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July 2016

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    The focus of telecom operators have shifted from reducing cost to delivering a superior customer experience over their networks.  Providing a greater customer experience every time is vital for limiting customer churn and building loyalty. With the advent of big data analytics and deep learning applications, the telecom sector has realized that data has become of the most strategic assets for their business success.

    Operators today are occupied with unprecedented amounts of data sources that include– customer profiles, device data, network data, customer usage patterns, location data, apps downloaded and clickstream data and so on. Given this abundance of data at their fingertips, they are not only sitting on a goldmine of information but, are also in a great position to capitalize on these valuable data sets. In telecom industry, data analytics is best described by the emerging requirements for more real-time information and breaking down data as per problem areas.

    Why monetize the data?

    By definition, data monetization is a process of generating revenue from various data sources available to the business or the real time streamed data, by instituting the discovery, capture, storage, analysis, dissemination and mobilizing use of that data. It is meant to leverage data generated through business operations as well as data associated with individual users as they interact with their product and services.

    Data monetization is essential as:

    1. It provides a process-led transformation in the business value chain.
    2. It makes use of digitization and emerging technologies such as cloud computing and machine intelligence to serve and automate the business functions better used in daily operations.
    3. Takes an integrated approach in bringing and reinstating the change.
    4. It integrates outside data with internal information and develops analytics to gain powerful insights into customer behavior and supplier capabilities to create new products and services.

    Industry practices for data monetization

    Needless to say, most operators are investing vigorously in big data. In return, the technology has created quite a stir in the industry and has impacted measures like churn reduction, loyalty management, customer acquisition and retention, generating new revenue streams, risk management, accelerated troubleshooting and network optimization.

    1. Targeting: Target products and marketing efforts using big data in order to increase loyalty and personalize the user experience aligned to the customer’s needs.
    2. Optimizing: Network generated data and other IT data can be used to inject into operational efficiencies and improving the effectiveness of customer experience management. Upon knowing how to optimize the network, operators are able to identify the telecom service areas to invest in new capacities, when is the right time to make telecom traffic-boosting offers and how to serve telecom customers more efficiently, etc.
    3. Innovating:This strategy is where the true potential of the business is unlocked, enabling new business models in place. This goldmine is capable of affecting the whole of telecom service value chain, making it a comprehensive and integrated strategy in use. This way the operators can swiftly move away from being just network infrastructure providers and become more than willing to invest in their partners for continuous innovation in service delivery.

     

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    How operators monetize big data

    1. Probes: This process is focused on collecting information solely to deliver it to the solutions capable of analyzing it.
    2. Appliances: There are devices put in place, that are capable of both aggregating and scrutinizing the data, thereafter, only a finished intelligence is required to be carried out.
    3. Real-time Analytics: These systems are able to create an exact measurement of jitter and delay in the networks allowing operators to improve customer experience significantly. Additionally, they are now able to reduce data packet loss which occurs during network overload.
    4. Advanced Software: The enhanced software used by operators today, help them to show how their services are being used. They are now able to capture the minutest of data about consumption, say how much of video an average user accesses on a daily basis, to keep operators informed about leveraging their service assets like multiplexers, optical fiber transmission, bandwidth and content.
    5. API Enablement: Application Programming Interface (API) enablement allows operators to bring in customers via a new application. This gives insight on which applications are used, how are they used and who are using it, enabling operators to cash more as per the usage need of the user and providing charging him on his preferences.

    Key business areas where Big Data has proved its worth:

    • Decision making
      • Network congestion control
      • Location-based services
      • Personalized advertising
    • Targeted marketing
      • Offer optimization
      • Churn prediction
      • SNA analysis
    • Operational efficiency
      • Customer care (IVR)
      • Network ROI analysis
      • Cell-site optimization
    • Intelligent business model
      • Payment data for increased sales
      • Match demand with supply
      • Data exposure and API enablement
    • Customer experience management
      • Dynamic customer profiling
      • Improved customer segmentation
      • Clickstream analysis

    The Road Ahead

    With the convergence of telecom services and big data-enabled solutions, every operator leverages the power of data analytics and predictive analysis and it is here to stay for long by impacting other performance areas in the near future, like:

