Why Low Cost Bandwidth Now is an Imperative

Low cost bandwidth always has been a prerequisite for commercial video networks, whether of the satellite, over-the-air TV or cable TV variety, and now for any internet service provider or telco (access or connectivity provider) that wishes to sell its own subscription video products.

The reason is simple and straightforward: video is the most bandwidth-intensive consumer application, by far. Text messaging and voice require use of almost no bandwidth, while video consumers nearly two orders of magnitude more capacity, for each minute of use.

H.264 Skype video conferencing, for example, uses four orders of magnitude (10,000 times) more bandwidth than Skype messaging, for example.

So revenue per bit and data consumption are inversely related: video consumes the most bandwidth, and produces the lowest revenue per bit for a connectivity provider, while text messaging and voice use the least bandwidth and produce the highest revenue per consumed bit.

source: Microsoft

Internet traffic is in between, with some apps consuming little capacity (email), some apps consuming a moderate amount of capacity (web browsing) while others are heavy capacity consumers (video).

Mobile networks have had cost per delivered bit an order of magnitude (10 times) higher than fixed networks, as well.

The point is that,  in an era where 70 percent of traffic is video, revenue per bit becomes a major issue. That is especially true when the video is supplied by third parties, generating no direct revenue for the connectivity provider.

source: FCC

There are both supply and demand drivers of lower cost-per-bit performance. On the supply side, there is a tendency for communications cost per bit to fall towards zero, resulting in near-zero pricing.

Over time, better technology has resulted in networks that can deliver far-more bandwidth at far-lower costs. Moore’s Law, optical fiber, satellite and microwave platforms, hybrid fiber coax, digital subscriber line, better codecs and radios, better modulation schemes, radios and compression algorithms are some of the drivers of better performance and lower cost per bit.

But there also are demand issues. In an era where transport and access economics are driven by entertainment video, networks and retail pricing must be optimized for delivery of video, including video that has to be delivered without direct revenue earned by the ISP (third-party video).

And that dictates very-low-cost bandwidth platforms, as consumer propensity to pay is sharply limited. Any given household or any single consumer will only be willing to pay so much on all communications and entertainment services. That figure typically is single digits worth of income.

Developed nation consumers generally pay about 0.7 percent of gross national income per person for mobile internet access. Fixed network internet access, in developed countries, costs less than one percent of GNI per person.

source: ITU

All U.S. household spending on entertainment of any sort represented about five percent of household spending in 2017, according to the U.S. Bureau of Labor Statistics.

If about 37 percent of entertainment spending is for video subscriptions, then perhaps two percent of household spending is devoted to video subscriptions.

source: BLS

The point is that consumer propensity to spend on communications and subscription video is limited to relatively fixed percentages of income that do not change much, year to year. And that puts a cap on potential supplier revenue.

The implications for networks are obvious: platforms can only afford to spend so much on infrastructure, as there are limits to consumer willingness to spend on communications and subscription video.

Cable and Telco Spend 4.4 to 5 Times As Much as Mobile Operators on Capex, as a Percent of Revenue

At least in the Canadian communications market, mobile networks are three to four times less costly than fixed (cabled) networks, based on capital intensity.

With the caveat that the mobile figures might be periodically higher when a next-generation network is under construction, ongoing capital investment is about nine percent of revenues.

Telcos tend to invest about 40 percent of revenues on their networks, while cable operators tend to invest about 45 percent of revenue. Those figures probably require some explanation.

In other words, telco networks spend about 4.4 times as much on capex as mobile networks, as a percentage of revenue. Cable operators spend five times as much as mobile operators.

Cable networks have historically been less capital intensive than telecom networks. But cable operators also are energetically investing in gigabit internet access, while most telcos are more measured in their spending. In part, that is because of the high cost of upgrading copper to fiber access facilities.

But telcos likely also are measured in their assessment of revenue upside from fiber upgrades. In other words, they might rationally conclude that there is no business case for rapid fiber upgrades, especially given revenue declines and a cable TV advantage in internet access and video services.

source: CRTC

Mobile services revenue generated 51 percent of all service provider revenue in Canada in 2017, the CRTC reports. Fixed network internet access generated 23 percent of total revenue.

