"Deep Packet Inspection can play a key role within cable operator networks; e.g., for traffic engineering and network security. But it presents significant shortcomings in holistically analyzing subscriber activity, which is key for both retention and growth." —Kate Mitchell, Edge Intelligence

Cable providers are at a critical juncture.

The number of consumers abandoning TV subscriptions for over-the-top offerings continues to grow. At the end of Q1, 3.4% of households cut the cord over the prior year, the highest rate ever, leaving about 83 million households paying for cable services in the U.S. This doesn’t include the increasing numbers among new households and younger demographics that have never subscribed to a pay TV service in the first place, a.k.a., “cord-nevers.”

Currently, approximately 13.5 million households (14% of all households) don’t pay for traditional forms of TV service. By 2021, eMarketer predicts, the number of cord-cutters will nearly equal the people who never had pay TV — a total of 81 million U.S. adults.

While this may all seem like doom and gloom for cable MSOs, it’s actually an opportunity to stop the cord-cutting trend and also win over cord-nevers through innovation. For cable operators to quickly turn the tide, it will require a stronger understanding of their subscriber base, or deeper than what’s possible with Deep Packet Inspection (DPI).

Seeing the Limits of the Old

DPI has been the default method over the past decade for examining and managing network traffic; it runs in line with production traffic or sends copies of packets to a network monitoring connection to inspect packets flowing through the network. Data is extracted from within each packet.

DPI can play a key role within cable operator networks; e.g., for traffic engineering and network security. But it presents significant shortcomings in holistically analyzing subscriber activity, which is key for both retention and growth.

What are those obstacles? First, it’s very challenging and costly to scale a DPI offering since it relies on inspecting at the packet level, on every port, at increasingly high network speeds. In addition, it can be difficult and immensely time-consuming and labor-intensive to gain customer insight from DPI systems since the hardware can be siloed and spread across many locations deep inside the network.

So how can cable MSOs obtain the subscriber insight they need to positively impact their business?

New Analytical Architectures

Big data analytics — the process of examining large and diverse data sets — can enable MSOs to discover hidden patterns, previously unknown correlations, customer preferences and other highly useful information to help them make more informed business decisions. And network data for cable operators is big, with hundreds of billions of records added daily, generated from millions of subscribers, and the need to retain trillions of records for analysis and compliance purposes. 

So the collection, real-time correlation, analysis and retention requirements placed on the analytical architecture are demanding — and many big data architectures are unable to keep pace. Analytics should provide the granular insight into and throughout the entire customer lifecycle that cable providers need to effectively support things such as usage-based billing, support-related inquires, proactive upgrades to bigger plans and anticipating those likely to churn. That knowledge can help inform activities directly geared to current and prospective subscribers.

For example, with the knowledge of subscriber behaviors garnered from big data analytics, cable providers can grow revenues through initiatives such as targeted promotions and customized product offerings. For those predicted to churn, better customer service and incentive offers may help in maintaining their business.

And for consumers who no longer subscribe to cable services but do still have data plans, providers can use big data analytics to determine their OTT viewing, web content and download data so they can figure out how best to monetize this use of their network. With this deep level of knowledge, cable providers can have accurate insight on data consumption to make sure usage-based billing and capped data tiers can capture revenue to offset what they’re losing from paid TV.

Going Deeper

While DPI still has an important role in supporting cable MSOs, it’s not cutting it in this time of cord-cutting. What’s needed is a way to understand subscribers on a deeper level than DPI can provide. By being able to better analyze the immense amount of data that’s available, cable providers can be well positioned to provide customers with personalized offers that resonate, incentives that motivate and service that delights — helping providers to retain and grow their business.

Kate Mitchell is CEO of Edge Intelligence, a distributed analytics platform.

Wicket Labs, a startup that is run by execs who are late of thePlatform, has raised a $2 million round of seed financing and has introduced a data-driven “Scorecard” product that can help OTT providers and other direct-to-consumer digital media companies keep tabs on key areas such as engagement, churn and customer-acquisition costs.