    • Revenue assurance and fraud
    • Precise marketing and campaign management
    • Preemptive service assurance
    • Package design for specific OTT
    • Multi-SIM prediction
    • Accurate capacity planning
    • Subscriber centric wireless offload

    Conclusion:

    The ultimate goal of big data is to combine and correlate every information source to foster a holistic, transparent, end-to-end view of all the interactions every subscriber has with the operator. But to really leverage big data, operators must radically modify how they gather, verify, learn from, and make use of the information available at their disposal. Because in an industry like telecom it’s not the intrinsic value in the data that matters, it’s what operators do with it, counts.

    July 19, 2016 0 comment
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    Since its introduction 2001, big data has improved the health of many industries and the telecom industry is no different. The industry is witnessing a phase of stagnant growth and thus, every telecom operator is searching for new ways to increase their ARPU, overall revenue and profits. Owing to this maturity phase, many of the telecom operators are turning to new technologies like big data to enhance their service value.

    Big data is being generated by everything around all the time majorly by all digital processes and social media exchange. Big data is being created from multiple sources at an alarming volume, velocity and variation. In order to extract value from big data the telecom companies need optimal processing capabilities, analytical power and skills to spot new business trends and opportunities to tap on. To begin with, operators can experiment with the data they have on hand in order to see the kind of connections and correlations it unveils. Operators have sought for decades to make the best use of information to improve their business capabilities and their answer lies in big data analytics.

    Why is big data technology unique?

    Big data technology focuses on finding hidden threads, trends, or patterns from heaps of company data. It represents significant information which can open new avenues of opportunities and the way this information is analyzed to help tap on those underlying opportunities. The three core reasons behind big data being in the forefront are:

    1. Finds competitive advantages
    2. Affects all spheres of business
    3. Drive innovation and improvement

     

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    Dimensions of Big Data in Telecom

    The concept of big data gained momentum is the early 2000s and is breaking the threshold of business potential. The dimensions in big data are:

    Volume– The telecom operators collect customer data from various sources including usage history, VAS subscription details, service transactions, location and lot more. In the past, data storage used to be a problem but now, the advent of new technologies has eased the burden of large volume of data storage.

    Velocity– In the telecommunication sector, data streams in at an unparalleled speed and thus, the storage and analysis must be done in a timely manner. Payments, sensors, RFID tags etc. are driving the requirement to deal with unprecedented amount of data in the near-real time.

    Variety- There are different data formats in telecom that get collected.The different types of data include structured, traditional databases with numeric data to unstructured text documents, financial transactions and emails.

    Variability-In addition to the high velocity and variety of data, the flow of data may be inconsistent with periodic peaks. Daily, seasonal or event-triggered data loads may be challenging to manage- even more challenging with unstructured data.

    Complexity –Telecom data arrives from multiple sources, which makes it difficult to be inter-link and transform across systems and services. However, it becomes important to connect data relationships, hierarchies and multiple data linkages, or data can go out of control.

    Importance of Big Data in the Telecom Industry

    The advent of smartphones, increased subscriber base and equally increased consumption worldwide, shows that the telecom operators have absolutely no dearth of data. They are engaged in collecting data from usage transactions, network performance data, data sourced from cell-site, service offering and portfolio data spread across geographies and back-office data alongwith the real-time data. Sitting atop this heap of data can reap out trends and patterns on customers’ usage, behaviour and complete end-to-end subscriber insights. Other benefits include:

    1. It saves time by correlating a 360-degree view from fragments of user data, which otherwise is a complex and time consuming done when it’s done manually.
    2. The quality of the collected data is needed to be polished, trimmed and de-duplicated. This is achieved easily by simple algorithms of big data technologies.
    3. As the role of telecom is evolving in the economy, technologies like big data give them access to leverage and exploit every bit of customer data to serve the people better quality.
    4. The rise of many telecom players necessitates each of them to build up key competitive advantages of their operations and services that are extracted from big data monetization.
    5. Big data is not a replacement of the traditional analytics, but an add-on to the operators to fill the gap between of collecting data and digging valuable business insights.
    6. The information or insights from big data is asked for and consumed to make better decisions or to create new products and services. This way the whole infrastructure evolves to fulfill the demand in a better way.
    7. The data collected arrives from different silos of the operator, namely- service provider’s business, network data, IT department, marketing and product departments, etc. Big data co-joins all of it to evolve as a unified business unit.
    8. Some other benefits of big data that directly helps in saving costs and time are:
    • Lowering down the need for data compression
    • Reduce data maintenance and storage cost
    • Reduced time in running manual queries on data
    • Leverage commodity hardware
    • Response in real-time
    • Easy implementation of any data model from any data source

    Outcome of Big Data in Telecom Industry:

    Today, the telecommunication sector has found big data as irreplaceably useful. The importance of big data doesn’t rely on the data volume you have but, what is done with it. Data can be collected from any available source and data analysis can be carried out to find results that enable:

    • Cost reduction
    • Time reduction
    • New product/service development
    • Service optimization
    • Smart decision making

    Big data combined with analytics, can accomplish many telecom service tasks which promise high sales and increased ARPU, among other things like:

    1. Determining root case of issues, defects and failures in near-real time
    2. Creating offers based on customer’s buying habits
    3. Offering customized services
    4. Analyzing value proportions
    5. Calculating risk portfolio within minutes

    Conclusion

    It is quite important to be kept in mind that the core value from big data arrives not from data in its raw form but, from data processing and analytics along with the insights, products and services emerging from the data analysis. The solution effectively utilizes structured and unstructured data to improve decision making and alleviating business problems.

    July 18, 2016 0 comment
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    Future proved business growth with loyalty programmes

    A few years ago, telecom operators were generating revenue and increasing their market share by investing resources in customers and through acquisition of various companies. Currently, however, in the face of increased customer attrition rate and declining ARPUs, loyalty programmes are the need of the hour.

    For 66 per cent of telecom customers, tailored or customized services, rewards and proactive care are the key drivers to win customer loyalty. For 89 per cent of operators, creating one integrated customer profile is crucial for supporting customer retention and loyalty strategies. However, only 21 per cent of telecom service providers have the needed collaboration between IT and loyalty departments. (Source:Amdocs)

    What is a loyalty program?

    Loyalty programs are a type of reward program and a marketing strategy structured in order to encourage the customer to continue their association with a product or service. The customer registers their information with a company, following which they are given a unique identifier which can be use while making a purchase in order to enjoy the benefits of a loyalty program. A loyalty program can give a customer special sales coupons, discounts, rewards, free merchandise, advanced access to new products and lot more.

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    Building successful loyalty programmes

    As today’s customers want value, service and rewards; merely a competitive price strategy is not enough to retain existing and acquire new customers. Competition has forced telecom operators to validate their sales and marketing strategies with loyalty programmes. Thus, customer retention is as important as customer acquisition to stay ahead of the competition. In this context, operators are focusing on how to build an effective loyalty program.

    A successful loyalty program:

    • Must share an equal value it earns in proportion to the value consumer gets.
    • Should be aligned with companies’ objectives and goals.
    • Must cater to the interest of the customers.
    • Must be attainable, meaningful and relevant to the customers.
    • Must have a capability to entice new customers to change their behaviours simultaneously retaining existing customers by rewarding present behaviour.

    A loyalty program should include a combination of effective rewards, promotions, communication and convenience to be successful.

    Summary

    In a highly competitive market, telecom operators can retain their values by building an affinity with subscribers, along with a pre-established expectation of trust driving loyalty. Keeping existing customers and acquiring new customers is a powerful weapon in today’s market.  Today, therefore, loyalty-based initiatives have become a necessity in the marketing mix, helping operators attain their marketing goals with the implementation of a successful loyalty program and creating a win-win situation for all.

