Email a colleague    

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 Black Swan Telecom Journal

 

About the Expert

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

Related Stories

  • A Big Data Starter Kit in the Cloud: How Smaller Operators Can Get Rolling with Advanced Analytics interview with Ryan Guthrie — Medium to small operators know “big data” is out there alright, but technical staffing and cost issues have held them back from implementing it.  This interview discusses the advantages of moving advanced analytics to the cloud where operators can get up and running faster and at lower cost.
  • Telecoms Failing Badly in CAPEX: The Desperate Need for Asset Management & Financial Visibility interview with Ashwin Chalapathy — A 2012 PwC report put the telecom industry on the operating table, opened the patient up, and discovered a malignant cancer: poor network CAPEX management, a problem that puts telecoms in grave financial risk.  In this interview, a supplier of network analytics solutions provides greater detail on the problem and lays out its prescription for deeper asset management, capacity planning and data integrity checks.
  • History Repeats: The Big Data Revolution in Telecom Service Assurance interview with Olav Tjelflaat — The lessons of telecom software history teach that new networks and unforeseen industry developments have an uncanny knack for disrupting business plans.  A service assurance incumbent reveals its strategy for becoming a leader in the emerging network analytics and assurance market.
  • From Alarms to Analytics: The Paradigm Shift in Service Assurance interview with Kelvin Hall — In a telecom world with millions of smart devices, the service assurance solutions of yesteryear are not getting the job done.  So alarm-heavy assurance is now shifting to big data solutions that deliver visual, multi-layered, and fine-grained views of network issues.  A data architect who works at large carriers provides an inside view of the key service provider problems driving this analytics shift.
  • The Shrink-Wrapped Search Engine: Why It’s Becoming a Vital Tool in Telecom Analytics interview with Tapan Bhatt — Google invented low cost, big data computing with its distributed search engine that lives in mammoth data centers populated with thousands of commodity CPUs and disks.  Now search engine technology is available as “shrink wrapped” enterprise software.  This article explains how this new technology is solving telecom analytics problems large and small.
  • Harvesting Big Data Riches in Retailer Partnering, Actionable CEM & Network Optimization interview with Oded Ringer — In the analytics market there’s plenty of room for small solution firms to add value through a turnkey service or cloud/licensed solution.  But what about large services firms: where do they play?  In this article you’ll learn how a global services giant leverages data of different types to help telcos: monetize retail partnerships, optimize networks, and make CEM actionable.
  • Raising a Telco’s Value in the Digital Ecosystem: One Use Case at a Time interview with Jonathon Gordon — The speed of telecom innovation is forcing software vendors to radically adapt and transform their business models.  This article shows how a deep packet inspection company has  expanded into revenue generation, particularly  for mobile operators.  It offers a broad palette of value-adding use cases from video caching and parental controls to application-charging and DDoS security protection.
  • Radio Access Network Data: Why It’s Become An Immensely Useful Analytics Source interview with Neil Coleman — It’s hard to overstate the importance of Radio Access Network (RAN) analytics to a mobile operator’s business these days.  This article explains why the RAN data, which lives in the air interface between the base station and the handset --  can be used for a business benefit in network optimization and customer experience.
  • Analytics Biology: The Power of Evolving to New Data Sources and Intelligence Gathering Methods interview with Paul Morrissey — Data warehouses create great value, yet it’s now time to let loose non-traditional big data platforms that create value in countless pockets of operational efficiency that have yet to be fully explored.  This article explains why telecoms must expand their analytics horizons and bring on all sorts of new data sources and novel intelligence gathering techniques.
  • Connecting B/OSS Silos and Linking Revenue Analytics with the Customer Experience by Anssi Tauriainen — Customer experience analytics is a complex task that flexes B/OSS data to link the customer’s network experience and actions to improve it and drive greater revenue.  In this article, you’ll gain an understanding of how anayltics data needs to be managed across various customer life cycle stages and why it’s tailored for six specific user groups at the operator.
  • Meeting the OTT Video Challenge: Real-Time, Fine-Grain Bandwidth Monitoring for Cable Operators interview with Mark Trudeau — Cable operators in North America are being overwhelmed by the surge in video and audio traffic.  In this article you’ll learn how Multi Service Operators (MSOs) are now monitoring their traffic to make critical decisions to protect QoS service and monetize bandwidth.  Also featured is expert perspective on trends in: network policy; bandwidth caps; and  customer care issues.
  • LTE Analytics:  Learning New Rules in Real-Time Network Intelligence, Roaming and Customer Assurance interview with Martin Guilfoyle — LTE is telecom’s latest technology darling, and this article goes beyond the network jargon, to explain the momentous changes LTE brings.  The interview delves into the marriage of IMS, high QoS service delivery via IPX, real-time intelligence and roaming services, plus the new customer assurance hooks that LTE enables.