Wicket Labs said Madrona Venture Group, Divergent Ventures and undisclosed angel investors joined the $2 million round. It also announced the appointment of new members to its advisory board -- Krishan Bhatia, EVP of business operations and strategy for NBCUniversal’s ad sales unit; Dustin Hillard, CTO of Versive, a machine learning company based in Seattle; and Mike Berkley, previously SVP of product management for Viacom.

RELATED: Startup Brings Vision to Video Cloud’s ‘Blind Spot’

In tandem with its partners, Wicket Labs’s new Scorecard product is designed to obtain data from various sources that are traditionally housed in separate silos and aggregate,  analyze and normalize that information into a form that can help to drive decisions that can aid with engagement, customer acquisition, conversion rates, and churn.

That combined and analyzed data can provide key predictive information about customers and audiences, Marty Roberts, CEO of Wicket Labs, explained.

RELATED: Learn More at the TV Data Summit, part of #NYCTVWK

Roberts co-founded WicketLabs with chief strategy officer Ian Blaine, who also started thePlatform, the online video publishing company that Comcast acquired in 2006. Roberts joined thePlatform that year and eventually rose to co-CEO before departing in 2015.

Roberts said the data is particularly helpful to publishers who are transitioning from print to digital as well as a growing array of OTT video services, including authenticated TV Everywhere offerings from MVPDs and programmers, that continue to chip away at viewership of traditional TV.

Rather than relying on their gut feelings, data will help them make decisions and get a better fix on their audience, and how customers are coming through the door (i.e. through social media, digital ads, search, email) and get a better sense of their company’s value, Roberts said.

“They are all learning a new vocabulary,” he added.

Former Cablevision Systems exec Kristin Dolan is extending her business acumen to the world of fast food.

Dolan, the former COO of Cablevision (now part of Altice USA), has been appointed to the board of The Wendy’s Company, which, through the appointment, has bumped the size of its board from 11 to 12 members.

Dolan is now CEO of 605 LLC, an audience and data analytics company.

RELATED: Dolan Family Ventures Launches Data and Analytics Company

She and her husband, James Dolan, the former CEO of Cablevision, have also formed an investment fund, Dolan Family Ventures, that is focused on providing capital to data, analytics and tech-based, media-focused businesses.

Kristin Dolan also also serves as a director of AMC Networks, The Madison Square Garden Company and Revlon Inc.

"My fellow Board members and I are delighted to welcome Kristin to serve on our Board,” Nelson Peltz, Wendy’s board chairman, said in a statement. “Her impressive experience in the media and entertainment marketing industries, coupled with her extensive experience as a chief executive officer, chief operating officer and public company director, brings valuable expertise and an insightful perspective to our Board."

Discovery Communications has named former Viacom executive Christopher McGrath senior vice president, data and analytics. He will be based in Discovery’s New York office and report to chief technology officer John Honeycutt.

In partnership with senior business leaders across Discovery’s digital, sports, ad sales, marketing and direct-to-consumer initiatives, McGrath will oversee a global group exploring, designing and delivering solutions to monetize Discovery’s proprietary data assets in new and innovative ways. 

“Discovery is laser-focused on utilizing new technologies to enhance both our longstanding and recent investments and to grow our global direct-to-consumer and digital businesses,” Honeycutt said in a statement. “Chris’ expertise and strategic ability to implement solution-oriented technology across our business will allow us to scale and widen our digital footprint for deeper consumer engagement.”

Previously, McGrath served as senior vice president, data strategy and consumer intelligence, at Viacom, where he oversaw data strategy, warehousing, governance, data science and analytic functions for the company. McGrath has held various e-commerce and analyst roles at Starwood Hotels, Bertelsmann and International Creative Management (ICM).

Emphasizing the company’s focus on analytics, Viacom named Kern Schireson as executive VP and chief data officer, a new position.

Schireson, who had been executive VP of data strategy and consumer intelligence, will be responsible for Viacom’s data capabilities for its domestic and international businesses, including its TV, theatrical and digital business units. The exec also helped create Viacom Vantage, the company’s data driven advertising initiative.

He will report to executive VP and CFO Wade Davis and work closely with the company’s leadership. Priorities include accelerating the use of data science at Paramount Pictures and incorporating data into the company’s events and consumer products businesses.

