Intelligent Mobile Network Media Optimization. Strategies for unlocking the full value of content optimization

Transcription

1 Intelligent Mobile Network Media Optimization Strategies for unlocking the full value of content optimization

2 Contents 1. Summary 3 The Current State of Content Optimization Where are MNOs today and why? 2. Pitfalls of optimization implemented too broadly and blunt 5 3. Intelligent Optimization Models 6 Intelligent optimization is an evolving process 4. The benefits of intelligent optimization 7 5. Implementation challenges of optimization Product architectures are shifting 8 6. Device and cloud-based solutions 9 7. Device and cloud-based solutions Recommendations for mobile network operators Clear business rules and policies are the starting point for a successful optimization strategy Reduce risk through proof-of-concept tests based on use cases Appendix 12 About Mahindra Comviva Ovum Consulting Disclaimer 2

3 SUMMARY Mobile network operators (MNOs) face a challenging future. Balancing network capacity creating new revenue opportunities. Media/ content optimization (MO) is one tool that can help, but most MNOs have thus far deployed optimization as a blunt instrument for capex containment. A more intelligent approach is possible, one that can improve customer experience and service personalization based on real-time invocation of business rules and policies. MO helps MNOs harness the flood of data traffic, especially video, through minimizing network investments, improving customer experience, and personalizing services. Unfortunately few MNOs have yet implemented optimization in full support of business goals beyond basic traffic reduction, not only missing the revenue opportunities it can offer but even negatively affecting revenues. Intelligent, selective optimization of content based on rules and policies invoked in real time can both reduce investment costs and provide tool to raise revenues. Intelligent MO that supports customers and a spectrum of MNO stakeholders from operations to marketing and one that is flexible enough to evolve over a carrier s technology lifecycle (e.g., from 2G to 3G to 4G and from physical to virtual and cloud-based) provides a much better path moving forward. Ovum sees this as part of a network evolutionary process that will require hooks between the end-user device, network, optimization, data analytics, and network policy and rules repositories and enforcement functions. The Current State of Content Optimization Where are MNOs today and why? Content optimization techniques in use today range from basic compression and transrating, to more elaborate traffic shaping and caching (especially useful for video, but it can be applied to all types of web and image traffic). These optimization techniques generally reduce the time for a video to start and eliminate external network fluctuations that sometimes cause videos to stall. They also speed up the time for the video to pick up when jumping forward in the video. The cache responds to the video request much faster than a remote location. The end result is a much smoother video that starts faster. MNOs are not currently using session shifting (whereby a session can be started on one device and completed on another), but they do aspire to provide that feature in the future. Currently, optimization is applied principally to save network capex, and is often applied blanket fashion because there has been a lack of real-time congestion detection tools which alert operators to the state of the network at a per customer, per location level. Moreover, these 3

4 tools need to be deployed deep into the network at each of the radio and core network interfaces. This is a cost-intensive proposition and without supporting use cases such as policy-based optimization, the ROI must be considered as questionable. Ovum s research suggests that media optimization strategies and deployments evolve in three basic phases, as depicted in Figure 1. MNOs already have basics in place (which deals with HTTP content optimization and caching) and now need to adopt more sophisticated approached. In Phase 1, which spans basic "always on" to more sophisticated approaches, the business goal is reducing or delaying capex by reducing traffic. Optimization is applied according to the content and device type, without any additional intelligence (such as network type, network state, the cell location, the customer s profile and tariff plan - arguably only unlimited plans can be optimized without any negative impact on revenue). Phase 2 focuses on managing traffic to improve customer experience (CE) and satisfaction and, therefore, reduce churn and other negative business consequences. A key to improving the quality of customer experience is determining when optimal conditions occur through historical analysis, and then setting this as the goal baseline. This calls for an investment in software tools. For example, there are tools emerging that help operators bridge the gaps between networks, services, and customers using an automated customer experience management center. This provides quality indices that can be used to monitor the end-toend quality of different services. Phase 3 adds a focus on more personalized, real-time service offers to increase ARPU. By applying optimization and acceleration techniques during peak hours or when the network is congested, it becomes possible to deliver differentiated service to defined premium subscribers. In the field, our research indicates that the goals of many optimization deployments are still at Phase 1 (reduce traffic 20% or more, typically), with some progression to Phase 2 goals. Indeed, Phase 1 and 2 goals tend to overlap and intertwine, as MNOs can "dial in" a balance of traffic reduction and customer experience improvement based on how they "tune" MO across the network. Although MNOs can see the benefits of moving to Phase 3, they are proceeding deliberately to test proof-ofconcept use cases and avoid any possible collateral brand damage. It will be a 1-2 year journey 4