Only mobility services and fixed network internet access sources grew; all others declined in 2017, CRTC says. And 34 percent of total revenue is earned by cable TV companies.

source: CRTC

source: CRTC

source: CRTC

Not Every Service Provider Can Enter the Video Content Business

Even when connectivity providers agree that development of new revenue sources beyond connectivity is essential, much disagreement remains about precisely how to develop those opportunities.

In large part, the differences of opinion arise from scale requirements. Simply put, many new opportunities require scale that most service providers do not have, and cannot get. Consider the matter of ownership of video content, or acting as a distributor of video content.

Telefonica, for example,  has been a big believer in the value of revenue sources beyond connectivity, and in recent years has boosted its video subscription revenue to about seven percent of total revenues.

And video revenues also have emerged as a huge driver of “digital” revenues. Video subscription revenue now accounts for more than half of total “digital revenues,” for example.

Digital revenues account for nearly 14 percent of total revenues, and are among the fastest-growing revenue sources available to Telefonica. Digital revenues grew more than 25 percent in the third quarter of 2018.

In part, Telefonica’s optimism about video distribution and content ownership flows from its strategic footprint in Spanish-speaking countries, which create a large market for video assets. Such footprints are hard to assemble.

Even if they wanted to become major owners of content assets, as Comcast and AT&T have become, even firms as large as Charter Communications and Verizon cannot afford to do so.

AT&T has joined Comcast as a major owner of video content, movie studios and related assets. But AT&T also has taken on huge amounts of debt to do so.

Verizon, at $126 billion annual revenue, and Charter Communications at about $40 billion annual revenue, do not have subscriber bases, free cash flow and other attributes of scale to acquire major media assets, for example, with CBS, Viacom, Discovery Scripps, AMC and Lionsgate possible acquisition targets, eventually.

Some might argue Netflix remains an acquisition target, but only for a very-well-heeled buyer, and likely far beyond the realm of possibility for a telco or cable company.

source: Recode

Both Comcast and AT&T are big video distribution outlets as well. In fact, AT&T is the largest provider of linear video subscriptions; Comcast the number two provider.

Of course, there also is the example of Netflix, which has become a major owner and producer of original video content, without acquiring major content production assets. Amazon Prime arguably has been less successful than Netflix, to date, but is on the same path.

One might well argue that there are few firms left with the strategic rationale and cash to consolidate the few remaining content assets of any scale in the U.S. market (Disney, CBS). And one might also argue that the logical path forward, for firms with strategic intent, is to follow the Netflix and Amazon Prime approach of directly funding and owning unique content assets.

With the development of the over-the-top streaming, firms such as Netflix and Amazon Prime have found they do not need to build, own or lease network assets to act as video distributors. Importantly, perhaps, the firms already in content include giant technology firms with lots of cash to make acquisitions and investments.

Alphabet, with $59.6 billion in media revenue (advertising revenue or content sales revenue), dwarfs Comcast, with $19.7 billion in media revenue, plus some portion of the 21st Century Fox revenues of $18.67 billion. Facebook already has about $11.49 billion in “media” revenues.

The simple conclusion is that a few connectivity providers have scale to make content ownership a viable strategy. Others will not be able to attempt that strategy, but can make a business out of content distribution. For some, not even distribution will make sense.

For many such firms, horizontal mergers, including out-of-market expansion, might be the only realistic opportunities.

What is Relationship Between Network Slicing, SDN, NFV?

New platforms in the networking business often are hard to classify and categorize. So there is criticism of “fake 5G” or 5G Evolution as AT&T calls it  (advanced 4G using infrastructure that will be shared with 5G) in some quarters, even if networking professionals all agree 5G will be built in large part on advanced 4G infrastructure.

In the same way, we might not all agree on how network slicing, network functions virtualization and software defined networks relate to each other. Some might argue they are nested subdomains. Others might see them as related but distinct domains. Eventually that all will be sorted out. The point is that common understanding has yet to develop fully.