Related Articles

  • Telecom CVM: From Scattered Campaigns to Unified & Consistent Communication with Customers interview with Cretièn Brandsma — Despite the many failures Customer Value Management has faced in telecom, CVM’s future is very hopeful.  A carrier expert explains why telecoms have faltered, how customer experience programs can be revitalized, and where telecoms should invest in better tactics and technology.
  • The Key to Driving 4G Profit: Sell Value, Not Bandwidth by Miri Duenias — Are you struggling to earn a profit on your 4G investments?  Many operators are failing today on the marketing side.  But aligning 4G products with a customer’s personal preferences and desires provides the necessary sizzle to boost sales and earn a handsome ROI.
  • Will Real-Time Decisioning Save Big Data Analytics from Overblown Hype? interview with Tom Erskine — Telecom analytics is more than just collecting and analyzing data.  It’s also about taking action — correct action — often in real-time and across a complex provisioning environment.  In this interview you’ll hear how next best actions are creating value in retention and upselling through a more flexible, business-process driven approach.
  • A Big Data Starter Kit in the Cloud: How Smaller Operators Can Get Rolling with Advanced Analytics interview with Ryan Guthrie — Medium to small operators know “big data” is out there alright, but technical staffing and cost issues have held them back from implementing it.  This interview discusses the advantages of moving advanced analytics to the cloud where operators can get up and running faster and at lower cost.
  • The Customer Engagement Era: How Personalization & Backend Integration Leads to a Richer Mobile Biz interview with Rita Tochner — How does a mobile operator move its subscribers to higher levels of spending and profit?  Fierce competition, social media scrutiny, and the high cost of new networks all conspire against these goals.  In this interview, however, you’ll learn how engaging better with customers, getting more personal, and being more sensitive to their individual needs is the path forward.
  • Telecoms Failing Badly in CAPEX: The Desperate Need for Asset Management & Financial Visibility interview with Ashwin Chalapathy — A 2012 PwC report put the telecom industry on the operating table, opened the patient up, and discovered a malignant cancer: poor network CAPEX management, a problem that puts telecoms in grave financial risk.  In this interview, a supplier of network analytics solutions provides greater detail on the problem and lays out its prescription for deeper asset management, capacity planning and data integrity checks.
  • Batting for More Churn Reduction and Revenue Assurance Home Runs interview with Peter Mueller — What’s it like to transform an IT shop to big data and cloud?  In this interview, the CTO of a boutique revenue assurance explains how his firm made the leap.  He shows how project-oriented programs and working with carrier customers to explore RA and churn reduction “hunches” is where much of the action is.
  • History Repeats: The Big Data Revolution in Telecom Service Assurance interview with Olav Tjelflaat — The lessons of telecom software history teach that new networks and unforeseen industry developments have an uncanny knack for disrupting business plans.  A service assurance incumbent reveals its strategy for becoming a leader in the emerging network analytics and assurance market.
  • From Alarms to Analytics: The Paradigm Shift in Service Assurance interview with Kelvin Hall — In a telecom world with millions of smart devices, the service assurance solutions of yesteryear are not getting the job done.  So alarm-heavy assurance is now shifting to big data solutions that deliver visual, multi-layered, and fine-grained views of network issues.  A data architect who works at large carriers provides an inside view of the key service provider problems driving this analytics shift.
  • The Shrink-Wrapped Search Engine: Why It’s Becoming a Vital Tool in Telecom Analytics interview with Tapan Bhatt — Google invented low cost, big data computing with its distributed search engine that lives in mammoth data centers populated with thousands of commodity CPUs and disks.  Now search engine technology is available as “shrink wrapped” enterprise software.  This article explains how this new technology is solving telecom analytics problems large and small.
  • Sharing Intelligence, Services, and Infrastructure across the Telecom Galaxy interview with Gary Zimmerman — The telecom industry is an industry of sharing.  In fact, the rise of mobile broadband is driving a greater reliance on real-time intelligence, services trading, and infrastructure exchange.  In this article, a leading info exchange provider explains the value of its services portfolio and points to other interoperability and sharing ideas under development.
