Can "Digital" Telco Sales be Measured?

Digital transformation is a term that can mean next to nothing. Consider a recent survey of connectivity service providers suggesting that retail stores and call centers account for 71 percent of sales, classified as physical channels. 

source: Upstream Systems 

By way of contrast, sales driven by email, websites, text messaging, data rewards programs or push notifications of some sort are considered “digital” channels. 

It is fair to say that the classification of sales “by channel” is imprecise and open to many judgment calls. Sales might be fulfilled physically, at a store or by call center order, when the sales process actually began with a digital channel. Conversely, sales might be fulfilled digitally when the sales process began physically. 

Word of mouth and advertising also play a part in consumer sales. How a sale is closed is one thing. How a sales journey began and was sustained might be quite another matter. 

The problem is that sales attribution cannot account for such nuances. Where and how revenue was booked is the only way most consumer sales are tabulated. So actual sales attribution is distorted. 

Some might argue that everything contributing to a sales journey is “marketing,” while “sales” refers strictly to a closed transaction with revenue attached. The larger point is that it is hard to differentiate between digital and physical channels for sales. Sales fulfillment might be the only thing we actually are measuring.

Is Covid a White, Grey or Black Swan? It Matters

Is the Covid-19 a white swan, a grey swan or a black swan? The answer portends the amount of disruption we could see, post-Covid. 

A “white swan” event that is one that could have rationally been expected to happen at some time, and has major effects. If Covid-19 was a white swan, the world will not be disrupted as much as many expect. Essentially, change will happen without expected and “normal” statistical ranges. In other words, Covid will have about as much impact as a normal recession. 

White swans are said to have relatively few negative implications, impacting the life of one or a group of people rather than the entire globe. 

Grey swans--neither a completely unexpected black swan nor a predictable white swan--can be devastating for many, and can have radically unsettling implications. To be sure, black swan or grey swan events can be positive, not just negative, though the most common outcome is a negative impact. 

The main difference between a grey swan and a black swan event is that one is known about beforehand, while the other takes us completely by surprise. But the bottom line is that a grey swan event can still be disruptive and devastating. 

If Covid-19 was a grey swan, we might see unexpectedly large outcomes, in terms of change. 

A true black swan event is typically expected to have disruptive implications. Black swans are outliers, events that are outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. 

Climate change, population growth or rising debt levels could be grey swans. Long-term water shortages could be a grey swan. 

Black swans typically have “extreme” or “massive” impact, exposing fragility in ideas or practices that had underpinned “normalcy.” Many would consider the personal computer and the internet as examples of black swans. 

Keep in mind that highly-disruptive events can still be white swans. Some consider the internet bubble burst of 2001 and the Great Recession of 2008 to be white swans. If that is the test, a grey swan or black swan would have unimaginable consequences. 

Others might consider the 2001 internet bubble burst a white swan, but the Great Recession of 2008 a black swan. World War I, World War II, the fall of the Soviet Union, the rise of Islamic fundamentalists, 9/11 and the internet might be called by some black swan events.

Others might consider the Great Depression and World War II as a single black swan event. Some consider the sinking of the Titanic a black swan event. Others might consider larger events, such as the Spanish defeat of the Aztecs, to be black swan events. 

The point of black or grey swan events is not “how to prevent them.” By definition, they cannot be predicted or prevented. But organizations can try and create robustness for their occurrence. 

Small businesses, sadly, almost by definition cannot create much robustness. 

Many businesses--large and small--will survive or die based on when economic activity can be resumed on a “normal” basis. A survey of U.S. respondents taken mostly in January 2021 suggests most believe it will be six months until they feel comfortable attending large public events. 

A plurality say it will be six months until they are comfortable taking a vacation. Other life sustaining activities--such as going to work--are largely believed safe now. A majority of respondents feel safe shopping in retail stores now. Less than half are comfortable eating at a restaurant. 

If these attitudes do not change, many restaurants and travel-related businesses will not be in business by the end of 2021. Failures in many OECD countries will eliminate more than three percent of jobs. 

source: Civic Science 

The number of active business owners in the United States plummeted by 3.3 million or 22 percent in just two months from February to April 2020. The drop in active business owners was the largest on record, according to the National Institutes of Health. 

By December 2020, 42 percent of small businesses surveyed on Alignable said they were in danger of going out of business. By the fall of 2020, at least 49 percent of businesses reported they were unprofitable. Some 18 percent were operating at about breakeven levels. 

It almost does not matter whether economic shutdowns and travel bans are classified as white, grey or black swans. For small businesses, they have the impact of a black swan, even if other segments of the economy will not be so affected. 

It remains unclear whether Covid will prove a black swan for cruise lines, retailing, the travel industry, airlines, entertainment or real estate. As with small businesses, Covid might well turn out to have black swan impact on some industries. 

Causation is Clear for Short-Term Rentals, Less Clear for Broadband

“Who benefits and who loses?” is a reasonable question for analyses of public policy and economic studies. As with all questions related to public policy and economics, though, correlation is easier to demonstrate than causation, simply because the number of variables is so great. 

In the connectivity business, the issue is most common in analyses of broadband impact on economic growth, household income or job growth. We assume broadband “causes” economic growth, but correlation is not causation.  

We assume quality broadband is associated with job growth, but cannot prove causation. We assume better mobile broadband also “causes” economic growth, so we might also believe 5G will similarly cause growth. We might be wrong. 

It also is plausible that areas with strong economic growth, above-average household income and wages, higher educational levels and wealth create demand for better broadband. In other words, demand for broadband is a result of strong economic growth, rather than its cause. 