    July 8, 2016 0 comment
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    There’s no escaping it-customer experience management (CXM) is what makes or breaks a brand. This is especially true today, since telecom operators live in precarious times, with falling average revenue per user and wafer-thin profit margins. To top it all, customers are just itching to jump ship at the first sight of trouble-i.e.-bad customer service. Let’s look at a few analyst statistics in this regard:

    • By 2020, customer experience will overtake price and product as the key brand differentiator-Walker
    • 68 per cent of customers say that they’ve switched service providers owing to poor customer experience-Accenture
    • 95 per cent of dissatisfied customers tell others about their bad experience-Zendesk
    • Customers who experience a positive social customer care experiences are nearly three times more likely to recommend a brand-Harvard Business Review

    In other words, operators, please sit up and take notice! A sound CXM roadmap is the secret ingredient that gives your business extra bite. And here are a few more statistics to prove it:

    • According to American Express, one happy customer can equal as many as nine referrals for your business.
    • Maximizing satisfaction with customer journeys has the potential not only to increase customer satisfaction by 20 per cent but also to lift revenue by up to 15 per cent while lowering the cost of serving customers by as much as 20 per cent-McKinsey
    • Customer experience leaders have more than a 16 per cent advantage over competitors in willingness to buy, reluctance to switch brands, and likelihood to recommend – Temkin Group

    Now the tricky bit-where do operators start? Well, it’s a two-fold step, really, that begins with proactively tracking down what customers (actually) want. This, in turn, will (hopefully) throw up actionable insights into the challenges related to ensuring customer satisfaction. Chalking out a CXM strategy is merely the most obvious result of the above plan.

    So, what makes a customer tick? While there is no single appropriate response to this question, I would like to offer up analytics as a viable option. Here’s why-the age of data is upon us. We collect copious amounts (some analysts say an estimated 2.5 quintillion bytes) about individuals, places, processes, et all, every day. This data comes from multiple sources, such as sensors used to gather climate-related information, posts on social media sites, digital pictures and videos, purchase transaction records, cell phone GPS signals, the list is endless.

    Here’s the catch, though. While there is little doubt that the intelligence of our systems is responsible for facilitating this growth, the volume of structured and unstructured data being collected isn’t necessarily valuable on its own. To gain actionable insights, an operator ought to know what to do with this data, how to leverage it to the fullest.

    This is where analytics steps in. Before jumping the gun, however, operators ought to be aware of the essential elements of using analytics successfully:

    • Aggregate data from multiple sources. Let’s face it, a multi-tiered approach is essential to gain a 360 degree view of the customer journey. Operators, think Facebook, think Twitter and all the social media platforms out there today. The customer uses these mediums to broadcast their views on every brand. The trick, therefore, is to integrate analytical tools from CEM into social media monitoring to identify customer behavioural patterns. The result? Proactive engagement at every stage of the customer lifecycle!
    • Utilize existing CRM system data: This essentially ensures that data is centralized, accessible and can be used to gain a holistic picture of the customer. Operators need not waste precious time in collecting the same data over and over again.
    • Examine unstructured data: Going forward, data volumes are only going to increase substantially. The idea, therefore, is to adopt an all-encompassing approach, which may have to include complex data mining practises. If carried out proactively, companies can gain a competitive edge and unearth previous unevaluated customer data links.

    There’s another catch-operators, please note, analytics isn’t a “one size fits all” strategy. Choose your best fit. Interestingly, the evolution of analytics itself took place over multiple stages. According to industry analysts, in the past, all available data was scrutinized using descriptive analytics, which looks at the reasons behind past success or failure. An example is the results a business gets from the web server through Google Analytics tools. The outcomes help understand what actually happened in the past and validate if a promotional campaign was successful or not based on basic parameters like page views.

    The next step (and with the advent of big data) is predictive analytics, which focuses on the question: “What is probably going to happen in the future?” An interesting example of an application is in producing the credit score. Credit score helps financial institutions decide the probability of a customer paying credit bills on time.

    Next up is prescriptive analytics, which goes beyond future outcomes to answer the question: “What is the able action?” Interestingly, analytics are taken a step further, with the advent of highly intelligent cognitive systems. Instead of needing to be programmed, they use natural language processing and machine learning algorithms to help make key decisions using huge volumes of fast-moving big data.