5 Figure 1: Intelligent optimization is an evolving process Phase 3 Business impact opportunity Phase 2 Quality-of-experience improvememtn Goal: Better overall customer satisfaction and, e.g., less churn Implementation spans from making sure Phase 1 everyone gets their fair share of capacity in a congestion situation to policy-aware action (e.g. customer-specific actions in congestion situations) Traffic reduction Goal: Capex Containment Implementation spans form always on to optimization driven by policy based on location/user/content/deivce-awareness Personalized services Goal: Better monetization Requires location/user/content/deviceawareness and integration with PCRF/PCC for realtime, flexible offers Deployment sophistication Source: Ovum Pitfalls of optimization implemented too broadly and blunt Optimization overkill can unmno s business strategydercut the Too many deployed solutions are built on the premise that the operator needs to optimize all content with the goal of freeing up X% (usually 20-30%) of network capacity. Ovum believes this simple approach can actually undermine the operator s business plan in two ways: Over investment in optimization technology. Trying to optimize all network content, and in particular video, can lead to over investment in network optimization solutions and divert capex investment from areas where it can be better spent. Additionally, some large Internet content providers are starting to provide their own optimization, such as converting a video stream from high-definition to standard-definition based on network conditions, albeit with no link to a subscriber or his/her service plan. Any over-optimization can divert capex investment from areas where it can be better spent. Negative impact on data revenues. Optimizing all content regardless of network conditions or subscriber situations can hurt an operator s ability to fully monetize its mobile broadband network. For example, if an operator reduces all traffic by 20% the end-user might be able to suffice on a monthly data service of 1GB. 5

6 However, if optimization was more selectively used the end-user could have opted for a more expensive data service plan. Finally, basing an optimization strategy purely on freeing up a specific percent of network capacity is shortsighted and unrealistic. From Ovum s discussions with mobile operators any capacity made available by way of optimization will be eventually consumed, either due to the bigger roads just invite more cars phenomenon or because savvy operators actively choose to allocate some capacity to improve customer experience. INTELLIGENT OPTIMIZATION MODELS Intelligent optimization is an evolving process Moving from simple media optimization to a more intelligent implementation requires that MNOs develop business policies and rules and invest in Policy and Charging Rules Function (PCRF) and related capabilities to enable more sophisticated data services. This evolution won t happen all at once and it can t be driven only by the network division: optimization will lead to more individualized services and therefore higher ARPUs only if the operator s marketing division is engaged. Ultimately Ovum sees optimization as part of the closed loop illustrated in Figure 2. In this loop optimization would have a role in all three areas collect or capture data, analyze data, and act on data and could work together with other elements and support systems to provide a differentiated service. The three elements in the close loop model constitute Colllect/Capture: to pull together all relevant network and subscriber (usage) data. Analyse: to correlation and draw insights from data on the network, the subscriber, the location and the access device. Act: to create policies for the PCRF, which includes details on location-based traffic policies, customer spend-based policies, and more traditional parameters such as device, time, volume, etc. Ultimately, optimization will become intelligent enough to distinguish a full range of parameters, so that it is user aware (and takes account of a customer s profile and their current plan), device aware, network and network congestion aware, and service aware (to take account of different application, protocols, and content types). 6

7 Figure 2: "Closed loop" allows better match of revenue and performance Source: Ovum The benefits of intelligent optimization Rather than employ always on, optimize everything approaches, intelligent network content optimization works on several levels. It combines traffic steering with real-time network conditions and network and subscriber rules and policy inputs in a closed loop system to support specific business goals beyond simple traffic reduction. With traffic steering, instead of all content automatically being optimized, based on rules and policies that determine when and what kind of traffic is optimized, specific traffic is routed to the optimization element. Through intelligent traffic steering, the operator can limit its overall investment in content optimization and protect the value of its data service pla The rules and policies used to properly steer traffic can be fully contained in the optimization solution or can be tied to other data and rules depositories such as a PCRF. Decisions on traffic steering can be limited to the state of the network or include subscriber information, such as class of service, to guide steering decisions. Device-based software clients fill an important role in providing real-time customer experience KPIs to the operator that can help set rules and policies and even reduce drive test costs. With intelligent optimization an operator has the ability to target network congestion relief at different parts of the network during different times of the day. For example, it can apply optimization to content heading towards the city center during peak work hours and redirect optimization towards the suburbs 7