"Kern has assembled a consumer intelligence team that is second to none in the media and entertainment industry," Viacom CEO Bob Bakish said. "I could not be more excited by the opportunity to leverage our unique capabilities across each of our brands."

Read more at broadcastingcable.com.

Amidst the mind-boggling number of churn reduction strategies communications service providers (CSPs) have at their disposal, how are some leaders generating tens of millions of dollars successfully navigating the retention challenge? They are applying a test vs. control approach to answer questions such as: How should renewal pricing vary based on promotional price, tenure, PSUs, etc.? Which customers should we proactively call, and when? Can we profitably reduce the extent of save offers for some customers? Should we offer free streaming, a price discount or encourage a tier downgrade?

While provider executives know the importance of answering these questions — and most are invested in using data to curb churn — many still fall short in answering them accurately. Isolating how business actions affect churn over its natural rate is challenging. A few issues at play are:

• Home ownership: Apartment dwellers are naturally more likely to churn than homeowners as they have expiring leases that cause relocation.

• Tenure: Subscribers with less tenure are more likely to have promotions up for expiration.

• Seasonality: Customers who moved into a new apartment in the summer versus winter may behave differently.

Without accurately identifying each customer’s baseline churn, CSPs often incorrectly predict the impact of their retention efforts (accidently measuring differences in baseline churn for various customer segments instead of incremental churn caused by their actions). As a result, concessions are made to subscribers who would have renewed anyway; meanwhile, investments aren’t made in subscribers who could have been saved with the proper offer or outreach.

A growing number of CSPs are applying test versus control analytics to understand which retention initiatives are incrementally effective. The “test group” consists of subscribers who experience a given action, such as a price increase at the end of a promotional period. The control group is then constructed from subscribers who are similar to test customers across all other dimensions (e.g., age, tenure, homeownership), but did not experience that action (e.g., still on promotion). When highly similar subscribers are compared, any resulting performance differences can be directly attributed to the business action taken.

Knowing the overall impact of a retention program is important. Even more critical is understanding which resulting actions should be deployed to profitably retain each customer.

In an industry faced with an onslaught of new competitive threats, accurately understanding the impact of your business actions is critical. Before you incorrectly target more subscribers, consider using advanced test vs. control analytics.

Marek Polonski is a senior vice president at APT, an Arlington, Va., based provider of cloud-based cause-and-effect analytics software.

As multichannel video programming distributors strive to boost subscriber loyalty in the face of increased competition, improving both customer care and network reliability is tantamount to their success.

Collecting and managing the Terabytes of data that providers receive from their customers and networks each day is a major part of delivering on those objectives.

Collection is easy — operators have been doing that for years. The trick is to analyze that information, then act and react to it in a timely fashion that improves subscribers’ perceptions of the customer experience.

To use big data effectively, distributors should look at their business decisions through a customer-experience lens, rather than just from an operations and technology standpoint, said Chris Menier, vice president of cable and media for Guavus, a data analytics company.

PARSING THE NOISE

Case in point: Operators receive tens of thousands of network alerts and alarms every day, Menier said. They can range from signal noise on a specific node to major blowouts. Big data analytics helps a network operator figure out how those alerts affect a customer’s experience, and helps parse how subscriber issues can be resolved with minimal impact to network operations or the bottom line.

Operators don’t need more information, Menier maintained. They need timely, contextualized information.

Breaking down the customer-care, operations and marketing silos and using all of that data simultaneously to solve problems and enhance service can help providers to improve the customer experience. That, in turn, leads to higher network promoter scores, lower churn and lower operational costs.

Sifting through operational data at a granular level doesn’t mean gathering more information, Menier said. It means using information that operators are already collecting more effectively.

For instance, rather than rebooting every modem overnight after an alarm is tripped, Guavus’s analytics can detect which modems are being affected and automatically send those customers a message explaining the problem and the solution. This approach both pinpoints where the operational problems reside and reduces calls into call centers and truck rolls, he said.

“We can turn detractors into promoters,” he said.

Over-the-top providers are delivering this kind of service now. Amazon Video, for instance, constantly monitors its quality of service and network operations. When something is amiss, Amazon subscribers recieve messages informing them of the problem and are issued credits commensurate with any interruption, said Kerry Sims, vice president, global solutions and smart infrastructure at Hitachi Consulting.

