Surveys are Difficult to Create and Interpret

Getting meaningful data from customer or user surveys are not as easy as many believe. Samples sizes, survey instruments, poorly-designed questions and research assumptions all affect the validity of results. Word chioce is another common source of error. 

Rank order questions sometimes help to discover relative priorities. Consider information technology professional responses to a survey on endpoint management. This set of responses does show some differentiation of “ability to manage” various elements of the IT environment. 

source: Automox 

Those results are not a normal probability distribution. As often is the case with self reporting, all the organizations perform “above average.” If capabilities really are described by a normal probability distribution, that cannot be correct. 

Sometimes researchers are looking for indicators of “most important/least important” opinions. In such cases, forcing respondents to choose can better highlight opinions about what is  “most important.” 

 

source: Automox 

Harder to control are researcher frames of reference. Any multiple-choice survey must necessarily embed assumptions about a few variables researchers believe are relevant. That bias is among the most-common I encounter as someone who gets asked to take surveys.

Disintermediation is at Heart of Many Telco Worries

When the global connectivity industry chose TCP/IP (Transmission Control Protocol/Internet Protocol) as its next-generation protocol, it opened Pandora’s Box. The industry does not presently like the business changes IP was wrought, but can hardly deny that it chose the platform willingly. 

The key problem is disintermediation, the removal of distributors and “middle men” from value chains. In a loosely-coupled ecosystem, participants operate in “layers” or disaggregated roles, much as software can run on a compliant platform without a formal business relationship with the platform or operating system. 

Think of the way modern software runs on compliant hardware, operating systems or networks: so long as the agree-upon interfaces are in place, software, hardware and networks can work seamlessly, without formal business relationships. 

Disintermediation is at the heart of the promise and period of a wide range of potentially-huge new enablers of business and economic life, including blockchain, cryptocurrencies, distributed finance and distributed autonomous organizations. 

source: GSMA 

One example of this is the direct supply of services to enterprises that otherwise might have been provided by a connectivity provider. A recent survey by TelecomTV illustrates the concern. Some 44 percent of respondents believe an inability to compete with the major cloud firms in the enterprise services sector is a major issue.

 

source: TelecomTV 

That’s a classic example of disintermediation.

A Good Definition of Initiative

Omaha Beach was the toughest of all the beaches targeted by the allies on June 6, 1944, on D-Day. "On this two-division front landing, only six rifle companies were relatively effective as units," says S.L.A. Marshall, who was there.

To put that in perspective, a U.S. Army division contains between 10,000 and 15,000 soldiers. So two divisions means 20,000 to 30,000 soldiers. A company contains between 60 and 200 soldiers. So six rifle companies functioning effectively implies somewhere between 360 and 1200 men in organized units that are "combat effective."

On Omaha Beach, "relatively effective" likely is the better description. 

Somehow, individual soldiers, on their own initiative, managed to climb the bluffs and secure the beachhead. A better example of initiative I am hard pressed to provide. 


source: taskandpurpose

source: Thirteen 

My nephew, who serves with the 101st Airborne, (82nd Airborne was the other) who dropped behind German lines) brought me back some sand from both Utah and Omaha beaches he picked up while representing the 101st Airborne at the 75th anniversary of D-Day. Sacred soil, indeed. 

source: National Archives

Supply Chains are Where Either Digital Transformation or Digitization Could be Seen Early in Most Industries

Digital transformation and digitalization (applying new technology to business models, in the former case; to business processes, in the latter case) are perceived as having the greatest benefits for product creation. 

Applied digital technology seems to be seen as a threat to supply chains and distribution mechanisms. 

That would be in keeping with the general impact of internet technologies generally, which have had an impact of varying degrees in product creation, but arguably the greatest impact in changing distribution costs and roles. 

source: McKinsey 

In other words, it is possible that digital transformation and digitization (changing business models and operating costs) will have the greatest business impact as a way of collapsing the distribution portion of all value chains. “Cutting out the middleman,” or disintermediation, could be the area of greatest immediate change in many industries.

FTTH Payback Models Have Changed

Larger U.S. internet service providers using fiber-to-home platforms sometimes continue to face an excruciatingly difficult business case for such investments. Consider Lumen Technologies, which has been relatively slow to upgrade copper access to FTTH. Lumen says average revenue per user (account) for new fiber connections is $59 a month. 

source: Lumen Technologies 

If average FTTH capex is about $1,000 per passing, and take rates are about 40 percent, then capex per account is about $2500. At $59 a month revenue, annual proceeds are about $710 per account.

That can make for a long payback cycle, which is why assets are being purchased by more-patient investors such as private equity, pension funds and other institutional investors. Such investors buy access assets as an alternative investment that produces predictable cash flow and offers some diversification from other asset classes. 

Recent presentations BY Frontier Communications also have shown fiber-to-home home broadband average revenue per user of about $63. 

source: Frontier Communications 

That is lower gross revenue than many had expected three decades ago. Where a triple-play bundle might have produced $130 per month to $200 per month revenues, home broadband might produce $50 to $80 a month. 

With the shrinkage of both fixed network voice revenues and entertainment video, ISPs increasingly must build their revenue models on home broadband. 

And payback models have changed. Increasingly, it seems, capex costs are not the most-important element of such models. Instead, take rates matter much more. Customer density and competitive conditions still matter, but government subsidies and expected equity value increases also are a factor. 

The former aids the investment cost; the latter increases the total expected return by increasing exit multiples or exit prices. 