    Please note dear readers that these four categories of analytics should ideally co-exist. There is no question of one outweighing the other, in terms of benefits, etc; they’re all different, with their own merits. Do remember, though, that all are equally necessary to obtain a complete and clear picture of what a customer actually wants by using all of the available information and data.

    I’d like to conclude with a caveat which is that analytics is only as good or as bad as the implementation of the requisite action plan. For analytics to be successfully leveraged, the operator ought to be guided to the actionable tasks which can be implemented. If not, the company runs the risk of “analysis paralysis”, which doesn’t leave any room for any quantifiable outcome. In the end, the numbers say it all!

    July 4, 2016 0 comment
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    Introduction

    Mobile broadband has witnessed incredible growth in emerging markets. Though growth and usage patterns have seen huge variability, traffic volumes have been registering exponential growth- mainly because of the rising deployment of 3G networks, the accelerated adoption of smartphones, the influx of application stores and the increasing use of bandwidth-intensive applications, such as video streaming.

    In cases where multiple subscribers attempt to connect to the same telecom network at the same time, the network faces tremendous pressure. In such cases, the network can trip and cause internet outage, disconnection, etc. In order to prevent such a situation, internet service providers employ a broadband traffic management system to limit the maximum download and upload speeds.

    What is intelligent broadband traffic management

    Intelligent broadband traffic management ensures that all broadband subscribers connected to the same local exchange have an equal share of bandwidth. This arrangement provides a unique end-to-end quality of service (QoS) differentiation with content adaptation, caching and distributed delivery.

    Broadband traffic management is employed when broadband providers adjust the download or upload speeds for certain tasks at peak times, to make sure that everyone using the network has a stable connection. In practice, every telecom provider has explicitly laid down a Broadband Traffic Management Policy, available for public disposal, to state it’s method of handling such critical cases beforehand.

    Advantages

    1. When traffic and content management blend together into the network, a more valuable mobile broadband experience is bound to be created while using up the minimum network capacity.
    1. It uses an integrated approach in traffic management, exercising control over network policy, optimization and delivery of content.
    1. Intelligent broadband traffic management promotes a fair and balanced usage of network resources.
    1. It ensures that operators are able to build customer loyalty by differentiating their offerings, as per the subscribers’ needs. In turn, this enables the operators to better map out the revenue potential of their existing customer base by offering tailored packages and services to each of them.
    1. When operators control delivery down to the device, they are capable of offering a unique and valuable service to the content rights holders. Intelligent broadband traffic management prompts in providing the best possible customer experience.

    IBTM

     Traffic prioritization in broadband traffic management

     Traffic prioritization assists in prioritizing the different types of broadband traffic directed towards the subscriber.It is used to create the best possible service when a large chunk of subscribers are trying to do more online than the given network can physically support at the time.

    However even in prioritization, the service subscribed to remains unlimited and there’s no limitation put on line speed, usage or downloads and has no downside to the practice of prioritization.By prioritizing some of the time sensitive traffic, the operators are providing the best possible service and experience, particularly in a relatively slow broadband speed.

    How is broadband traffic management achieved?

    In practice, broadband traffic management is achieved by limiting a user’s maximum download and upload speeds during peak hours to distribute the bandwidth evenly and provide online access to all. There are various services that fall under this technique to serve the network better:

    1. Policy and Charging Enforcement Function (PCEF): The PCEF process combines the power of deep packed inspection (DPI), policy enforcement and charging function engines to inspect, adapt and enforce traffic management policies. The use of DPI makes it possible to find, identify, classify, reroute or block packets with specific data. It allocates the available resources to streamline traffic flow and achieve advanced traffic shaping and efficient bandwidth management. The PCEF optimization component helps network equipment and software vendors to enhance their strength of serviceswith protocol management capabilities.
    1. Traffic Detection Function: These are used to enable operators to optimize the delivery and performance of applications and services, personalize the user experience by providing tiered service plans. This even allows subscribers to manage their own usage and monetize network utilization, charging and controlling the usage, adjusting service availability and the cost.  The Traffic Detection function is an important element in the network due to the increasing complexities in managing data services, the growing demand for personalization and the need for service differentiation.
    1. Traffic Steering: This element enables operators to accelerate service creation and deployment by intelligently routing traffic to the value-added services, that helps improve efficiency and scale the network services more effectively. Traffic steering is context-aware at both user and application level.By steering only the relevant traffic to each service in the chain, it achieves operational efficiencies, optimization and monetize the operator’s network.

