Transcript of a BriefingsDirect podcast on how service providers are harnessing the power of data analytics to improve customer service and customer relations.

1. Service Providers Gain New Levels of Actionable Customer
Intelligence from Big Data Analytics
Transcript of a BriefingsDirect podcast on how service providers are harnessing the power of
data analytics to improve customer service and customer relations.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Dana Gardner: Hello, and welcome to the next edition of the HP Big Data Podcast Series. I’m
Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing
sponsored discussion on how data is and used to enrich the way you live and
work.
Once again, we’re showcasing how companies and industries worldwide are
capturing myriad knowledge, gaining ever deeper analysis, and rapidly and
securely making those insights available to more people on their own terms.
Our next Big Data innovation discussion highlights how the
telecommunication service-provider industry is gaining new business analytic
value and strategic return through the better use and refinement of their Big Data assets. To learn
more about how Big Data analytics has become a business imperative for communication service
providers – or CSPs for short -- please join me now in welcoming our guest.
We are here with Oded Ringer, Worldwide Solution Enablement Lead for HP Communication
and Media Solution. Welcome, Oded.
Oded Ringer: Hi, Dana, thanks for bringing me in. It's great being here.
Gardner: Glad to have you. What are some of the major trends and even competitive pressures
that are leading CSPs to now view themselves as being more data-driven organizations?
Ringer: It’s not a secret that CSPs are under a lot of pressure. On one hand, this industry has
never been more central than today. Everybody is connected, spending so much
more time online than ever before, and carrying with them small devices
through which they connect to the network. So CSPs are central to our work and
personal lives – as a result, they’re under lot of pressure.
They’re under a lot of pressure, because they’re required to make massive
investments in the networks, but they also need to deal with shrinking margins
and revenues to subsidize these investments. So, at the end of the day, they’re
squeezed between these two motions.
Gardner
Ringer

2. One approach many CSPs have adopted in the last year was to reduce cost and to cut operations.
But this is pretty much a trip to nowhere. Going into most basic services and commodity services
is no way for these kinds of things to survive.
In the last two to three years, more and more traditional operators understand that they must go
beyond what they did before. They need to offer more compelling services to reduce churn and
acquire new customers. They need to leverage their position as a central place between
consumers and what they are looking for to become some kind of brokers of information.
The key asset they have in their hand to become such brokers is the huge amount of information
that they maintain. It’s exactly where analytics comes into play.
Talking about mobile
Gardner: When we say CSP and telecommunication companies these days, we’re more and
more talking about mobile, right? How big a shift has mobile been in terms of the need to
analyze use patterns and get to know what's really happening out in the network?
Ringer: Mobile services are certainly the leading tool in most operator’s arsenals. Operators that
have the subscriber “connected” with them wherever they go, around the clock,
have an advantage over those that are more dependent upon or only provide
tethered services.
But we need to keep in mind that there’s also a whole space for analytics
solutions that are related to fixed-line services, like cable, satellite, broadband,
and other, landline services. CSPs are investing a lot in becoming more predictive, finding out
what the subscriber really wants, what the quality of those services are at any given time, and
how we can reduce churn in their customer base.
Another kind of analytics practices that operators take is trying to be predictive in their
investments in the network, understanding which network segments are used by more high-worth
individuals, those that they do want to improve service to, beefing up those networks and not the
other networks.
Again, it’s these mobile operators who are on the front lines of doing more with subscriber data
and information in general, but it is also true for cable operators and pay-TV operators, and
landline CSPs.
Gardner: Oded, what are some of the data challenges that are specific to CSPs. We know of
course that Big Data is an issue associated with rapidly increasing velocity, volume, and variety
of data for just about any organization, but is there something specific about the Big Data
challenge in order to get these all important analytics that’s specific to CSP?