That new FTTH projects increasingly are feasible with a $50 to $60 monthly revenue target and adoption around 40 percent to 50 percent shows how much the capex and opex assumptions have changed over the past three decades. 

Of course, some ISPs are able to justify the dense fiber networks by including the benefits of fiber to support cell sites and business customers. Government subsidies also help. 

Nor are profit margins in home broadband especially high. AT&T profit margins for new broadband builds are said to produce profit margins “in the mid- to upper teens,” AT&T has said. 

Such revenue prospects are one reason why co-investment has grown in popularity. If expected revenue is at such levels, a reduction in capital investment burdens is necessary.

Innovation Takes Time, Be Patient

Anybody who expected early 5G to yield massive upside in the form of innovative use cases and value has not been paying attention to history. Since 3G, promised futuristic applications and use cases have inevitably disappointed, in the short term. 

In part, that is because some observers mistakenly believe complicated new ecosystems can be developed rapidly to match the features enabled by the new next-generation mobile platform. That is never the case. 

Consider the analogy of information technology advances and the harnessing of such innovations by enterprises. There always has been a lag between technology availability and the retooling of business processes to take advantage of those advances. 

Many innovations expected during the 3G era did not happen until 4G. Some 4G innovations might not appear until 5G is near the end of its adoption cycle. The point is that it takes time to create the ubiquitous networks that allow application developers to incorporate the new capabilities into their products and for users to figure out how to take advantage of the changes. 

Non-manufacturing productivity, in particular, is hard to measure, and has shown relative insensitivity to IT adoption.

source: MIT

Construction of the new networks also takes time, especially in continent-sized countries. It easily can take three years to cover sufficient potential users so that app developers have a critical mass of users and customers. 

And that is just the start. Once a baseline of performance is created, the task of creating new use cases and revenue models can begin. Phone-based ride hailing did develop during the 4G era. 

But that was built on ubiquity of mapping and turn-by-turn directions, payment methods and other innovations such as social media and messaging.

Support for mobile entertainment video also flourished in 4G, built on the advent of ubiquitous streaming platforms. But that required new services to be built, content being assembled and revenue models created. 

The lag between technology introduction and new use cases is likely just as clear for business use cases. 

The productivity paradox remains the clearest example of the lag time. Most of us assume that higher investment and use of technology improves productivity. That might not be true, or true only under some circumstances. 

Investing in more information technology has often and consistently failed to boost productivity.  Others would argue the gains are there; just hard to measure.  There is evidence to support either conclusion.

Most of us likely assume quality broadband “must” boost productivity. Except when it does not. The consensus view on broadband access for business is that it leads to higher productivity. 

But a study by Ireland’s Economic and Social Research Institute finds “small positive associations between broadband and firms’ productivity levels, none of these effects are statistically significant.”

“We also find no significant effect looking across all service sector firms taken together,” ESRI notes. “These results are consistent with those of other recent research that suggests the benefits of broadband for productivity depend heavily upon sectoral and firm characteristics rather than representing a generalised effect.”

“Overall, it seems that the benefits of broadband to particular local areas may vary substantially depending upon the sectoral mix of local firms and the availability of related inputs such as highly educated labour and appropriate management,” says ESRI.

Before investment in IT became widespread, the expected return on investment in terms of productivity was three percent to four percent, in line with what was seen in mechanization and automation of the farm and factory sectors.

When IT was applied over two decades from 1970 to 1990, the normal return on investment was only one percent.

This productivity paradox is not new. Information technology investments did not measurably help improve white collar job productivity for decades. In fact, it can be argued that researchers have failed to measure any improvement in productivity. So some might argue nearly all the investment has been wasted.

Some now argue there is a lag between the massive introduction of new information technology and measurable productivity results, and that this lag might conceivably take a decade or two decades to emerge.

Work from home trends were catalyzed by the pandemic, to be sure. Many underlying rates of change were accelerated. But the underlying remote work trends were there for decades, and always have been expected to grow sharply. 

Whether that is good, bad or indifferent for productivity remains to be seen. The Solow productivity paradox suggests that applied technology can boost--or lower--productivity. Though perhaps shocking, it appears that technology adoption productivity impact can be negative. 

All of that should always temper our expectations. 5G is nowhere near delivering change. It takes time.

Mobile Innovations Often Fail to Arrive as Predicted

Exponential technology change never is matched by exponential human and culture change, which means the use of technology by humans will lag what  the technology enables. As rates of change climb, it is reasonable to assume that what “can be done” will diverge from “what is being done.”

Much of the capability change is driven by Moore's Law and its effect on computing power and cost. 

source: Intel

As computing costs decline, capabilities are embedded where it would not have been commercially possible in the past. That drives innovation. 

  

source: Cadbury Communications 

But humans, organizations and culture do not change at exponential rates. 

So even if we can envision implanted communications, it is unlikely to happen as fast as some predict. 

source: Astro Teller 

In fact, it is normal for developers and hardware designers to be “behind the curve” where it comes to matching product capabilities with technology advances. That is the case for 4G internet of things products, for example. 

source: Embedded Computing 

For such reasons, we commonly see forecasted innovations fail to arrive as early as expected. That will likely be true of implanted communications, as has been the case for other innovations. 

The established trend in mobile communications, for example, has been for product expectations to take a decade or more to achieve commercial adoption. If implanted communications devices are expected to displace phones in less than 10 years, it might take 20 years to happen at scale. 

Some innovations will simply never happen, and others will take 30 years or more to arrive.