http://media2.telecoms.com/e-books/DTVE/magazine/aprmay16/files/20.html
Data is the currency on which TV Everywhere players depend but service providers are facing organisational hurdles to managing the wealth of information at their disposal.
Data is everywhere but it's how you use it that counts. Many service providers are reportedly only scratching the surface of what is possible. Even where there's a will to interrogate data and effect rapid response to consumer needs or business models, many multi channel networks face an uphill task to overcome legacy organisational barriers.
That's in stark contrast to pure-play streamers like Netflix or Amazon which have built a business on integrated customer service and technical operations. Cross-correlation of data sources is part of their culture spanning quality of service (QoS), marketing, advertising and content recommendation pulling in remote control commands to the set-top box sent back over the return-path and server logs recording media player interactions.
With
traditional broadcast TV networks there wasn't a huge need for a lot
of data. The equation could be reduced to quality of content versus
how many people watched. With pay TV the model barely shifted. If
shows were packaged at a decent price the subscription rate went up.
The need for data wasn't great since the variables didn't waver.
When
TiVO introduced the concept of time-shifted consumption the cracks
began to appear. OTT has taken this to another level.
“The
internet opened a floodgate of consumer choice,” says Keith
Zubchevich, chief strategy officer at OTT video optimisation
specialist Conviva.
“It handed control of the TV from networks to viewers. The data
that is needed now is of a fundamentally massive order of difference
compared to what has gone before.”
Data
types
There
are broadly three types of data: consumer
viewing behaviour, programme metadata, and network performance
statistics. The latter
has been a factor in network capacity planning for some time but is
also starting to be used in other areas, such as content acquisition.
“For
a long time QoS monitored bit rates, how many sessions failed.
Increasingly the data is about what devices are being used and how
user behaviour differs device to device. How long are average
sessions and when do they occur?,” says Edgeware's VP
products, Johan Bolin.
For
instance, through insights gained in QoS analytics, a service
provider can understand specific customer preferences and
experiences, and tailor products more accurately. Understanding what
services or content are more popular during certain times of day, in
certain geographies, or certain devices, can give insight into holes
in content offerings. This data can then be used to adjust existing
services, or add or remove suppliers in the ecosystem to better
support the delivery goals.
“A
lot of the focus is still on more technical use-cases but the ability
to get data from end-customer devices is driving additional areas
such as marketing, customer base management, customer care and
service management,” says Per Unell, business development,
Agama Technologies.
“Processes
like fault detection and localisation, network optimization and
change management are very much a reality,” Unell adds. “We also
see significant use in service management such as SLA and overall
service performance tracking. Some customers are also using data to
support their customer care and customer understanding processes.
In
general, it’s more straight-forward to see quantifiable benefits in
operational processes – fixing problems faster, solving customer
issues at first call. At least as much value can be realized by
systematically and proactively tracking down issues before they
become problems, but it’s harder to quantify beforehand.”
Another
component of consumption analytics is device-level data, which is
increasingly important as the number of devices and form factors
grows. “The power of this type of data is granularity,” says AIB
Research in its white paper on the topic
[http://www.conviva.com/conviva-whitepapers/abi-whitepaper/]
published by Conviva.
It
can be used to guide purchasing decisions for service providers,
operators, or content creators. Real-time data can help with quick
monetisation decisions for unexpectedly popular content, and can
guide future investments to identify more popular content, states AIB
Research. Popularity of live events can be more accurately judged,
and licensing and delivery decisions can be centred around this.
“You’re
beginning to look at being able to measure the way that people
interact with a TV system in the same way that they interact with the
internet,” says Andy Hooper vp, Cloud, Solutions & Services,
Arris.
Scratching the surface
“It is relatively early days for the use big data for service providers,” says Peter Docherty, founder and CTO at recommendations engine provider ThinkAnalytics. “Data is already captured but is not being taken advantage of as much as it could be. The risk of not using data to drive the business is a lost opportunity. Let's say only 40% of your VOD catalogue has been watched. If you don't have data you won't know that and if you don't have data about what is watched or being routinely declined then you can do nothing about it.”
It's
not as if operators haven't recognised the need or that solutions are
having no effect. ThinkAnalytics'
research suggests that just a few months after integrating its
recommendations engine, clients saw their subscribers increase their
viewing time by 20-50%, and the number of channels watched rise by
25-35%.
Subscriber
management platform Paywizard says it has delivered acquisition
campaigns that drive conversions up to 25%, and has also run reduce
churn programmes that achieve conversion up to 60%.