  • Data Monetization: Why Selling Intelligence is a Hot New Revenue Stream for Mobile Carriers interview with Joe Levy — Data monetization is a revenue dream come true for mobile carriers: a highly profitable sideshow where the carrier analyzes and sells data it already collects for other purposes.  In this article you’ll learn how operators monetize their data through use cases in corporate advertising and media branding.
  • Harvesting Big Data Riches in Retailer Partnering, Actionable CEM & Network Optimization interview with Oded Ringer — In the analytics market there’s plenty of room for small solution firms to add value through a turnkey service or cloud/licensed solution.  But what about large services firms: where do they play?  In this article you’ll learn how a global services giant leverages data of different types to help telcos: monetize retail partnerships, optimize networks, and make CEM actionable.
  • Raising a Telco’s Value in the Digital Ecosystem: One Use Case at a Time interview with Jonathon Gordon — The speed of telecom innovation is forcing software vendors to radically adapt and transform their business models.  This article shows how a deep packet inspection company has  expanded into revenue generation, particularly  for mobile operators.  It offers a broad palette of value-adding use cases from video caching and parental controls to application-charging and DDoS security protection.
  • Radio Access Network Data: Why It’s Become An Immensely Useful Analytics Source interview with Neil Coleman — It’s hard to overstate the importance of Radio Access Network (RAN) analytics to a mobile operator’s business these days.  This article explains why the RAN data, which lives in the air interface between the base station and the handset --  can be used for a business benefit in network optimization and customer experience.
  • Back Office Streamlining to Enterprise Support: The Many Flavors of Wireline Analytics interview with Tom Nolting — Mobile analytics gets plenty of press coverage, but analytics is just as crucial for wireline operators.  In this article, a billing VP at a leading wireline operator discusses several diverse uses of analytics in billing, enterprise sales/retention, and network partner margin assurance.
  • Analytics Biology: The Power of Evolving to New Data Sources and Intelligence Gathering Methods interview with Paul Morrissey — Data warehouses create great value, yet it’s now time to let loose non-traditional big data platforms that create value in countless pockets of operational efficiency that have yet to be fully explored.  This article explains why telecoms must expand their analytics horizons and bring on all sorts of new data sources and novel intelligence gathering techniques.
  • B/OSS Mathematics: The Quest to Analyze Business Problems & Drive Operating Decisions interview with Matti Aksela — Analytics is the glory of mathematics brought to practical use.  And in telecom, analytics has merely stratched the surface of its full potential.  In this article, you’ll learn how machine learning is being combined with the power of CDR number crunching to optimize mobile top-ups, control churn — and in the future, help telecoms make critical network and operating decisions.
  • Leveraging the RA/FM Platform to Deliver Business Insights to Finance & Marketing by Amit Daniel — Carrier professionals using RA and fraud management tools are getting requests from internal customers who want the role of RA/FM platforms expanded to deliver up-to-date analytics data for finance and marketing purposes.  This article advocates a cross-product layer to serve such broader use cases.  The effect would be to transform the existing RA/FM platform into a combined business protection and business growth analytics engine.
  • A Mobile Marketer Service: Bridging Personalization & B/OSS Flowthrough interview with Efrat Nakibly — Marketing analytics is a prescriptive program for driving  actions such as sending a timely promotion to a mobile subscriber.  But completeness demands that you also be able to provision that treatment, qualify the promotion, and keep billing fully in the loop.  This article shows how a managed services program can deliver such an end-to-end process and manage customer life cycles on a one-to-one basis.
  • Science of Analytics: Bringing Prepaid Top Ups & Revenue Maximization under the Microscope interview with Derek Edwards — Prepaid subscribers are the customers that carriers know the least about.  The operator is not interacting with prepaid customers on a monthly basis.  You’re not sending a bill, nor do you have detailed profiles on these customers, especially in the developing world where customers are buying SIMs at a grocery store.  This interview explains how contextual marketing meets the unique analytics challenge of prepaid customers.
