Data is definitely one of the most strategic assets for Communication Service Providers (CSPs) today. With the rampant adoption of smartphones and growth in internet, CSPs today have access to unprecedented amounts of data sources including customer profiles, device data, network data, customer usage patterns, location data, apps downloaded, clickstream data so on and so forth. Given an abundance of data at their fingertips, CSPs are virtually sitting on a goldmine of information and are in a great position to capitalize on these valuable data sets.
In order to gain insights into the avalanche of data that they have at their disposal, Telecom Service Providers are increasingly starting to adopt Hadoop & big data analytics solutions to turn their data into valuable business insights. Operators believe big data will play a critical role in helping them meet business objectives, promote growth, drive efficiencies and profitability across the entire telecom value chain. Today, Service Providers are using Hadoop in a multitude of ways to meet their business objectives. Some Service Providers utilize Hadoop purely as an operational data store to drive operational efficiencies – increasing storage capacity, improving performance and reducing costs while others are building specific data applications on top of Hadoop to drive real-time analytics and actionable insights. So how are Service Providers planning to leverage big data and analytics in the future?
Operators believe big data will play a critical role in helping them meet business objectives, promote growth, drive efficiencies and profitability across the entire telecom value chain
Key Big Data Use Cases for Telcos
From driving down churn to improving customer experience and driving operational efficiencies there are a number of use cases for big data in Telecom domain today. Based on a Telecoms.com industry survey, big data is poised to bring most value to operators in the areas of customer retention, customer segmentation, network optimization & planning and delivering upsell/cross sell opportunities.
See below the results from the Telecoms.com survey on the potential benefits or applications of big data for CSPs. As Telcos continue to accelerate adoption of big data, new and more innovative use-cases that leverage Hadoop are being developed every day. Although there is endless potential for big data within the realm of an operator’s business, most of the key use cases within Telecom can be grouped under the following four key buckets:
The section below looks at each of these key areas in more detail.
1. Customer Experience Management (Customer 360)
For today’s Telcos, improving and optimizing the customer experience is key to maintaining a market differentiation and driving down churn. Telcos are leveraging Hadoop and big data analytics to gain a true 360-degree view of their customers along the customer journey and across all of the diverse interaction channels. Based on the detailed customer profiles, Telcos can then do targeted micro-segmentation of their consumer base, offer a compelling customer experience, develop personalized offer recommendations and predict and prevent churn. Some of the key use cases in this area include:
- Targeted Marketing & Personalization: Offer personalized product offerings or derive specific upsell/ cross-sell opportunities based on modeling a number of key attributes including – subscriber’s usage patterns, device preferences, billing data, customer support requests, purchase history, buying preferences combined with their demographic information, location and socio-economic influences. Telcos can now create targeted customer micro-segments to offer more personalized offers and campaigns. This enables CSPs to proactively present the right offer at the right time, in the right context to the right customer in order to improve conversion rates. Examples include – personalized data top-up plans or up-sell recommendations based on data usage, device upgrade campaigns based on specific customer preferences, and discounts or tailored offers based on recent purchases or enquiries or calls into the call center.
Targeted Marketing & Personalization and Churn Analytics are among some of the most pervasive and common use cases for big data Analytics within Telcos today
- Customer Journey Analytics: Real-time analytics that map the user journey and generates actionable insights that can allow Telcos to respond quickly with a “next-best offer” and convert interested prospects into customers. Data such as customer demographics, purchasing behavior and clickstreams are being combined with attributes such as location and content preferences to for next best offers. This also enables CSPs to map specific customer’s interactions with the Telco at various stages of the lifecycle to promote tailored offerings and campaigns. Journey analytics example could include a real-time analytics model that pull together two personalized offers based on customer’s/prospect’s recent interactions, overall lifetime value and where they belong in the customer lifecycle.
- Proactive Care: Using big data, Telcos are building intelligence and analytics tools so as to proactively identify issues and fix it or offer a solution before it impacts the customer. Not only does it provides a compelling customer experience but it also deflects and prevents calls to the customer care centers thereby lowering support costs. Based on a recent survey conducted by Coleman Parkes focused on the Telco industry it found that 84 percent of respondents were more likely to recommend their service provider if the provider was able to identify and pre-emptively resolve potential issues affecting them’. Given the impacts, Service Providers are proactively fixing issues or reaching out to customers to help resolve issues before they negatively impact the experience. Telkomsel, in Indonesia, for example, has built a ‘proactive dashboard’, based on the Cloudera platform, for their broadband services to identify customer experience issues for their highvalue customers and proactively fix those issues or engage with customers.