The
most recent figures released by Sky from its
500,000-home Sky Viewing Panel show
that channel switching
during Sky AdSmart commercials was 48% lower than for standard
non-targeted
ads
– an effect consistent regardless of channel, household type and
amount of viewing. “As
viewers cannot distinguish AdSmart commercials from any others,
higher viewing levels can only be attributed to customers finding
them more interesting or engaging,” observed Jamie West, Sky
Media’s deputy MD.
Pancrazio
Auteri, CTO at content
personalisation firm ContentWise
explains some of the ways it uses data to assist customers like
Maxdome, Mediaset and Sky Italia. The first is to boost churn
prevention by detecting
anomalies in behaviour.
“If
patterns in behaviour diverge from established patterns this may mean
a user is using a competitive service and can be an early detection
of cancelled subscription,” says Auteri.
ContentWise
data is also used to make service provider promotions more relevant.
“If users are sent irrelevant items then any associated
communication from that service provider will also be seen as
irrelevant so we match the promotion to user habits or tastes.”
Doing so has seen a rise of between 20% to 40% in subscribers opening
(viewing) a promotion, ContentWise claim.
“The
same method can be applied to advertising. If a service provider is
sending promotions of different advertisers we look at the profile of
the user and lifestyle traits and try to narrow down the promotions
while increasing their relevance.”
A
third data usage, dubbed ARPU Rebuilder, will be offered as a module
within the ContentWise Content Personalization system this fall.
Targeting pay
TV skinny bundles it will include a
set of algorithms designed to up-sell related micro-subscriptions to
users and
will focus on the free trial phase of a service and the first month
of subscription to ensure that the new user understands the value of
the content offer.
“Based
on our research the obstacles to large-scale adoption of TV
Everywhere are the lack of awareness from consumers that the content
they are interested in is available, and the fragmentation of the
applications, making it difficult for users to find and seamlessly
consume this content,” says Auteri.
ContentWise
uses viewability tracking to ensure that each user can see a
different, uniquely personalized UI. “Given the fragmentation of
the TVE applications, metrics and KPIs cannot be computed across all
the applications owned by single content providers,” says Auteri.
“The unified discovery provided by the pay TV operator UI is the
best place to measure the user behaviour, across all touch points
(i.e. screens, apps, devices).”
“SMS are a natural collection point of statistical data regarding a pay TV operator's business processes and subscriber community,” says Bhavesh Vaghela, CMO. “This raw data, when given context and viewed against trends, allows marketers to develop and track sales initiatives. Questions like: 'Do our free month offers lead to full subscriptions?' or 'Which device is the most popular for viewing as to impact our application development strategy?' can be answered by a whole host of valuable insights uncovered through reporting and analyses. These answers can help solve short term issues and improve the longer term profitably of a pay TV business model.”
Data
consolidation
Vendors
uniformly contend that from a technical (i/e from their product)
angle, big data collection and analysis is not the issue. The chief
problem is the ability of service providers to handle it.
“We
have all the tools and databases to perform analysis,” says
Docherty. “The challenge is joining the data together from a
business perspective. Service providers are trying to gather data
from different parts of the business but have quite a way to go.
There's a lot of emphasis on the customer acquisition side and on
customer retention / churn reduction programmes but not so much focus
on the stages in between. For example, when you've got an active
customer how do get them to spend more? That's a lot to do with not
having data in one place able to serve a more personalised engagement
with customers.”
“The
main hurdles are about getting a wider understanding across the
operator of what is possible and overcoming organisational
'stove-pipes' in access to data,” agrees Agama's Unell. “It’s
about cherry-picking the most promising use-cases and overcoming the
compartmentalisation of existing systems. If this can be done the
business case is often very positive.”
Arris
reports that a number of operators have built out their own big data
teams offering it as an IT service internally with mixed results.
“This works well in organisations which understand the value that
flows end to end from capturing and analysis to taking action from
big data insights,” says Hooper. “In other organisations,
however, there are too many barriers to collecting and sharing data.
Departmental teams tend to keep data within their own group. It's
classic big organisation type of problem.”
An
example, cited by Hooper, is a company deploying a video monitor
solution into its multiscreen video apps for smartphone and tablet.
“Using that solution they were getting a huge amount of data yet
people in charge of the call centre had no view on when a customer
session failed. Despite the operator paying a licence to this vendor
it had no oversight on the poor customer experience arising out of
buffering video. Either through inefficiency or deliberate
obstruction, that information was not being leveraged end to end
across the business.”
As
Hooper sees it, the customer's experience with a service provider now
spans traditional data silos from the broadband call centre, to the
TV call centre to operational teams examining video player and
session data to digital marketing teams interrogating intent to
purchase.
“This
is not being done at anywhere near enough scale,” he says.