3. Ringer: In the CSP industry, Big Data is bigger than in any other industry. Bigger, first of all, in
volume. There is no other industry that runs this amount of data – if you take into consideration
they’re carrying everybody’s data, consumer and enterprise. But that’s one aspect and is not even
the most complicated one.
The more complicated thing is the fact that CSPs, unlike most enterprises, need to handle not
only the structured data that’s coming from databases and so on, but also unstructured data, such
as web communication, voice communication, and video content. They want to analyze all those
things, and this requires analyzing unstructured data.
So that’s a significant change in that type of process flow. They are also facing the need to look at
new sets of structured data, data from IT management and security log files, from sensors and
end-point mobile device telematics, cable set-top boxes, etc.
And two, in the CSP industry, because everything is coming from the wire, there’s no such thing
as off-line analytics or batch analytics. Everything needs to be real-time analytics. Of course, this
doesn’t mean that there will not be off-line or batch analytics but even these are becoming more
complex and span many more data sets across multiple enterprise silos.
More real time
If you analyze subscriber behavior right now and you want to make an offer to improve the
experience that he’s having in real time, you need to capture the degradation of service right now
and correlate it with what you know about the subscriber right now. So it's so much more real
time than in any other industry.
In short, CSP Big Data analytics is Big Data analytics on steroids.
Gardner: Of course, all these different types of media, information, and data need to be
associated in order to get those bigger analytic payoffs. That is to say, having separate pools of
analysis isn’t as valuable as analyzing them all together. How do they start to pull together data
and assets that up until now they really didn’t try to join or analyze in conjunction or in
association with one another. How did they bring it together?
Ringer: There are different data sources or information sources, and it makes no sense to
consolidate everything in one database, because it's endless, and most of the existing databases
are limited in purpose and scale to their existing databases – not to mention the exponential
increase in governance problems associated with wholesale transfer from existing siloed data
stewards to a single consolidated repository.
The idea is that we need to have good tools of mediation and collection of data from different
places, collecting them in a staging database for trend analysis over time and connecting them
via event triggering for real-time analytics. So the sources of information remain separate and
many times, isolated.

4. We’re not talking here about projects of data consolidation. It may be necessary in some cases,
but that’s not really the practice that we’re talking about here. We’re talking about federating,
referring to external information, analyzing in the context of the logic that we want to apply, and
making real-time decisions.
Gardner: We've outlined some of the issues and challenges that are specific to CSPs. We
recognize that this is extremely important to how they conduct their business, how they make
their investments and how they satisfy and engage their customers.
What does a long-term solution look like, rather than cherry picking against some of these
analytics requirements? Is there a more strategic overview approach that would pay off longer
term and put these organizations in a better position as they know more and more requirements
will be coming their way?
Ringer: Actually we see two kinds of behaviors. The market is still young. So it's very hard to
say which one will be more dominant. We see some CSPs that are coming to us with a very clear
idea on what business process they want to implement and how they believe a data-driven
approach can be applied to it.
They have clear model, a clear return on investment (ROI) and they want to go for it and
implement it. Of course, they need the technology, the processes, and the business projects, but
their focus is pretty much on a single use case or a variety of use cases that are interrelated.
That’s one trend.
There’s another trend in which operators say they need to start looking at their data as an asset,
as an area that they want to centralize. They want to control it in a productive manner, both for
security, for privacy, and for the ability to leverage it to different purposes.
Central asset
Those will typically come with a roadmap of different implementations that they would like to
do via this Big Data facility that they have in mind and want to implement. But what’s more
important for them is not the quickest time to launch specific processes, but to start treating the
data as a central asset and to start building a business plan around it.
I guess both trends will continue for quite some while, but we see them both in the market
sometimes even in the same company in different organizations.
Gardner: Let's look at what harnessing Big Data can bring to an organization, whether they do it
tactically or strategically. It seems to me that the business case for this is simply getting more and
more pronounced and more powerful.