- Predictive Churn Analytics: Based on a recent research from Ovum, Telecoms operators globally can be sure to keep only about half of their existing customer base over the next 12 months and about a quarter of all users globally say they will definitely change providers’. Given the impact of customer churn affecting the Telco industry today, Service Providers are effectively using big data analytics to bring together various data points including – quality of service, network performance, subscriber billing information, details on calls to the care centers, and social media sentiment analysis to build an effective model to predict and prevent churn. Churn prediction models allow Telcos to launch retention campaigns that identify and then address “at risk” customers via outbound channels. For example CSPs would be able to proactively reach out to high value customers, who have experienced a series of Quality of Service (QoS) issues or who shared a negative sentiment regarding the service in social media, and address those issues and offer them discounts or service credits to prevent customers from defecting.
Big data enabled Churn prediction models allow Telcos to identify “at-risk” customers and by proactively target them with retention programs
2. Network Optimization & Analytics:
In order to keep up with the explosive growth in mobile data, CSPs will need to continue to invest heavily in their networks, pumping in as much as 18 – 20% of their revenues’ every year into CAPEX. Network capacity is a highly valuable resource and Telcos are starting to leverage big data & analytics to effectively monitor and manage network capacity, build predictive capacity models and use it for prioritizing and planning network expansion decisions.
- Network Capacity Planning & Optimization: By correlating network usage, subscriber density, along with traffic and location data, CSP’s can more accurately monitor, manage and forecast network capacity and plan effectively for potential outages. Using real-time capacity data, CSP’s can visualize and pinpoint highly congested areas where network usage is nearing its capacity thresholds, in order to prioritize expansion for new capacity roll out. Similarly for areas with excess network capacity, CSPs can plan on running specific customer campaigns or promotions to
By correlating network usage, subscriber density, along with traffic and location data, CSP’s can more accurately monitor, manage and forecast network capacity and plan for potential outages increase uptake. Effectively optimizing and utilizing network capacity can mean millions of dollars in savings for Telcos every year. Based on real-time analytics and traffic, CSPs can also develop predictive capacity forecasting models, track actual versus forecasted traffic to fine-tune the model and plan for supplemental capacity in case of outages.
- Network Expansion & Investment Planning: Planning and prioritizing network expansion projects can be a tricky and a balancing act for CSPs. With so many dependencies and considerations, CSPs need to be able to effectively prioritize their investments and resources based on – future connectivity needs, strategic objectives, projected RoI, forecasted traffic, customer experience etc. all while ensuring that your highest valued customers get to benefit from these investments as well. CSPs need to be able to effectively combine network traffic data, customer experience metrics, revenue potential and location data along with customer value data to ensure they are investing their CAPEX in the right spots. A number of CSPs are already using Hadoop and big data analytics tools to aid in their network expansion and planning purposes. BT, for example, is using Hadoop and big data analytics to help them prioritize how and where they can expand high-speed broadband services to customers within the UK.
- Real-Time Network Analytics: CSPs are also using big data and analytical tools to build real time capacity heat maps that continually monitor the quality of user experience and alert the teams for network congestion or potential outages. CSPs used to rely on historical data for their network management but Big data analytics can enhance these processes by enabling real-time processing of network data to continuously monitor and manage the network and help them model network activity and map future demand. As a result, network engineers can get a holistic view of events occurring in the network and can proactively respond to network failures and outages helping them save millions. For example, Service Providers can now model the potential impact, in real-time, of a particular cell site goes down based on the number of subscriber and capacity in the adjacent sites. Similarly based on real-time data collected from the cell towers, engineers can monitor any drop in service performance at a specific location and send in crews, if need be, for a proactive resolution.
CSPs are using big data and analytical tools to build real time capacity heat maps that continually monitor the quality of user experience and alert the teams for network congestion or potential outages
3. Telco Operational Analytics
Another key area of application for CSPs is the use of big data around driving internal efficiencies, process improvements and cost savings around the core Telco operations. Telcos are starting to adopt big data solutions powered by Hadoop for everything from plugging and minimizing revenue leakage, managing network and cyber security, driving down order-to-activation lead-times to proactively identifying and fixing customer issues in order to minimize truck rolls. Some of the more
prominent use cases include:
- Revenue leakage & Revenue Assurance: Based on industry estimates, CSPs lose approx. 2.8% of their revenues to leakage & fraud annually – costing the industry approximately US $40 Billion every year which means Telcos could be adding $ 40 Billion to their bottom-line without selling any additional products or services. Leveraging Hadoop and big data solutions enables the CSPs to examine and plug dozens of actual or potential leakage points through the network and customer-facing systems, and to correct data before it reaches the billing system. Hadoop based solutions can help Service Providers to process and analyze both structured and unstructured data going back several years, rather than just a few months, enabling them to gain a better understanding of the behavior of customers. Crucially, Hadoop has made it cost-effective to use deep packet inspection (DPI) to detect fraud and revenue leaks, as well as identify new revenue opportunities. DPI generates vast amounts of data – up to millions of records per second, which simply wouldn’t be possible to collect or analyze without Hadoop.