“Customer experience management crosses organisational boundaries.
Some service providers are addressing this by installing a chief
digital officer or customer experience executive, even at board
level, but they need to to do more.”
Big
data is an established IT discipline in many industries but most
telco or cable companies retain a legacy of network / operations
teams separate from marketing and consumer facing departments.
Hooper
points to home network management as another area barely addressed by
the pay TV operator. “Trouble shooting of this falls on the
responsibility of the service provider, explicitly or implicitly,”
he says. “It includes management of the home network, whether
there's good home WiFi, whether the kids are moaning at Dad because
he can't download game updates. All these things are part of the
subscriber experience and typically they will end up talking to
different bits of the service provider organisation when it should be
one entire customer experience journey and one digital strategy with
big data enabled to deliver insight into this.”
Sharing
data sets and personalising offers are hampered by limitations on
content rights. “Targeted advertising is still in its early phases,
with just a few operators using it in production,” says Unell. “It
also requires additional infrastructure and generates privacy and
personal integrity requirements on the solutions used.”
“Video
professionals are struggling to adapt to new business models and must
use data analytics to manage and grow their services,” concludes
Sam Rosen, vp, consumer at ABI Research. “Leveraging a single,
unified dataset for the needs of different functions in an
organisation—and opening up the avenues for data sharing between
affiliates jointly responsible for a video service—can help align
everyone on a common definition of success.”
Edgeware's
Bolin believes a single repository is desirable, and perhaps
possible, but not soon. “Given
that there are so many different systems and sources of data it would
be a challenge to have a centralised data store continuously updated
and compliant but as the market matures and business intelligence
advances I wouldn't rule it out.”
“You
can spend more time crunching data than analysing it,” agrees
Zubchevich.
“I don't think we'll get to a unified single data service.
Publishers and pay TV operators have to break data into chunks and
look for key providers of, for example purchase data, advertising and
content recommendation.” Naturally, anything to do with the
playback subscriber experience should be measured by Conviva, he
says.
“Failure
to do means churn and subscriber loss,” he adds. “The simple fact
is that consumers are polling publishers online. The consumer will
terminate their relationship with a network if that service provider
is not proactive.”
Insight
into Ad fatigue
Buffering
and delivery analysis should also be extended to ads. There is
evidence from Conviva that as much as a 58% viewer churn is based on
poor online ad experiences.
“The
impact on a viewer's experience from ads is massive,” says
Zubchevich. “If I watch a show and it's riddled with ads I'm
getting ad fatigue and I'm beginning to look for content elsewhere.
The fundamental next step for service providers is to monitor ad
impact.”
Not
coincidentally, Conviva is launching an Ad Insight product which
essentially expands the capabilities of its video playback experience
monitoring.
“Service
providers could do more in this area,” agrees Bolin. “Today, very
little is being done partly due to data being sourced from two
different parts of the business. There is data from the ad insertion
server about the actions of customers interacting with an ad, and
data from the ad streaming server about the rendering of that ad.
There is a need to cross-corrolate this data. As ad personalisation
(targeting) grows I would assume service providers would see these
reports as much more of a requirement.”
Acting
on Quality of Experience
A
study published in March by IneoQuest found that more than half of
consumers who watch streaming video have experienced rage as a result
of buffering. Buffer Rage is defined as “a state of uncontrollable
fury or violent anger induced by the delayed or interrupted enjoyment
of streaming video content from OTT services.”
It's
no laughing matter. With cord cutting on the rise, and nearly three
out of every four consumers watching streaming video daily a better
understanding of the implications of Buffer Rage is essential,
IneoQuest argues.
The
contention is that metrics from QoS (such as packet loss and delay)
can be used beyond an interpretation of the performance of network
and services to account for the experience of the user. Accounting
for subjective user experience with objective data is where Quality
of Experience (QoE) comes into play.
“Tracking
packet loss can highlight problems in a network, and these problems
can be extrapolated to possible user experience difficulties;
however, there are a few issues with this,” suggest ABI Research.
“Not all service difficulties always lead to user experience
degradation, users vary in their tolerance for network problems, and
different content types (i.e., short-form video versus long-form
video) are affected more by network difficulties.”
Logging
a consistent stream of data—such as stream completion or early
exit—and correlating this to available user data creates a powerful
combination that allows for deeper and more personal data dives.
Combining this data stream with dashboards to help with specific
content filtering, such as geographic location, content source, user
ISP, and more, helps real-time analysis and subsequent decision
making.
According
to vendors, however, most companies are primarily reactive and do not
have the resources nor the time to proactively look for potential
issues that might impact customers.
