Comviva optimization wpc final

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SUMMARY
Mobile network operators (MNOs) face a challenging future. Balancing network capacity growth
and revenue growth requires new tools for network operators to improve network efficiency and
performance while 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 a 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.
Intelligent Media Optimization
Strategies for unlocking the full value of media optimization

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 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-to-end 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 ppossible 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-of-concept use cases and avoid any possible collateral brand damage. It
will be a 1-2 year journey.
Figure 1: Intelligent optimization is an evolving process
Source: Ovum
Pitfalls of optimization implemented too broadly and bluntly
Optimization overkill can undercut the MNO’s business strategy
Too many deployed solutions are built on the premise thatthe 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:
 Overinvestment 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. 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:
• Collect/Capture: Pull together all relevant network and subscriber (usage) data.
• Analyse: Correlation and draw insights from data on the network, the subscriber, the location
and the access device.
• Act: 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).

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 plans.
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 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 deployment through pre-integration (less work for the MNO or system integrator to
do at deployment).

Some 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.
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 isin 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
which offers MNOs good levels of flexibility, once the tools are well integrated with
existing systems.
 Network equipment provider (NEP) vendors including 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 cloud-based video optimization
specialist SkyFire. The benefits of partnering with them is their network expertise aids in
integrating 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 specific use
cases will boost confidence in projected returns. Take advantage of vendors' service organizations
to help deploy an 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, and so on, and then 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.
Deploy tools for real-time and predictive network state modelling
Do this intelligently by crowd-sourcing 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
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.

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 www.mahindracomviva.com.
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
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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.

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