“This is the kind of proactive response consumers are increasingly demanding,” Sims said.

Because OTT providers are fully IP-configured, it’s easier to implement big data, Sims said. And it takes more than just network monitoring to meet customer-satistfaction goals.

INSIDE, OUTSIDE INFO

Information must be collated from both inside and outside a provider’s walled garden, including data on customer interaction via phone call, email or chat; social-media chatter; and even factors such as weather.

Effective use of big data “is like opening the aperture of a camera lens to better understand the whole scene,” Sims said.

AT&T analyzes more than 100 factors known to affect customer satisfaction, according to Nicole Rafferty, AT&T Entertainment Group’s vice president of customer experience and operations support . They include everything from call completion rates to terrain to posts on social media.

AT&T makes about 30 billion (yes, billion) overall service-assurance measurements across its wireless and wired networks in a typical hour, Rafferty said. It’s all aggregated and analyzed in real time to derive insights that help AT&T manage its network.

Customer-service agents use big-data analytics to parse the best way to remove those pain points, reducing time spent on the phone and improving the customer experience. For example, a virtual assistance speech-recognition tool makes it easier for AT&T subscribers to reach the right agent to solve their problems quickly and without a lot of number pushing or rerouting. The company worked with Interactions LLC to develop the tool, which understands when customers speak in complete, conversational sentences.

Since launching the product last year, AT&T has seen a 53% reduction in the number of callers who dial “0” to speak with an agent; most were able to resolve their issue without talking to an agent at all. Misdirected calls were down 36%, and the time customers spent with the automated system fell 28%. The tool should be in place company-wide by 2017, Rafferty said.

Comcast is using information from customer interactions to improve and personalize the call-center experience. For example, agents can access real-time, relevant context about a customer’s account to smooth customer interactions, said Comcast spokeswoman Jenni Moyer.

Agents can make recommendations based on prior interactions, the reasons the customer is calling at that moment, past service issues, etc.

“We can use this information to provide updates in our IVR system that gets customers to the right type of agent or information based on why they’re calling,” Moyer said. “We are also piloting technology to get the caller back to the same agent they talked to earlier.”

AT&T has created what it calls the Big Data Center for Excellence to gather and analyze the Terabytes of data it receives every day. The center works with AT&T’s business units to understand and eliminate pain points for customers, and makes the company more proactive.

“If you know you can be proactive, it takes the burden off the customer,” Rafferty said.

Predictive and proactive care may be the Holy Grail of improving the customer experience, but it’s really only one piece of the whole big-data picture, Matt Roberts, director of BDSI marketing at Amdocs, said.

“Some situations and parts of a company’s operations are better off when analyzed in real time,” he said. “But not everything has to be analyzed in real time.”

Using real-time big data analytics, a service provider can more quickly resolve network issues following customer calls and, perhaps more important, identify a customer’s issues before they he or she is even aware there is a problem. A service provider can either fi x the problem or reach out to let a subscriber know of a pending issue in advance and offer customers a salvo to ease the pain, Roberts said.

SETTING OTHER OBJECTIVES

Big data can also help operators also make strategic and long-term plans and that doesn’t necessarily require real-time analytics. For instance, by taking network data and overlaying it with customer data, an operator can determine when and where to upgrade plant first, Roberts said.

Big data can also be used to predict when components are likely to fail and be replaced before failures happen. It can pinpoint where new product launches will have the most impact, or determine which products are going to give a boost to customer satisfaction, Menier said.

Everything needs to be filtered through the customer lens, and big data analytics can help operators do that more efficiently and effectively.

“Big data has become a tangible entity,” Roberts said. “It’s gone from solving problems to helping make strategic long-term decisions.”  

Euclid Analytics, a company focused on location analytics, said it has closed a $20 million Series C funding round led by Cox Enterprises, with help from other new investors Groupe Arnault, Moët Hennessy Louis Vuitton, and Gold Sky Capital.

Euclid, which has raised $44 million since its founding in 2010, said existing investors Benchmark, NEA and Harrison Metal also joined the round.

Euclid’s technology aims to provide businesses with a “holistic view” of the customer by factoring in data from a mix of online and offline channels. That data, it holds, helps brands provide a more “streamlined, personalized experiences in their physical stores.”