Network slicing is the ability to create multiple customized networks operating on a common physical infrastructure.

Network slicing often is said to be an outcome of network functions virtualization, and many would agree that NFV underpins and enables network slicing. But network slicing (creation of customized private networks) perhaps is more-properly viewed as an application of software defined networks, in the same way that a software-defined wide area network is an SDN-enabled product.  

There probably also is dispute about the business value of platform innovations. Is upside primarily on the operating or capex reduction fronts, new revenue creation areas, or a combination of all those elements? The answers might be somewhat subtle.

source: Ericsson

Network slicing should enable new revenue generation through market stimulation, faster time to market and opportunities from smaller niche services, Ericsson argues.

Market stimulation will come from offering of new customized service level agreements and self-service opportunities, Ericsson believes. But the upside is going to hard to quantify, relying on the value of better service performance, customer experience and customer satisfaction.

Smaller niche service opportunities will become economically viable for operators to explore, providing value through “sandboxing, temporary events and tailored business models,”  Ericsson argues.

Shortened service delivery cycles and simplified, tailored operations will be possible because processes are streamlined.

Capex efficiency Infrastructure efficiency - Network optimizations can be made with slicing, due to the implementation of an efficient traffic model with service type segregation.

Functions in network slices are dynamically scaled according to traffic or service demand, so network resources are more-efficiently used.

Customer Experience Might be Twice as Hard as You Think

“Bothersome experiences” and “shopping delights” are thought by most people to be drivers of retailer abandonment, in the former case, or customer loyalty, in the latter case. Most of us tend to think of the full range of things a supplier does, or fails to do, that can move buyer perceptions in either direction.

Applied to the connectivity business, outages, incorrect billing, long waits for customer service, high perceived prices and low perceived quality of service are seen as drivers of churn. The logical thought is that high availability, correct bills, prompt customer service, reasonable prices and high value are seen as drivers of customer loyalty.

But an argument can be made that the bothersome and delightful dimensions of experience are not linear, on a single scale, but perhaps even two different categories: things that bother customers and need to be avoided, as well as things that delight customers, and have to be created.

In that view, you cannot delight customers by removing irritation: one only removes the bother and the risk of customer abandonment. In other words, no retailer creates delight simply by removing sources of unhappiness. Consider the results of studies by Qualtrics.

source: Qualtrics

In-store irritants (complaints) include rude employees, high prices, items not in stock and long checkout lines. Online irritants include shipments that do not arrive, fake product reviews or misleading or inaccurate descriptions and depictions.

Say any given retailer has all those problems. Say those problems, at significant effort and expense, get fixed.

So now customers in stores encounter courteous employees, reasonable prices, items always in stock and fast checkout. Is that enough to produce “delight?” Maybe not. Maybe that is what customers shopping in stores simply expect. So the reasons to avoid shopping are removed.

The single exception is price, in the in-store context. Shopping are irritated by high prices, and report enjoying unexpectedly low prices. With that exception, the irritant issues and enjoyment values do not overlap.

In an online context, say a retailer fixes irritants by improving logistics, the quality and accuracy of product reviews and descriptions. Again, the question is whether doing so creates a sense of buyer delight, or simply removes a reason not to use the site.

Now consider feedback from shoppers about what they most enjoy about particular retail or online shopping experiences. In-store, the ability to try on a garment, being able to “get out of the house,” unexpectedly-low prices, doing something with friends or family, and serendipitous exposure to products are positives.

Online, shoppers value larger product selection, free shipping, the ability to shop from anywhere and avoiding checkout lines as drivers of enjoyment. None of the major online “enjoyment” drivers are directly related to the complaints.

In-Store and Online Complaint and Enjoyment Drivers
In-Store irritant	In-Store enjoy	Online irritant	Online enjoy
Employee rudeness	Try on garment	Item did not  arrive	Product selection
High prices	Low prices	Fake reviews	Shop anywhere
Merchandise unavailable	Get out of house	Misleading description	Free shipping
Long checkout	Be with friends	Low quality	No checkout line
	Serendipity	Shopping cart	No need to leave house

source: Qualtrics

The point is that it is perhaps not so clear that all consumer interaction issues are on a single scale: high to low, good to bad, irritant or enjoyment.