8 after work. The optimization follows the users rather than be applied to all areas even during low-usage periods. Operators can also use intelligent optimization as a tool for supporting different class of customers. Some customers may be willing to pay more to guarantee a better customer experience for sports content, for example; the MNO can build an offer around this user segment. MNOs could also use intelligent content optimization to offer content providers service level agreements (SLAs) around video delivery. Implementation challenges of optimization Currently the operation of MNO organizations is heavily siloed and to ensure the shift from a network centric approach to a more customer centric approach requires bringing together different groups within the company, including marketing and customer service. These other organizations may not be knowledgeable about media optimization or used to working so closely together. Still, these hurdles are worth tackling: customer requirements will become a primary focus. Ultimately, this will ensure the success of the closed-loop model and provide a boost to the MNO s top and bottom lines. Product architectures are shifting Products can be architected as mobile optimization solutions, as single-function products, or as part of a more broadly integrated solution. The optimization function can be located in a variety of places in the network. There are two broad architectural variants in the market. Single-function point product deployments for example, where deep packet inspection (DPI) and optimization capabilities are separate and not integrated will be relegated to the past. Longer-term trends favor virtualized, cloud based approaches and commercial off-the-shelf hardware. Until that time, integrated solutions where multiple, modular optimization-related tools are integrated into a single box or blade server provide some advantages, including: Lower latency and improved QoE through less need to steer video between multiple nodes. Procurement simplicity (a single product to purchase). Faster deploym ent through pre-integration (less work for the MNO or system integrator to do at deployment). OSS/BSS, and analytics engines. 8

9 good levels of flexibilisome mobile network equipment vendors integrate capabilities right into core elements, for example gateway general packet radio service (GPRS) support node (GGSNs) or packet gateways (PGWs). To make the most out of the business opportunities, as shown in Figure 1 s Phase 3, the ultimate integration of optimization is with other network functions, including PCRF, OSS/BSS, and analytics engines. try, once the tools are well integrated with existing systems. Network equipment provider (NEP) vendors: including the likes of Ericsson, Huawei, ZTE, NSN and Alcatel Lucent who are collaborating with specialists for select components, e.g. Ericsson is partnering with Vantrix, Alcatel Lucent with the cloud-based video optimization specialist of SkyFire. The benefits of partnering with them is that because the so intimate with the network, it should be easier to integrate the necessary features. They may not be as agile as specialist providers, however, in addressing customization requirements. Non-NEPs: like Comviva or Allot who are offering optimization solutions that are focused on a limited set of areas. Total cost of ownership, flexibility to customization needs, and speed to market are their strengths. Integration challenges may remain and the depth of network expertise of some vendors could pose challenges for operators. Recommendations for mobile network operators Clear business rules and policies are the starting point for a successful optimization strategy Optimization products are increasingly capable through integration with rules and policy engines, traffic steering elements, caching functions, network probes, and other functions. But to truly benefit from these capabilities MNO stakeholders from multiple departments from operations to marketing and customer support must work together to establish business goals and priorities and provide the best balance between capex efficiency, customer experience, and revenue creation. Vendors can help educate MNO stakeholders on what s possible through case studies. Reduce risk through proof-of-concept tests based on use cases Once business goals are established, focusing on and testing a small number of 10