Euclid said a mix of retailers, quick-service restaurants, shopping malls, retail banks, and transportation hubs use its technology to analyze customer behavior in physical locations, noting that its cloud-based platform analyzes more than 10 billion daily “events” and 300 shopping sessions annually. Its software platform has been integrated with WiFi products from vendors such as Aerohive, Cisco Systems, HP/Aruba, Meraki, Ruckus Wireless and Xirrus.

“Technology is transforming how the world buys and sells products across all industries, and as a company that provides broadband communications, Cox understands the importance of Wi-Fi,” said Duncan O’Brien, Cox Enterprises’ senior vice president and general manager of corporate strategy and investments, in a statement. “With its extensive technology, Wi-Fi and data science expertise, Euclid is positioned to bridge the gap across the online and offline worlds in this new digitally-connected era of retail.”

O’Brien is also joining Euclid’s board of directors. 

The pay TV industry is transforming more rapidly than any other major industry. In an acknowledgement of this shift, 2015 was the first year in which the National Cable & Telecommunication Association renamed its marquee “Cable Show” the “Internet & Television Expo (INTX),” reflecting an evolution in the key players involved in the content ecosystem.

Undoubtedly, one of the biggest cable trends for 2016 will continue to be growth in over-the-top (OTT) services: the number of subscribers, the types of players, and the content available.

Meanwhile, M&A will continue to dominate significant mindshare. The question that many cable executives will ask is: In the face of these broad industry shifts, what are the actions that we can take to stay relevant and drive profitable growth?

1. Combatting OTT
If Orange Is the New Black, then OTT is the new reality for the Internet and television industry. Potentially even more worrisome for the industry, original content creation from these services is on the rise, and content providers may continue to follow in the footsteps of HBO Now and offer their content as standalone services.

Facing this existential threat to its video base, the industry will ramp up its response in 2016. These efforts will fall into two key buckets: improving retention strategies with existing products and services and launching new directly competitive services.

Providers can take many actions to prevent cord-cutting and downgrades. Yet an all-of-the-above approach is not likely to be the best option; executives need to know which specific strategies work best to profitably retain specific customers in the face of this threat. Understanding the most profitable price to charge subscribers upon the expiration of their promotion is a critical factor in moving the needle on customer retention. Getting this aspect right is worth tens of millions of dollars. Similarly, offering unparalleled user experiences through new innovative operating systems (e.g., Comcast's Xfinity X1 Platform, Dish’s Hopper, etc.) can increase usage and prevent customers from leaving. As they continue to innovate with their technology offerings, some cable companies are using small-scale tests of their new operating system features to ensure that they have the intended effect of increasing customer satisfaction and product usage, and not turning away customers.

While it is critical to optimize existing offerings, many MSOs are also considering (or have already launched) their own directly competitive streaming services. We’ve recently seen the introduction of Dish Sling, Verizon Go90, Comcast Stream, and others. There will undoubtedly be more to come in 2016. Launching these services does not come without risk. Will existing customers simply trade down from their more expensive TV bundles? Will new customers now have lower ARPU than they otherwise would have? Ultimately, MSOs need to understand how to go to market with their streaming services in a way that compliments their existing business (e.g., retaining customers who would have otherwise left TV completely).

For MSOs that are launching their streaming services in phased approaches (e.g., Comcast “Stream” initially launched in Boston), it can enable them to refine their offerings as they roll them out. Using test vs. control analytics, they can understand optimal pricing, which customers to target with marketing outreach, and more.

2. A Step Change in Analytic Capabilities
Cable and telecom companies have incredible data that they could use to inform their initiatives, but for years, they have not had analytics that can clearly differentiate between value-add and value-destroying programs. As the volume and variety of their data (and the importance of targeted marketing and retention strategies) increases, some MSOs will begin to move towards analytic strategies that provide more accurate and targeted predictions.

Today, many companies measure the effectiveness of their offers by analyzing how many customers redeemed that offer. The problem? By looking at redemption rate in isolation, it is impossible to know how many of those customers would have redeemed the offer anyway. In 2016, more organizations are likely to start using analytic approaches that enable them to identify the cause-and-effect relationships between their actions and customer behavior.