Instead, there are at least two different scales: presence or absence of “things that irritate consumers,” and presence or absence of “things that delight customers.”

With the possible exception of an expectation of high in-store prices and unexpectedly low store prices, irritants and pleasures do not seem to be on the same scales. They appear to be different dimensions of experience.

AI in Everyday Life: More Common Than You Think

People routinely use electricity and think nothing of it. Artificial intelligence, machine learning and deep learning are likely going to be experienced the same way. Few people can explain how electricity works, but it already powers many parts of modern life.

Equally few are probably aware of how they already use machine learning in everyday life.

Amazon recommends books; Netflix suggests a film or TV show; your email app filters spam using machine learning. Machine learning underlies consumer interactions with Siri, Alexa, Google Assistant.

Personal assistant apps on smartphones use machine learning. Portrait mode cameras use machine learning.

Social media feeds use machine learning to customize your content. Video games sometimes incorporate machine learning to vary app responses based on player actions.   

Machine learning helps Uber estimate how long a trip will take. ML also helps Uber estimate how much you are willing to pay for any particular trip. Riders are matched with drivers using machine learning.

source: KDnuggets

Machine learning powers GMail’s auto-response features. LinkedIn uses machine learning to match jobs with jobseekers. Pinterest uses ML to classify photographs and visual images.

Your bank and credit and debit card providers use machine learning to monitor your accounts for fraudulent activity. ML also powers their reminder and alert systems.

Airliners use machine learning when autopilots are engaged. By some estimates, a typical flight on a Boeing 777 is on autopilot for all but 3.5 to seven minutes in the air.

Customer interaction software uses ML to answer questions and resolve problems at inbound call centers. Search engines use ML when you seek information.

Amazon makes product recommendations for you using ML. Spotify uses ML to make music recommendations. Online ads often are served up with the assistance of ML.

Google Maps uses ML to predict the fastest routes to a destination. Smart thermostats use ML to alter thermostat behavior based on how a user has acted in the past.

ML powers workforce analysis systems that make deductions from email and other written text. Machine learning increasingly can be used to assess organizational risk.  

How AI in general, or machine and deep learning will work to enhance or create products and features are subjects likely of little immediate and compelling interest for most people, whose job responsibilities or life routines do not require any specific knowledge about AI.

AI, almost by definition, works in the background. People use computers, apps and smartphones, but few have any need to understand what happens at the component layer, inside computer rooms or servers. People do not need to understand particular coding languages to use software built with such languages.

You Might be Surprised at the Number of Gigabit Connections Small Rural Telcos Supply

Causes, industries, organizations and policies do not get support at any level of government unless there is a “crisis” or “big problem” to address. And, to be sure, rural internet access or communications services of any type are less robust than in cities, for all sorts of good reasons.

But a balanced assessment would also include the contributions thousands of small rural telcos  and internet service providers already make--in rural areas--to supply internet access under most-difficult conditions. We are talking about service providers in areas where there are 10 or fewer residences per square mile, and many operating in areas where there are but two homes per square mile.

The latest survey of 194 small telcos that are members of the NTCA rural broadband association is instructive. The average (“mean”) respondent organization has 4,355 residential voice lines; 1,493 business lines; 4,455 residential broadband connections in service and 530 business internet connections in service.

You might be surprised to learn that 23 percent of all connections made available by these rural service providers offer at least a 1,000-Mbps connection. Another 34 percent of connections offer speeds from 100 Mbps to about 999 Mbps. In other words, 57 percent of available connections operate at 100 Mbps or faster.

As typically is the case, that does not mean most customers buy the fastest services. They do not. Actual buying clusters in the range between 4 Mbps and 100 Mbps minimums.

Source: NTCA data, IP Carrier analysis  

The service area covered by such telcos is approximately 2,244 square miles. Some 60 percent of respondents have service areas 500 square miles or larger and 27 percent were at least 2,000 square miles.