5. Let’s look at some examples. There’s a new retail model called smart shopping that takes the
advantage of geolocation in the mobile tier. There’s electronic fraud detection and prevention,
where they can help people protect themselves and do more commerce to gain trust on their
networks.
There are marketing benefits that can be brought back to providers of services, sellers that want
to engage. And of course, it's important to track all the use patterns along the way for all of these
and be able to make that data available to as many parties as possible. Tell me more about why
this is essential and not something that’s likely to go away or even diminish any time soon?
Ringer: First of all, it’s essential because operators realize that they need to use the data to
differentiate themselves, be more relevant to the subscribers, and to be more proactive in their
behavior. They can’t continue to be a dumb pipe. They realize that. That’s clear to everybody.
It's interesting that you mentioned those areas. Some are very similar to the way we also define
this space that we’re active in. You mentioned the implementation of smart shopper, which is
something that we actually did with a large North American operator in collaboration with a
chain of malls in North America.
Gardner: When we think about these really important business imperatives and how a CSP can
really change their identity from being a pipe, a conduit, to being more of a rich services
provider on top of communications, I can see why they’re really putting a lot of emphasis on
this.
What is it that HP is bringing to the table? What is it about HP HAVEn, in particular, that is well
suited to where the telecommunications industry is going and what the requirements are?
Ringer: HP has made huge investments in the space of Big Data in general and analytics in
particular, both in-house developments, multiple products, as well as acquisitions of external
assets.
Complete platform
HAVEn is now the complete platform that includes multiple best-in-class product elements
based multiple, cutting edge yet proven technologies, for exploiting Big Data and analytics. Our
solution for the space is pretty much based on HAVEn and expanded with specific solutions for
CSP needs, with a wide gallery of connectors for external data sources that exist within the CSP
space.
In short, we’re taking HAVEn and using it for the CSP industry with lots of knowledge about
what traditional CSP operators need to become next-generation CSPs. Why?
Because we have a very large group within HP of telecom experts who interact with and leverage
what we’re doing in other industries and with many of the new age service providers like the

6. Amazons, Googles, Facebook and Twitters of the world. We go a long way back in expertise in
telecom but combine this with forward thinking customers and our internal visionaries in HP
Labs and across our business units.
Gardner: Just to be clear for our audience, HAVEn translates to Hadoop, Autonomy, Vertica,
and Enterprise Security, along with a whole suite of horizontally and vertically integrated set of
applications that are vertical industry specific. Is that right?
Ringer: Exactly.
Gardner: Tell me what you do in terms of how you reach out to communications organizations.
Is there something about meeting them at the hardware level and then alerting them to what these
other Big Data capabilities are? Is this a cross-discipline type of approach? How do you actually
integrate HP services and then take that and engage with these CSPs?
Ringer: Those things exist, like engaging at a hardware level, but those are the less common go-
to-market motions that we see. The more popular ones are more top-down, in the sense that we
are meeting with business stakeholders who wants to know how to leverage Big Data and
analytics to improve their business.
They don’t care about the data other than how it’s going to be result in actionable intelligence.
So, at the CSP level, it can be with marketing officers within the CSP who are looking to create
more personalized services or more sticky services to increase the attention of their subscribers.
They’re looking to analytics for that.
It can be with business-development managers within the CSP organization that are looking to
create models of collaboration with the Yahoos and Facebooks of the world, with retailers, or
with any kind of other participants of their ecosystem where they can bring the ability to provide
the pipe, back-end hosting of services and intelligence about how the pipe is providing the
services and the sentiment of the customers on the other end of the pipe.
They want to share information of value to their customers, making them dependent on them in
new ways that aren’t just about the pipe thereby gaining new revenue streams. That’s the kind of
motivation they have. It can be with IT folks as well, but at the end of the day the discussion
about CSP Big Data isn’t coming from the technology. It’s coming from the business people that
understand that they need to do something with the data and monetize it.
Then, of course, it becomes pretty quickly a technical discussion that the motion is business to
technology, rather than infrastructure to technology.