    Conclusion

    Owing to the complexity and growing load on the network, operators resort to such unbiased measures, especially when it comes to real-time traffic management. The baseline is that such systems don’t stop or deliberately block certain applications; instead they make sure that the most important, time-sensitive activities get the highest priority.

    July 4, 2016 0 comment
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    The convergence of broadband services, smart mobile devices and virtually unlimited content has significantly altered the telecommunication market. This has resulted in a rapidly increasing demand for internet services. Alongside, it has also resulted in the explosion in usage patterns and data traffic, evolution of 3GPP, advancement in microchip technology and standardization benefits like freedom in choosing a service provider and cost reduction and increased benefits.

    And thus, to remain relevant in this changing environment, operators in the telecommunication industry are addressing critical challenges in order to create new business models, transform their revenue streams radically and in turn, the business models and value chains are being modified consistently. Therefore, telecom operators must examine statistics to determine how and under what conditions the subscribers and the applications use the network resources in order to formulate the policies.

    The policy rules between the application and the policy enforcement points are managed by the Policy server, which can add or re-configure policies in order to control and manage the Quality of Service (QoS), charging quota, admission control and optimization.

    Various interfaces make the integration of PCRF with any network (mobile or fixed broadband network) easier. Since, the telecom operators will keep migrating from 2G to 3G to 4G networks in the coming years, the existing networks must operate efficiently and concurrently with the newer networks.  As subscribers opt for portability between networks, the operators must maintain session visibility in order to be able to control sessions per subscriber dynamically.

    PCRF addresses these challenges. It is a network agnostic solution (for wireless or wire line networks), which enables a multi-dimensional approach to create an innovative and lucrative platform for the service providers.  It can be integrated with various platforms like billing, charging, rating, subscriber database and can also be deployed as a standalone entity.

    PCRF in-detail

    PCRF stands for Policy and Charging Rules Function. It is a dedicated policy equipment standardized in 3GPP, which enables policy function for charging and bandwidth on the multimedia networks. It’s a new term introduced alongside the publishing of 3GPP policy charging control (PCC) architecture.  Often, it is also referred to as Policy Decision Function (PDF) or Policy Server.

    Policy and Charging Rules Function (PCRF) has an increased strategic significance and wider potential role than conventional policy engines. The operators deploy PCRF as a policy solution that considers actual usage scenarios of the user and combine it with charging and billing system while providing differentiated policy control and tariff packages.

    As a policy tool, PCRF plays a central role in making policy and charging rules based on subscriber’s usage, location, status roaming and other factors. It implements flexible policy control for mobile, fixed-line, and IP multimedia subsystem (IMS) services. Being an important part of IMS architecture, PCRF integrates avalanche of information to and from various multimedia networks like portals, operational support system, and charge rules while automatically creating policy decisions for each active network subscriber.

    The PCRF function serves as a part of the PCC architecture along with P-CSCF (Proxy Call Session Control Function) andPCEF (Policy and Charging Enforcement Function). These elements of the PCC architecture provide resource, access and Quality of Service (QoS)control.

    PCRF is a significant part of the IMS architectures; however, it isn’t exclusive to the 3GPP network in which it’s certified.  It arrives pre-integrated in an IT server and works across wireless networks.

    PCRF is also an important element in SPIT (Service Provider Information Technology). Interfacing with the main packet gateway, PCRF takes charging enforcement decision on its behalf. The centralized device can serve as a Policy Decision Point (PDP) for the mobile operator and can get as granular as an individual subscriber.