“What
service providers need to do is to proactively poll the
infrastructure and network that impact customer experience and match
that performance information against fixed, dynamic and baseline
thresholds as well as configurable SLAs to identify potential issues
that will impact customer experience before the customer is
affected,” says Gregg Hara, VP business development and marketing,
Centrica Systems.
Centina's
NetOmnia Cable Assurance is one tool that can provide this
functionality. It polls all customer premise devices at a high
frequency as well as polling all network devices then correlates this
information in realtime against the network to identify performance
and customer experience impacting issues.
“This
can then automatically kick of a work order or trouble ticket to
initiate a truck roll and get a tech onsite to resolve the problem
before the customer even notices an issue,” Hara explains.
Hooper
explains that Arris has systems in the field that monitor and manage
the delivery of bits on the network layer. These can spot when, for
instance, a customer’s QoE has dropped due to a network outage, and
proactively schedule resources to fix the problem. This is important
because viewers are increasingly impatient when QoE deteriorates and
ever more likely to vote with their feet.
In
its OTT: Beyond Entertainment Survey published last November, Conviva
concluded
that one in five viewers will abandon poor experiences immediately,
regardless of genre and that 90% of viewers choose to return to
services that deliver a superior experience. After a poor experience,
one in five will never return to that service.
This
leads Conviva's Zubchevich
to conclude that content is no longer king. “For the first time you
can forget quality of content as being the single most valuable
metric. The number one currency is QoE. We are just starting to see
operators look at marketing the experience of viewing as important as
the content itself. This is not something they've ever collected in
the past and it's a fundamental shift.”
Publishers
still care about the quality of content of course. Zubchevich's
contention is that with so many sources to which a consumer can turn
to get the same content, the defining factor will be the experience
they receive from the site serving it.
“We
need to redefine QoS from basics such as 'is the stream available at
all?' and 'can I watch it largely uninterrupted? to questions about
resolution. If I watch a HD or UHD TV broadcast and move to an
IP-based provider I am not expecting a poor experience in comparison.
We need a zero tolerance approach to starting the stream. What used
to be acceptable is no longer and failure to address buffering or
bitrrate issues in that moment means you lose the consumer.”
Arris'
Hooper suggests that the mantra 'content is king' has been a fallback
excuse for some service providers. “As the market fragments into
different content sources you'll find that consumers will gravitate
over time to the site where they're having least friction. That means
pay TV doesn't just have their business threatened by OTT but by new
data pipes which can provide a greater customer experience journey
through the content lifecycle. Having exclusive content deals is a
defensive mechanism. Enabling a better customer experience will
deliver more positive brand benefits in the longer term.”
Freesat expands analysis
Freesat,
the BBC and ITV hybrid satellite and broadband platform, launched its
Freetime
box in September
2012 with a December
2015 software update enabling real-time measurement and monitoring
capabilities.
It
was subsequently able to take advantage of solutions from TVbeat for
realtime audience measurement of viewers who opt-in to allow
collection of their data. Freesat also uses Google Analytics to
monitor how its app, guide and content is used.
“One
insight [from Google Analytics] was how much numeric entry is used as
a shortcut for channel entry,” explains Matthew
Huntington,
Chief Technology Officer.
“Such analytics
can inform how production of channel numbers are used in marketing
material. It shows that we need to protect and keep a channel’s
number stable. It also informs us that we should not be developing a
remote control without number keys at this time and deter TV
manufacturers from only supplying numberless remote controls.”
Another
insight was around the service's Now
& Next view. “Customer research groups had suggested this was a
popular feature but extrapolating that to the whole of our install
base is a risky jump,” he says. “We were gratified to learn that
the
vast majority of EPG usage is indeed with the first page of that 'now
& next' information.”
Freesat
has yet to put in place a mechanism to monitor the quality of video.
“We would like to have done that but we're not yet able to get that
information,” says Huntington. “It would be useful to have more
insight into signal strength, for example, so if a customer complains
we can take a look specifically at that aspect. Our OTT service is
provided by third parties, like BBC iPlayer or Netflix, which perform
their own QoE monitoring. As yet we've not been able to get inside
those players from a QoE or content usage perspective to extract
information and do cross platform analysis. Over time we hope our
partnership will develop so that they will share that data to the
mutual benefit of our platform and their service.”
Huntingdon
feels Freesat have only scratched the surface of the insight it can
derive from the data it already collects.
“We
are wary of the bottlenecks of trust that occur in organisations when
only a few people can access key data and data gets locked into an
ivory tower,” he says. “We have taken a democratic approach to
exposing data in our company. The next step is to use that data more
effectively to find insight or solve a problem.”
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