7. Support practice
We also developed the support practice within our organization that does exactly that,
business advisory workshops. It’s for stakeholders of different roles to realize what the priorities
are in using Big Data. What is the roadmap that they want to implement?
The purpose of this exercise is to quickly bring everybody to the same room, sit together for a
day or two, and come out with an agreement on how to turn themselves from conventional
services to more personalized services and diversify the business channels via using information
data.
Gardner: Let’s go to some examples to demonstrate what telecommunications and service
provider organizations are doing to accomplish that, to become smarter in their services, to get
more personalized, and leverage Big Data to do that.
Maybe there are use cases you can think of or anecdotes of how this is being used. Or perhaps
you have some named customers that you could use to show us what they’re doing and what they
are getting from their investments.
Ringer: For several years now, we have one large customer, Telefónica a Latin American
conglomerate, has been working with us on analytics projects to improve the quality of
experience of their subscribers.
In Latin America, most people are interested in football, and many of them want to watch it on
their mobile device. The challenge is that they all want to watch it during the same 90 minutes.
That’s a challenge for any mobile operator, and that’s exactly where we started a critical project
with Telefónica.
We’re helping them analyze the quality of experience. Realizing the quality of the experience
isn’t a very complicated thing. There are probes in the network to do that. We can pretty
accurately get the quality of experience for every single video streaming session. It’s no big deal.
Analytics kicks in when you want to correlate this aggregation of quality with who the subscriber
is, how the subscriber is expected to behave, and what he’s interested in. We know that the
quality isn’t good enough for many subscribers during the football game, but we need to
differentiate and know to which one of them we want to make an offer to upgrade his package.
What’s the right offer? When’s the right time to make the offer? How many different offers do
we test to zero in on the best set of offers?
We want to know which one of them we don’t want to promote anything to, but just want to
make him happy. We want to give him a better quality experience for free, because he is a good
customer and we don’t want to lose him. And we want to know which customer we want to come
back to later, apologize, and offer him a better deal.

8. Real-time analytics
Based on real-time triggering of events from the network, degradation of quality with
information that is ongoing about the subscriber, who the subscriber is, what marketing segment
he belongs to, what package is he subscribed to and so on, we do the analytics in real time, and
decide what the right action is and what the right move is, in order for us to give the best
experience for the individual subscriber.
It’s working very nicely for them. I like this example, first of all, because it’s real, but also
because it shows the variety of processes we have here with correlation of real-time information
with ongoing information for the subscribers. We have contextual action that is taken to monetize
and to improve quality and to improve satisfaction.
This example touches so many needs of an operator and is all done in a pretty straightforward
manner. The implementation is rather simple. It’s all based on running the right processes and
putting the right business process in place. But this isn’t always straightforward for enterprise
customers, particularly those in the small to medium enterprise segment so imagine what CSPs
could do for their customers once they’ve gotten a handle on this for their own businesses.
Dana Gardener: It seems to me that that helps reduce the risk of a provider or their customers
coming out with new services. If they know that they can adjust rapidly and can make good on
services, perhaps this gives them more runway to take off with new services, knowing that they
can adjust and be more agile. It seems like it really fundamentally changes how well they can do
their business.
Ringer: Absolutely. It also reduces quite a lot the risk of investment. If you launch a new service
and you find out that you need to beef up your entire network, that is a major hit for your
investment strategy. At the same time, if you realize that you can be very granular and very
selective in your investment, you can do it much more easily and justify subsequent investments
more clearly.
Gardner: Are there any other examples of how this is manifesting itself in the market -- the use
of Big Data in the telecommunication’s industry?
Ringer: Let me give another example in North America. This is an implementation that we did
for a large mobile operator in North America, in collaboration with a chain of retail malls.
What we did there is combine their ongoing information that the mobile operator has about its
subscribers -- he knows what the subscriber is interested in, what they’re prior buying pattern
and transactions were and so on -- with the location information of where the individual person is
at the mall.
The mall operator runs a private wi-fi network there, so he has his own system of being able to
track where the individual is exactly within the mall. He knows within two meters where a