  • Connecting B/OSS Silos and Linking Revenue Analytics with the Customer Experience by Anssi Tauriainen — Customer experience analytics is a complex task that flexes B/OSS data to link the customer’s network experience and actions to improve it and drive greater revenue.  In this article, you’ll gain an understanding of how anayltics data needs to be managed across various customer life cycle stages and why it’s tailored for six specific user groups at the operator.
  • Profitable 3G: China’s Mobile Operators Monetize Networks with Retailers & Partners interview with Kevin Xu — Mobile operators are at the center of explosive growth in wireless services.  But to exploit this opportunity requires IT ingenuity and a broader view on how the mobile user can be served.  In this article you’ll learn the innovative techniques Chinese operators use to monetize 3G networks via analytics and partnerships with retailers, social networks, and advertisers.
  • Customer Analytics: Making the Strategic Leap From Hindsight to Foresight interview with Frank Bernhard — Are your company’s analytics programs scattered?  Is there a strategy in place for customer analytics?  This interview with a leading telecom analytics consultant explains why strategy and planning around the analytics function is crucial to getting your money’s worth.  Topics discussed include: hindsight vs. foresight; an advanced analytics program; and the interface sophistication required to support high end vs. low end analytics users.
  • Meeting the OTT Video Challenge: Real-Time, Fine-Grain Bandwidth Monitoring for Cable Operators interview with Mark Trudeau — Cable operators in North America are being overwhelmed by the surge in video and audio traffic.  In this article you’ll learn how Multi Service Operators (MSOs) are now monitoring their traffic to make critical decisions to protect QoS service and monetize bandwidth.  Also featured is expert perspective on trends in: network policy; bandwidth caps; and  customer care issues.
  • Analytics Meditations: The Power of Low-Cost Hardware and the Social Network Within interview with Ken King — Analytics didn‘t arrive yesterday.  Data warehousing and BI have been in the telecom vocabulary for twenty-five or more years.  In this interview, you’ll gain a perspective on why “big data” changes the game and why social network (or social circles) analysis promises the next level of insights.  Other interesting topics include: segmenting the analytics market, engaging with carrier clients, and upgrading from older- to newer-style methodologies.
  • LTE Analytics:  Learning New Rules in Real-Time Network Intelligence, Roaming and Customer Assurance interview with Martin Guilfoyle — LTE is telecom’s latest technology darling, and this article goes beyond the network jargon, to explain the momentous changes LTE brings.  The interview delves into the marriage of IMS, high QoS service delivery via IPX, real-time intelligence and roaming services, plus the new customer assurance hooks that LTE enables.
  • Shared Data Plans: The Challenge of Managing a Family of Pricing, Revenue Assurance, Fraud, and Network Policy Issues by Amit Daniel — Verizon Wireless‘ recent announcement of its move to shared data plans for families shook the mobile industry.  In this column, cVidya’s Amit Daniel shines a spotlight on the knowhow and analytics tools that operators now deperately need to offer the right  shared data price plans, ensure bandwidth throttling is handled correctly, and address new fraud concerns.
  • Analytics Guru: Are Telecoms Ready for the Biz Intelligence Explosion? interview with John Meyers — Business intelligence is evolving from the creation of dashboards and reports to taking action based on a deep knowledge of the environmental context.  The article explores the implications of “big data” in terms of IPTV, storage requirements, hardware, event collection, and deep packet inspection.
  • Social Networking for Telecoms: How To Enlist Friends and Family as Mobile Marketers interview with Simon Rees — Social Network Analysis (SNA) is about exploiting data on “friends and family” connections to combat churn and win new CSP business.  The article explores how an analysis of the ebb and flow of CDRs, phone calls, and messages, can identify key influencers and drive powerful marketing campaigns.
  • Making the Strategic Leap From Billing to Merchandising interview with Humera Malik — Today billing/charging technology has progressed to the point where the usage intelligence, the charges, the user behaviors, and the analytics can all come together in near real-time.  This article discusses the organizational and marketing strategies that enable a operator to create a true “merchandising” system that can revolutionize a CSP’s business.