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August 2013

Harvesting Big Data Riches in Retailer Partnering, Actionable CEM & Network Optimization

Harvesting Big Data Riches in Retailer Partnering, Actionable CEM & Network Optimization

If you’ve been reading his Journal, you’ve read many interviews I’ve held with experts at relatively small telecom analytics firms.

But in this article we take a departure because we’re interviewing HP, the largest IT vendor in the world with about $120 billion in annual revenue and 332,000 employees.

So what gives?  How can the analytics market sustain both small startups and global giants?

I think it all depends what you’re looking for.  Do you need: 1) a quick ROI program requiring only a modest investment and having very little impact on existing processes; or 2) a large scale program having many moving parts and requiring complex, highly-coordinated processes.

The fact that forty or more small solutions vendors are out there shows there’s plenty of room for adding value through a turnkey service or cloud/licensed solution.

Fine, if you can basket 20% of the low-lying fruit on the apple tree by hand, that’s good business, but that still leaves a good 80% of apples on the tree you can‘t reach.  And that’s where the scale and experience of a firm like HP provides a ladder — particularly for tier 1 operators — to harvest the higher, less accessible fruit.  And for HP that includes a number of things: consulting, synchronizing complex B/OSS processes; and tackling new use cases that are beyond the scale or integration capabilities of smaller firms.

We are joined now by Oded Ringer, HP’s Worldwide Solution Enablement Manager of Communications Solutions, who gives us a nice overview of HP’s analytics programs for the Telco industry.

Dan Baker: Oded, it would be great if you could tell us where the telecom analytics team sits in the larger HP organization.

Oded Ringer:Happy to, Dan.  I’m part of HP Communications & Media solutions (CMS) business unit, where 3,000 services people work on our full portfolio of products and services.  Our solutions span core transport, service enablement, service delivery, analytics, API exposure, value added services, BSS and OSS, subscriber data management and content delivery.

What are the challenges and opportunities telecom operators today where analytics can help?

Well, there’s no question that telecoms face many challenges.  For one, the ARPU for many operators is declining, especially in developed countries: the costs to earn a dollar of revenue are also going up.

So the first problem analytics needs to address is an operational one: provide the intelligence to better use existing assets and optimize network investments.

Secondly, telecoms clearly need more insights on what their customers want and an ability to act on those insights to produce happier customers and a more personalized experience.

Third, several business models are coming into play that bring fresh revenue streams to CSPs.  And these are things the CSPs can either do on their own or through partnering with the Over The Top (OTT) and cloud players.

What we know for sure is that the older generation of BI solutions — running batch queries and analyzes against a big database — are simply not enough anymore.  In fact, the total amount of customer data that lives in structured databases today is relatively minor compared to the huge amount of unstructured voice records, emails and so forth.  And rather than run queries against a database, today you are analyzing a live stream from the wire.

Finally, there’s practically no other industry that keeps such huge volumes of data as telco — and none that maintains as many interactions with the subscriber as we do.

How does HP bring it all together?

Well at the core of our telco analytics offering is HP’s Smart Profile Server or SPS (at the center of the diagram below).  The job of the SPS is to collect data from many sources: customer experience data, static subscriber data from a CRM, and also data from the applications the person is using.

Once collected, two kinds of analytics are applied: one for structured and one for unstructured data.  And here, as you may know, HP has made huge investments in product assets which line up with HP’s belief that Big Data will be one of three major focus areas for the company up until 2020.

One solution we acquired is Vertica, which powers our database analysis.  So the idea is to use the Vertica platform to look at structured data -- who is the customer, where are they located, and what’s their tendency to buy?

The second acquisition was Autonomy, the market-leading solution for analyzing unstructured data.  Why is unstructured data so important?  Because unstructured data is often the key to determining what information a mobile user is interested in right now.  A tremendous amount of relevant data can be gleaned from unstructured documents such as emails, SMS, call center conversations, and social networking websites, etc.  Autonomy can also detect the urgency of customer’s needs — even their emotional attachment to the subject.

Now as you can imagine, the trick is to manage all this information properly.  It’s very sensitive and needs to be wrapped with tight privacy controls.  The data is anonymized so you never recognize the customer by their real ID.

HP Telco Big Data and Analytics Blueprint

I guess the best way to see how it works is through a couple of examples.

Yes, I actually have three cases to discuss with you, Dan:

Real-Time Advertising & Retailer Partnering

This first case shows how a carrier can exploit its big data in a retail setting.

To begin, we know that operators maintain a great deal of intelligence on their customers — including the subscriber’s age, gender, phone brand, etc.  Plus we are constantly collecting and analyzing mountains of unstructured data on customer behaviors.

Now to make this use case more interesting to tell, I’m going to use a hypothetical person named Pam.  In reality, of course, Pam’s identity is totally hidden so that people looking at the data can‘t associate anything with a real person.