9. person is in the mall but with the map overlay of the physical mall and all product and service
offerings to the same grid.
When we know a person is in the mall, we can correlate it with what the CSP knows about this
person already. He knows that the specific person has high probability of looking for a specific
running shoe. The mobile operator knows it because he tracks the web behavior of the specific
individual. He tracks the profile of the specific individual and he can have pretty good accuracy
in telling that this guy, for the right offer, will say yes for running shoes.
Targeted and timely
So combining these two things, the ongoing analytics of the preferences, together with real-
time location information, give us the ability to push out targeted and timely promotions and
coupons.
Imagine that you go in the mall and suddenly you pass next to the shoe store. Here, your device
pops up a message and that says right now, Nike shoes are 50 percent off for the next 15 minutes.
You know that you’re looking for Nike shoes. So the chance that you’ll go into the store is very
good, and the results are very good because you create a “buy-now or you’ll miss-out” feeling in
the prospect. Many subscribers take the coupons that are pushed to them in this way.
Of course, it’s all based on opt-in, and of course, it’s very granular in the sense that there are
analytics that we do on subscriber information that is opted in at the level of what they allow us
to look at. For instance, a specific person may allow us to look at his behavior on retail sites, but
not on financial sites.
Gardner: Again, this shows a fundamental shift that the communications provider is not just a
conduit for information, but can also offer value-added services to both the seller and the buyer -
radically changing their position in their markets.
If I am an organization in the CSP industry and I listen to you and I have some interest in
pursuing better Big Data analytics, how do I get started? Where can I go for more information?
What is it that you’ve put together that allows me to work on this rather quickly?
Ringer: As I mentioned before, we typically recommend engaging in a two-day workshop with
our business consultants. We have a large team of Big Data advisory consultants, and that’s
exactly what they do. They understand the priorities and work together with the telecom
organizations to come up with some kind of a roadmap -- what they want to do, what they can
do, what they are going to do first, and what they are going to do later.
That’s our preferred way of approaching this discipline. Overall, there are so many kinds of use
cases, and we need to decide where to start. So that’s how we start. To engage, the best place is
to go to our website. We have lots of information there. The URL is hp.com/go/telcoBigData,

10. that’s one word, and from there you just click Contact Me, and we’ll get back to you. We’ll take
you from there. There are no commitments, but chances are very good.
Gardner: Before we sign off, I just wanted to look into the future. As you pointed out, more and
more entertainment and media services are being delivered through communication providers.
The mobile aspect of our lives continues to grow rapidly. And, of course, now that cloud
computing has become more prominent, we can expect that more data will be available across
cloud infrastructures, which can be daunting, but also very powerful. Where do you see the
future challenges, and what are some of the opportunities?
Ringer: We can summarize four main trends that we’re seeing increasing and accelerating. One
is that CSPs are becoming more active in enabling new business models with partnerships,
collaborations, internet players, and so on. This is a major trend.
The second trend that we see increasing quite intensively is operators becoming like marketing
organizations, promoting services for their own or for others.
The third one is more related to the operation of the CSP itself. They need to be more aware of
where they invest, what’s their risk and probability of seeing an specific ROI and when will that
occur. In short, Big Data and Analytics will make them smarter and more proactive in making the
investments. That’s another driver that increases their interest in using the data.
Overall they all look to become more proactive, they all realize that data is an asset and is
something that you need to keep handy, keep private, and keep secured, but be able to use it for
variety of use cases and processes to be ready for the next move.
Gardner: I am afraid we’ll have to leave it there. You’ve been listening to a Big Data innovation
discussion that highlights how the telecommunications service provider industry is gaining new
business analytics value and strategic returns through better use and refinement of their Big Data
assets. And we have seen how Big Data capabilities and advanced business analytics have
become essential to CSPs, especially as mobile and e-commerce drives their business’s future.
This discussion marks the latest episode in the ongoing HP Big Data Podcast Series, where
leading-edge adapters of data-driven business strategies share their success stories and where the
transformative nature of Big Data takes center stage.
Please join me now in thanking today’s guest. We’ve been here with Oded Ringer, Worldwide
Solution Enablement Lead for HP Communication & Media Solutions. Thank you so much,
Oded.
Ringer: Thank you very much, Dana.
Gardner: To learn more about how businesses anywhere can best capture knowledge, gain
deeper analysis, and rapidly and securely make those insights available to more people, visit the
HP HAVEn Resource Center at hp.com/HAVEn, and for more CSP-specific Big Data
information visit, hp.com/go/telcoBigData.

11. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing
sponsored journey into how data is analyzed and used to advance the way we all live and work.
Thanks for listening, and come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Transcript of a BriefingsDirect podcast on how service providers are harnessing the power of
data analytics to improve customer service and customer relations. Copyright Interarbor
Solutions, LLC, 2005-2014. All rights reserved.
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