For companies that have already moved beyond redemption-based analyses and do random-holdout testing, speed and granularity of analytics will be the next natural progression. MSOs will increasingly need to evaluate all of their hundreds or thousands of campaigns, not just a select few. Further, they will need to be able to not just understand the aggregate impact, but fully understand which customers will respond best to each initiative. Today, most don’t have the capabilities to conduct accurate, granular analysis on a large scale, leaving millions of dollars on the table by sending the wrong offers to the wrong groups of customers. Significant advances in software automation will start to enable more organizations to rapidly understand the incremental impact of each of their campaigns and instantaneously apply those learnings to the next outreach.

Further, while MSOs have historically relied on highly trained analysts and statisticians, as more organizations see the potential in their data, they will begin to provide more decision-makers with intuitive analytic platforms that enable them to use data to inform each decision, not just a select few.

3. Quad Play: Figuring Out the Expansion of Offerings
In an environment where customer retention is more critical than ever, the importance of Quad Play is growing. More companies are looking to bundle additional products and services with the traditional Triple Play to increase ARPU and increase customer stickiness. AT&T has broadly expanded their reach with the acquisition of DirecTV. Some MSOs may seek to build or strengthen partnerships to offer their own version of an expansion of offerings. The traditional quad play adds wireless, though other service offerings (e.g. home security) may also be in the running.

However, some companies may be struggling to effectively sell quad play. Verizon CFO Fran Shammo recently noted, “It is very difficult for some reason in the U.S. to sell a quad play.” It’s the classic question of how to provide the right products at the right price to the right customer (and with the right message), but now in a much more complicated environment. As MSOs explore traditional quad plays and other new bundles, they need to quickly determine the right products and the right discounts, how those vary by customers, and what the right interplay is between the acquisition, cross-sell side, and retention sides of their businesses. Further, they need to compare all of those possibilities with the potential price upside of offering those same products a la carte.

The industry is changing quickly – and customer preferences and expectations are changing quickly too. MSOs need to experiment with new ideas so that they can zero in on the right answer as soon as possible.

4. Differentiating With Brick and Mortar
As has recently been true in the retail world, as more sales go online, the optimization of the in-store experience is actually becoming more important. Face-to-face touchpoints are becoming rarer, and so they need to be treated with care to make sure the right effects come from those limited opportunities. The store can still be used to form and expand connections with customers, in addition to differentiating customer service from pure-play online competition. Additionally, as the trend of consolidation between wireless providers and wirelines providers continues, wireline providers may have access to a substantial network of physical locations. The recent AT&T and DirecTV deal gave DirecTV a wide network of stores to potentially leverage, which may cause concern for MSOs that could now face pressure to build a physical presence. For MSOs, experience stores – such as the new “Studio Xfinity” – may serve as great tools for customer acquisition, retention, and cross-sell. By giving customers the chance to test-drive innovative new products, businesses get customers to explore products that they would not otherwise have considered.

However, as with any new initiative, it is difficult to figure out which store investments will pay back. Which increase customer satisfaction? Do customers who interact with associates in the store have higher retention, or would those customers have renewed their contract anyway? As MSOs consider such investments, experimentation will be a critical component to understand how in-store interactions affect behavior across channels (e.g., does a customer who goes through an in-store product demo spend more money on video-on-demand servicestwo months later?).

5. Consumerization of B2B
The B2B telecom market has grown faster than the B2C market over the last five years, according to a McKinsey report. Given the saturation in the consumer market and the potential upside in offering business services, focusing on B2B may be a smart growth strategy. While the upside is strong, some consumer-first organizations have under-invested in developing their B2B sales and marketing capabilities, the resources devoted to optimizing those capabilities, and in some cases their ability to track data and analyze B2B sales.

Recently, we’ve seen companies circling back to reinvest in B2B, and we expect this trend to continue and intensify in 2016. Companies need to develop a more data-driven understanding of what levers are affecting their business clients, and how it varies by type of client. It will be crucial to develop and focus analytic rigor on their business side (particular the high-volume areas of small and medium businesses) that starts to approach the levels they’ve been applying on the consumer side for years. Given the fast pace of the industry, acting on a good answer today may be more valuable than acting on a better answer in a year. Companies can’t afford to wait for their data to be perfect before using it, otherwise they’ll fall behind. As such, as they build out these capabilities, they need to be capabilities that can evolve with the organization: they need to work both in the messy here-and-now, and in the more clean-and-organized future.