OK, so who is Pam?  Well, she’s a married, 32-year old woman who uses an iPhone, lives in Miami, and whose family income it projected to be in the top 15% of households.  That’s the kind of structured data that lives the database we maintain on Pam.

Now if we dig deep into the unstructured data, we discover from Pam’s browsing history that she’s in the market for an iPad.  And network data also shows she experienced 6 dropped calls yesterday.  Finally, Pam began looking at baby accessory websites within the last 3 months.  And at this moment, Pam has just arrived at a large shopping mall.

OK, so let’s delve into our big data and turn Pam’s seemingly meaningless arrival at a shopping mall into money for the operator.

First of all, the analytics system predicts from behavior that Pam’s going to have a baby in a few months.  And since Pam is shopping in a mall where there’s a Baby World franchise store, suddenly we have a significant event because Baby World, a national retailer of baby accessories, has a marketing agreement with the operator.  So a discount coupon for buying something at Baby World is immediately sent to Pam’s iPhone and should she make a purchase at Baby World, the operator uses the coupon number to bill Baby World for a commission.

This is an example of how big data analytics can monetize the intelligence that the operator has on its customers.  Now while this example may sound far-fetched, actually an HP customer — one of the largest operators in Canada — is operating such a service today.

So what is HP doing for the operator?  Well, first of all there are many processes to be integrated, such as with the billing system since we are offering a coupon.  Second, we are getting a feed on Pam’s location.  So these are just two examples of many processes that need to be coordinated.  It’s not as easy as running a query against a database: a B/OSS workflow is what’s triggering the database.

Actionable Experience Management for Video Streaming

A second opportunity is what we call “actionable experience management” and this is a use case where HP got involved with three Telefonica operators in South America.

The use case begins by scoring the video quality of streaming video on either a mobile device or landline network.  If the score is below a certain threshold, we correlate that fact with other data we know about the subscriber, for instance his tendency to buy things and many other factors.  The result is we go with one of three actions:

  1. Offer a Two Hour Quality Upgrade.  If the quality is not very good and we know the user frequent enjoys videos, maybe we offer an upsell: “Would you like to watch this episode in much better quality?  Well, the cost is only 99 cents.” If he says “Yes” — and the uptake is very high on these low cost offers — the user gets a great value for the next two hours watching the video because we deliver the best quality we can.
  2. Loyal Customer Gets QoS Improvement for Free.  If we recognize the user as good and loyal and realize we have not given him the level of quality we feel he deserves, we give him the best video quality free of charge by changing our QoS policy.
  3. Network Operations Does the Best it can — Finally, if we realize that the quality cannot be improved because of problems in the network, in that case we turn the intelligence over to network operations to handle the issue the best they can.

So this use case, I think, provides a good example of real-time analytics and integration with the B/OSS, subscriber profiles, and real-time triggering of a network process to improve quality.

Network Optimization

So many of these analytics use cases are new and we can‘t really predict how successful they will be in the long run.

However, a safe bet is that network experience optimization will be a winner because it’s a capability all the operators must have.  On one side, they need to give the customer a great experience.  On the hand they can‘t afford to beef up the network everywhere.  So they need to come up with more insightful approaches.

We know who the good customers are and we can afford to be generous to them.  On the other hand, others need to pay for higher quality.  We cannot upgrade the network across the board.  So knowing what a specific customer wants and expects is the only way to beef up your network in a prudent way.

The operator needs to decide, very granularly, who gets a higher priority over limited network resources.  Others may get less bandwidth —the best available perhaps — but you can‘t afford to offer the best to everyone.

Oded, thanks for these very interesting use cases.  There are certainly are a lot of things operators need to think about as they roll out analytics and big data.

Yes, and when there are a great many things to do, you need to set priorities.  For instance, what are you most interested in doing?  Optimizing the network?  Improving the customer experience?  Selling more things?  Doing better marketing?

HP actually conducts an in-depth business advisory workshop with an operator where we come in, analyze their situation, and identify a few use cases that make sense to start with.  The workshop is conducted by a global team: our Telco Big Data experts go in and sit together with all relevant stakeholders in the operator environment.  These typically include IT leadership, Marketing management as well as Customer retention and Service experience arms.

Copyright 2013 Telexchange Journal

 

About the Experts

Oded Ringer

Oded Ringer

As manager of solution enablement, Oded Ringer is responsible for bringing together: Products, Business, Operations, Sales and Marketing into a coherent Go-To-Market Strategies, across all regions, countries and market segments.

Oded has many years in the telecom industry at a wide variety of technology, business and management roles.  Before joining HP, in 2007, Oded had leadership positions in Companies like Alcatel-Lucent, TTI-Telecom, Ness technologies and Goldman Sachs.

Oded holds an MSC from the University of Bridgeport in Connecticut, and a BSC in Computer Science from the Bar-Ilan university in Israel.  He lives in Tel-Aviv with his wife and 2 kids.   Contact Oded via

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