10 Finally, optimization functions can be centralized or decentralized and located as: Mobile core solutions upstream from or integrated with the GGSN. Cloud-based solutions in data centers. Network edge solutions between the radio access network (RAN) and mobile core. Device-based capabilities. There is no one right architecture or location - each mobile network operator will need to determine the best mix of capabilities in partnership with its vendors. Device and cloud-based solutions Ovum does foresee increased popularity of device-based and cloud-based solutions for better service personalization and scaling, respectively. Specifically, we anticipate the following developments: Client-based software agents: More processing and intelligence is likely to move to the device, including caching, decryption, pre-fetching of content, policy enforcement, and so on. Client-based software can also provide content source information on the state of the network and initiate changes in coding and rating. On-device monitoring agents: On-device monitoring agents aggregate information on the network as experienced by the customer and relay information to the network. MNOs can make use of this information to apply the best optimization policy. These on-device monitoring agents are less expensive than network probes. Essentially the operator is in effect able to crowd source information from customers on the state of the network. Session shifting: These capabilities will enable new mobile operator services. This would require a client application and a GUI-based menu of content. Video could be started on one device and continued on another. Time shifting: This will allow downloading of content during off-peak hours. Subscribers could be prompted for whether video downloads could be delayed in exchange for a lower price or other incentive. Moore's Law in action: As processors get more powerful, more functions will be added to integrated systems and more systems will be based on COTS hardware. It s important that MNOs query prospective vendors regarding future development to make sure capabilities will evolve in line with their own plans. These vendors fall into three broad categories: Specialists: including video, policy and caching specialists like Vantrix, Openet, PeerApp, etc. The appeal of these as vendors is that they are designing leading edge technology in which offer MNOs 9

11 specific use cases will boost confidence in projected returns. Take advantage of vendors' service organizations to help deploy an so on, and integrated solution based on specific use cases don't just deploy a set of boxes. Set specific KPI targets to meet business goals Setting specific targets will help reinforce business goals and monitor results. For example, if the desired outcome is better customer experience in congestion situations, set specific targets for transactions per second, MOS scores (mean opinion score, a measure of video quality), download or page display stats, andthen measure them as objectively as possible. In this context, there is a genuine need for a cross-functional organizational model involving the office of the CTO, the CIO, CMO/CSD, and with clearly defined KPIs. The key is to focus on KPIs that matter to the customer, not just network-centric parameters. Device agents that report real-time KPIs can help fine-tune policies and rules based on network, location, subscriber category, device, application, and congestion. this information from users. Be honest with your customers, and don't assume you know best In a congestion situation, fairness will go a long way. Be as transparent as possible with customers about what you are doing and why. Give customers choices (e.g. to display video in standard definition vs. high definition) and a clear sense of the benefits. A clear opt-in choice is better than an obscure opt-out or a one-size-fits-all automated action. You might even want to consider what it would take to give customers direct control over invoking optimization. Make sure your optimization vendor s roadmap includes new architecture models for better scalability Scaling conventional architectures can be challenging. As a result, many of the vendors are evolving products for virtualized and cloud-based architectures. Some vendors are pushing an network functions virtualization/software-defined networking (NFV/SDN) approach to traffic steering too. Press vendors for their roadmaps to make sure their solutions will evolve with your needs. Deploy tools for real-time and predictive network state modelling Do this intelligently by crowd-sourcing 11

12 Appendix This white paper was researched, authored and produced by Ovum in association with Mahindra Comviva, as part of a series of papers assessing the current state and future market direction of mobile broadband services for mobile operators. About Mahindra Comviva Mahindra Comviva is the global leader in providing mobility solutions. It is a subsidiary of Tech Mahindra and a part of the USD 16.7 billion Mahindra Group. With an extensive portfolio spanning mobile finance, content, infotainment, messaging and mobile data solutions, Mahindra Comviva enables service providers to enhance customer experience, rationalize costs and accelerate revenue growth. Its mobility solutions are deployed by 130 mobile service providers and financial institutions in 90 plus countries, transforming the lives of over a billion people across the world. For more information, please visit Ovum Consulting Ovum has an enviable and hard-earned reputation as a provider of telecoms consulting services. Our consulting customers tell us that, above all else, it is Ovum's industry knowledge and attention to detail that puts us ahead of our competitors. This is directly related to the expertise of our consultants and analysts, and the project and research methodologies we use. We work across the globe with business leaders of telecoms operators, service providers and ICT vendors and with investment banks, governments and industry regulators. We hope that this analysis will help you make informed and imaginative business decisions. If you have further requirements, Ovum s consulting team may be able to help you. For more information about Ovum s consulting capabilities, please contact us directly at consulting@ovum.com. Disclaimer All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of the publisher, Ovum (an Informa business). The facts of this report are believed to be correct at the time of publication but cannot be guaranteed. Please note that the findings, conclusions, and recommendations that Ovum delivers will be based on information gathered in good faith from both primary and secondary sources, whose accuracy we are not always in a position to guarantee. As such Ovum can accept no liability whatever for actions taken based on any information that may subsequently prove to be incorrect.