     The PCRF architecture

    PCRF comprises of three main components:

    • Policy Servers- to provide the policy and charging management function
    • Subscriber Profile Repositories (SPR)
    • Configuration Central Management System (CMS)– to centralize the provisioning and management of policy servers

     

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    Policy Server- The PCRF server serves as the main server that processes the policy request from core network element or Operator Support System /Business Support System (OSS/BSS) in the real time. The Diameter based 3GPP, Sy, Gx,  Gxx, Rx, Policy and Charging Rules Server, Policy Decision Platform, Sp and Sd Connectors, Subscriber Profile Cache and Subscription Management Service are important parts of the PCRF server. The rules engine of the Policy Server works as the standards based PCRF in the network. It functions on triggers, processes conditions and then executes proper actions based on the conditions. The rules engine can be evoked based on any interface trigger. Both GGSN and DPI via either the SOAP/XML interface or the Gx interface, the SPR via the Sp, also the application function via the Rx interface can activate the rules engine.  The internal timers that support a range of time of day based applications/use cases can also trigger the rules engine. Policies can be developed rapidly using Policy Rules wizard.

    Subscriber Profile Repositories (SPR)- The main component to run PCRF server is SPR- Subscriber Profile Repository that is the repository to store all technical assets, business assets, and configuration items used by the PCRF Server also known as Central Management Server. Policy’s SPR is the policy solution database to store subscriber profile, quota, location and state information of the Policy Server to use in its policy implementation. In order to store subscriber profile information and intersession state information (e.g. usage and quota tracking), the SPR is deployed in networks. It should be implemented in a range of configurations according to the customer requirements.

    Central Management System (CMS)- To monitor and manage the PCRF Server and Repository Server there is centralized server node which is called as Policy Management Platform/Central Management Subsystem Central Management Subsystem. It is considered as the main part of Central Management Server to provide the OA&M functions. The Management Platform gives an integrated view of system alarms and logs and has SOAP/ XML API to interface to external systems. Add to this, PCRF server also has following components /functionalities:

    1. SPR Proxy subsystem – For the management of internal SPR i.e. subscriptions and subscribers, this component exposes the Web Services API within PCRF Server.
    1. Load Balancer – For PCRF servers, the load balancer is an important part in the distributed deployment environment. It provides the diameter application level load balancing capability.

    Functioning of PCRF

    Service providers use PCRF for automatic creation of policy and charging rules for subscribers. It becomes an important element in SPIT where it interfaces with the main packet gateway and takes charging-related decisions. For an example, using PCRF, service providers charge subscribers extra for quality guaranteed servicesor volume of usage of high-bandwidth applications during peak and off peak time sessions.

    Organizations that offer PCRF functionality include the major equipment vendors: Alcatel-Lucent, HP Inc., Nokia Networks and Ericsson AB.

    Benefits of PCRF

    1. High performance and reliability

    With its multi-dimensional approach, PCRF is a software component and operates in real-time to take policy decision and charge rules in a network. Its internal units work in load sharing mode which avoids single point failure and ensure reliable performance.

    1. Reduced time-to-market:

    The policy and charging solutions provided by PCRF enables service providers to efficiently manage their differentiated services and pricing models. With its flexible interface extension capability, it helps service providers launch a new service rapidly.

    1. Flexible policy control

    PCRF provides a framework that can implement and control wide range of policies that address subscriber location, address usage, roaming status, rating, subscriber class, and access network type.

    1. Pre-integrated solution with billing, charging system

    Being a single, consolidated database across multiple applications, pre-integrated platform gives a centralized view of the customer and the service.  Thereby, PCRF provides unprecedented flexibility in rolling out and manage services at much faster pace.

    Conclusion

    PCRF enables service providers and operators with highest level of flexibility required to innovate in the era of next generation data services. It supports policy enforcement, service data flow detection and flow-based charging. Offering a comprehensive solution in order to allow a new generation service operator to extend multiple use cases, PCRF allows telecom companies to better control their services and alongside align their revenue with their resources.

    As the service providers shift to LTE, PCRF policy will play a critical role in networking, becoming a strategic component in the race to manage and monetize LTE networks. Thus, to stay ahead in the competition, operators are making PCRF as part of their functioning.

    July 1, 2016 0 comment
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