Telecom industry globally loses an estimated US $40 billion each year due to revenue leakage
Cyber Security & Information Management: As device proliferation continues, Cyber security takes center stage for CSPs are they race to ensure their networks and associated systems are secure from malicious attacks. Legacy event detection capabilities are unable to collect and analyze all the data sources necessary for identifying & responding to advanced threats due to the sheer cost and complexity. Security professionals need to be able to access and analyze an avalanche of data (including logs, events, packets, flow data, asset data, configuration data etc.) in real-time in order to mitigate risk, detect incidents, and respond to breaches. Communication Service Providers are increasingly starting to rely on Hadoop-based big data platforms to collect and analyze log data, to find anomalies that will in turn fire an alert when detecting unusual activity and creates an event for a security analyst. The Hadoop based data hubs can provide a cost-effective platform for storage and advanced analytics capabilities to support deep packet analysis, behavior analytics and profiling and threat modeling.
Given all the data they have at their disposal, CSPs are starting to mine, model, aggregate and anonymize these data sets to create powerful statistics that can be of significant value to other businesses and verticals
4. Data Monetization
CSPs have unique advantage in that they have access to a wide variety and ever-increasing valuable sources of data including subscriber demographics, subscriber location, network usage, devise, application usage, preferences etc. Given all the data they have at their disposal, CSPs are starting to mine, model, aggregate and anonymize these data sets to create powerful statistics that can be of significant value to other businesses and verticals.
- Data Analytics as a Service (DAaaS): By combining the customer location information with customer demographics and preferences, CSPs are starting to provide Data Analytics as a Service (DAaaS) to other key verticals including: retail, financial services, advertising, healthcare, public services and other customer-facing businesses. There is a wide variety of application and use cases for data centric analytics ranging from – customer footfall analytics which is helping retail chains decipher who is visiting their stores and when, to assisting cities understand their traffic patterns and bottlenecks, helping logistics companies fine tune their delivery processes and aiding advertising companies offer targeted campaign and advertising for specific micro segments. A number of leading Service Providers including Verizon, Sprint and Telefonica are already capitalizing on these opportunities and have created specific business entities that focus on delivering analytics services and monetizing data assets for other verticals. Even though the data analytics market opportunities are still in a nascent stage, there is significant traction and enthusiasm in this space and CSPs will look to accelerate their revenue share from analytics services in the future. However, Telcos must navigate personal and data privacy issues by carefully aggregating and anonymizing customer data to ensure confidential provide information about individual customers are not disclosed. Though privacy is still a concern, if executed right, CSPs have an opportunity to effectively monetize customer insights by making it relevant to other businesses and verticals without compromising on subscriber privacy and rights.
The number of connected objects representing the IoT ecosystem is expected to reach 50 Billion by 2020
- IoT & M2M analytics: According to industry sources, the number of connected objects representing the IoT ecosystem is expected to reach 50 Billion by 20205 . Beyond playing a key role in managing the connectivity requirements of the 50+ Billion connected devices, CSPs are trying to leverage the IoT opportunity to move up the value chain from providing just connectivity services, to providing end-to-end M2M solutions, platforms and data analytics services. As data volumes from IoT are expected to increase at an accelerated pace, CSPs, due of their inherent proximity to data generated, can play a dominant role across the value chain from collecting the streaming data, to processing, storing, analyzing and serving intelligence back to their end customers. More importantly, CSPs have the ability to add location based and geo-spatial elements to the streaming data to enrich the insights of the data coming so that it can provide valuable insights to the enterprise verticals. Also since most of the streaming data from sensors needs to be encrypted before they can be transferred across a WAN, CSPs are ideally positioned to be the data integrators and aggregators providing security and analytics to the data. With petabytes of data streaming in multiple formats in real-time from sensors across multiple geographies, CSPs are leveraging Hadoop as the ideal platform to collect, store, secure, manage and analyze these data sets in real-time. Today, CSPs are already in the forefront driving the evolution of key IoT concepts including connected homes, connected cars, e-health and smart cities, and the demand for data management and analytics services will only continue to grow as these offerings mature.
This is just a summary of some of the most prominent use cases within the Telecom domain today and as the industry evolves, big data and analytics will continue to transform and be an inherent part of Telco operations with multitude of new use cases and applications. Harnessing big data presents Telcos with tremendous opportunities to effectively leverage the enormous amounts of data that they have in order to enhance customer experience, build more efficient networks, drive down costs and open new revenue generating engines.
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