In this rapidly transforming industry, the companies that come out as winners will be those that continue to experiment and innovate. Whether launching new brick and mortar stores or new streaming services, it will be critical to rapidly figure out which ideas will truly lead to profitable outcomes. As we ring in the new year, MSOs should plan which innovations they want to test, refine, and roll out in 2016.

Jared Brown is a vice president and Marek Polonski is a senior vice president at Applied Predictive Technologies, an Arlington, Va.-based analytics software firm.

Few know and appreciate that one of the best examples of the power, indeed the magic, of modern data analytics is Netflix’s series House of Cards (HOC).

Indeed, HOC registers as one of the most successful TV and video programming introductions of all time. Yet, what is most remarkable is how HOC was developed and introduced. As HOC star Kevin Spacey explained in his iconic YouTube speech, “Netflix was the only network that said, ‘We believe in you…we’ve run our data, and it tells us the audience would watch this series’” (emphasis added).

Netflix replaced the traditional (and very expensive) “pilot” TV test process with intimate knowledge (via intensive data analytics) of the Netflix viewer. Netflix used its sophisticated data analytics as a basis to invest $100 million upfront in HOC; then, as a basis to green light HOC for at least four seasons (thru 2016), and for today’s 39 completed episodes. Today, Netflix producers envision a 12-season collection of HOC, through 2023.

Anther Example: AMC and 'Mad Men'

Other ways in which data about viewers is being collected are nearly as interesting. AMC, the network home of The Walking Dead and Mad Men, has also taken a fascinating lean toward data collection and analysis.

As noted in a recent, free IBM-New Bay Media whitepaper, Vitaly Tsivin, AMC’s SVP of business intelligence, used IBM’s advanced analytics to get a richer picture of who its viewers are, what they want, and how to keep their attention in an increasingly crowded entertainment marketplace.

“We need to know who’s watching and why, and we need to know it quickly, so that we can decide…whether to run an ad or a promo in a particular slot during tomorrow night’s episode of Mad Men,” Tsivin said.

Much of the challenge comes from having so much information available – hundreds of billions of rows of data from industry providers such as Nielsen and comScore; from channels such as AMC’s TV everywhere live Web streaming app and video-on-demand service; from retail partners such as iTunes and Amazon; and from third-party online video services such as Netflix and Hulu – and the need to analyze data minute-by-minute and viewer-by-viewer.

And CBS, Too

A recent article talked of CBS News Digital (CBSN), a free, ad-supported streaming news network, available on the Web, via mobile and on connected TVs. Providing 15 hours a day, typically, of news-related content, CBSN taps into its deep well of video viewer data and analytics, much of it via social media, to determine what  viewers want to see (or see more of).

The data analysis also helps CBS News strategize what content to promote on its social channels. Additionally, data influences CBSN content in the presentation itself, i.e., if the average age of the viewing audience is 40 years old, a subtle but important change involves dressing male news anchors without ties, and having all anchors stand instead of sit behind a desk occasionally.

CBSN is another prime example of how networks and others in the ecosystem are accessing data and analyzing it to drive better viewing and thus more profits in the making and distribution of content.

10 Lessons

The IBM white paper mentioned above spells out 10 key lessons that members of the telecom, media, and entertainment ecosystem need to consider and eventually understand and implement if they are to be successful in marketing and selling future content, including (1) Know Your Goals; (2) Know the Viewer; and (8)Realize the Vastness and Potential of Today’s Analytics.

A current telecom adage has it, “We will see more change in the next 10 years than we have in the past 100.” Data analytics in the media, entertainment and telecom worlds (especially the video part of those) is a perfect example of this very lesson.

Jimmy Schaeffler is chairman and CSO ofThe Carmel Group, a streaming/broadband, broadcast and pay TV/video consultancy based in Carmel by the Sea, Calif.; he writes about telecommunications, entertainment and media.