom analytics space however, has its share of challenges, one of them being lack of data correlation. A telecom network has numerous interfaces and protocol stacks and the equipment used is sourced from multiple vendors, rendering the task of collecting real-time information cumbersome. For instance, a customer may be facing difficulties in watching a YouTube video due to buffering issues, but the communication service provider (CSP) may not be able to detect and resolve the issue on time due to lack of data correlation. This results in a degraded customer experience. Added to that, context changes such as a change in customer location often leads to inconsistent user experience. Operators need to focus on finding means of correlating data drawn from different silos even in the face of changing contexts. Maintenance of and data security and customer privacy is another challenge. Telecom analytics today enables telecom operators to monetize their massive data mounds by offering customer information in terms of profile, location and phone usage to first responders, urban planning and law enforcement agencies and retail outlets among others. A pizza chain can avail of the location-based services offered by CSPs to push discount coupons to phone users when they are in the vicinity of the company s outlets. Irrespective of whether the data is being used for self or for offering to a third party, customer privacy should be accorded priority. Flouting privacy norms will not only annoy customers but also be in contravention of regulatory laws. Privacy regulations in the European Union (EU) and elsewhere restrict telecom operators from storing customer reference information. To ensure privacy, operators can anonymize customer references at the data collection layer itself and also use persona-based analytics for offer management. However, too many restrictions on storing and using customer data add to the difficulties in designing effective data monetization solutions. Policy enforcement in real time based on a decision derived from analytics logic is another key challenge. Molding the future of Telecom At the organizational level, telecom analytics has been enabling service providers to take proactive actions such as effecting capacity increase and network upgrades to prevent deterioration in user experience due to service degradation or failure. Now with real-time analytics, even operational staff can focus on day-to-day problems that have a significant business impact. Meanwhile, operators can draw on the same data to optimize available network resources and services, focusing on the pieces of network infrastructure that are vital in providing near-perfect service to high-value customers. Telecom companies are already leveraging advanced technologies like Event Stream Processing (ESP) and Complex Event Processing (CEP) in combination with predictive analytical scores based on historical data to deliver real-time operational intelligence. With further development in these and other technologies, real-time analytics is likely to meet more highly complex requirements of telecom companies. CSPs also need to work on creating Self-organized Self-optimized (SESO) networks that use real-time insights for optimizing network resources, say by throttling bandwidth when a user is monopolizing resources. Also, software-defined networking (SDN) is poised to play a significant role in telecom analytics. A research firm has predicted that the telecom analytics market will jump to $5.4 billion by the end of 2019; a very likely scenario, given the back-to-back innovation in the space and the increasing demand for real-time insights

5 About the Authors Anindito De Anindito De has over 14 years of experience as a consultant, data integration architect and specialist in the telecom, financial services and pharmaceutical sectors. In his current role, he leads the CXO Industry Solutions team in the Advanced Technology & Solutions business unit at Wipro. His present focus is on developing event analytics solutions for enterprises across domains, leveraging Real-Time Analytics and Complex Event Processing technologies. Subhas Mondal Subhas has over 22 years of Telecom experience spanning across Product R&D, Outsourced Software development, System Engineering, Solutions architecting, Technical presales. He currently heads the Wireline R&D practice that includes a portfolio of Optical, Ethernet and IP technologies. Subhas is a passionate technologist, always eager to learn new Technologies. SDN is a key interest area and he is developing SDN solutions that are relevant to Wipro customers. About Wipro Council for Industry Research The Wipro Council for Industry Research, comprised of domain and technology experts from the organization, aims to address the needs of customers by specifically looking at innovative strategies that will help them gain competitive advantage in the market. The Council, in collaboration with leading academic institutions and industry bodies, studies market trends to equip organizations with insights that facilitate their IT and business strategies. For more information please visit About Wipro Ltd. Wipro Ltd. (NYSE:WIT) is a leading Information Technology, Consulting and Business Process Services company that delivers solutions to enable its clients do business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of Business through Technology - helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner s approach to delivering innovation, and an organization wide commitment to sustainability, Wipro has a workforce of over 140,000, serving clients in 175+ cities across 6 continents. For more information, please visit 5

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