Other cases arguably have stronger causation relationships. Consider the argument that use of short-term rental apps causes a reduced supply of housing and higher housing costs. It seems plausible for the simple reason that housing vacancies in any market tend to be fixed, and rental property managers can make choices about how to market their rentals: short term or long term. 

The issue is how much difference a robust short-term rental has. It also seems plausible that the biggest impact should be in “touristy” areas where there is high demand for short-term housing. Areas with modest tourist demand should also have modest demand for short-term housing. 

It also seems plausible that the greatest effect is in “touristy” areas that also are affluent. 

Airbnb leads to a reduced supply of housing as properties shift from serving local residents to serving Airbnb travelers, which hurts local residents by raising housing costs, according to Josh Bivens, Economic Policy Institute director of research. 

That assessment seems mirrored by some other studies. Short-term rentals using apps such as Airbnb contribute to housing shortages and rent increases, according to Felix Mindl and Dr. Oliver Arentz, researchers at University of Cologne in Germany. 

They attributed 14.2 percent of overall rent increases to short-term rentals or 320 euros ($385) per year for new tenants.

“While a large proportion of hosts can be considered home sharers, we find an increasing proportion of providers who have developed a professional business model from short-term rentals,” Mindl said in a statement. “Professional short-term rentals are available to tourists throughout the year, and thus compete directly with long-term tenants, for whom the rooms are then no longer available.”

Researchers also have found that in local neighborhoods with a lower share of owner-occupancy, Airbnb had a higher impact on rising housing prices and rents. In areas with a higher share of owner-occupancy, Airbnb had somewhat less of an impact on property prices and rents.

The study also found that the total supply of housing was not affected by the entry of an Airbnb property in a given neighborhood, and that Airbnb listings tend to increase the supply of short-term rental units, while contributing to a decrease of the supply of long-term rental units.

Aside from the presumed effect on housing, short-term rental apps also shift revenue between lodging suppliers. As with sports stadiums, which arguably mostly shift spending from one form of entertainment to another, short-term rental apps shift revenue from hotels to individual property owners.

“The most obvious benefit stemming from the creation and expansion of Airbnb accrues to property owners who have units to rent,” EPI noted.

There are other issues, though. The housing market is affected by forces other than Airbnb, such as gentrification and economic trends. A one-percent increase in Airbnb listings is causally associated with a 0.018 percent increase in rental rates and a 0.026% increase in house prices, other researchers argue. 

“In aggregate, the growth in home-sharing through Airbnb contributes to about one fifth of the average annual increase in U.S. rents and about one-seventh of the average annual increase in U.S. housing prices,” say researchers Kyle Barron, Edward Kung and Davide Proserpio. 

In contrast, annual zip code demographic changes and general city trends contribute about three fourths of the total rent growth and about three fourths of the total housing price growth.

“These results translate to an annual increase of $9 in monthly rent and $1,800 in house prices for the median zipcode in our data,” they say.

The biggest impact comes if a long-term rental unit is converted to a short-term rental unit on a full-time basis, as that subtracts one living unit from the long-term rental stock. On the other hand, an owner-occupied home that rents a room in that house does not do so.

How Might AI affect Trusted Advisor Businesses?

Information disparity explains why enterprises and smaller businesses buying information technology use consultants and system integrators. IT decisions often are complex, almost always lie outside the buyer’s domain expertise and can be significant choices with long-term consequences. 

Some 64 percent of 500 respondents to an Avant Communications poll of U.S. based enterprise decision makers use third party consultants, for example. 

source: Avant Communications 

The role of the advisors is to reduce buyer risk by providing expert advice, and therefore is an “information” service. At a high level, that raises the question of what happens--eventually--as artificial intelligence advances.

source: Avant Communications 

Predictably, experts in fields ranging from finance to law to  management consulting argue that AI cannot replace human judgment and insight. Others point to growing AI roles and argue AI will replace trusted advisors. To be sure, most expect AI displacement for any jobs with routine tasks.

The issue is that AI will become more capable over time, allowing AI systems to potentially displace many more functions at a higher level, even when those human-supplied functions involve “advice.” 

Consider the advice consultants can provide. Much of that advice concerns the choices buyers should be making to place advertising, buy one technology over another, evaluate the life cycle costs of such choices or use cloud computing in place of premises computing on owned hardware, for example. 

In the connectivity business, many of the buyer choices involve MPLS, SD-WAN, cloud computing, security or unified communications. But the value of AI is, in part, the ability to automate any rule-based systems. And the choices between MPLS and SD-WAN are largely rule-based, in terms of total cost of ownership. 

Increasingly, the total cost of ownership choices between cloud computing and do-it-yourself computing are understood and can be routinized. That does not mean complete replacement of trusted advisors, but arguably moves the value of those advisors “up the complexity stack” to areas where benefits and costs are more intangible. 

To the extent that information disparity creates the value added by third party trusted advisors, AI is going to erode the extent of that information disadvantage, and therefore alter the places where advisors can add value. 

So AI will start to replace the lower-order information mismatches at first, then gradually begin to replace more-complex mismatches over time. McKinsey studies suggest AI will replace parts of tasks, perhaps 30 percent of job tasks by about 2030. That might not fully replace jobs, but will change them. 

But most expect the changes to deepen over time, as AI becomes more capable, and can handle more complex information-related tasks. The shift of U.S. jobs from farming to other pursuits did not happen immediately, either, and was not directly affected by information technology as by mechanical technology. But it happened. 

Some 90 percent of U.S. residents once worked on farms. Today about one percent do so. Digital transformation, defined as the use of digital technologies to create new--or modify existing--business processes, culture, and customer experiences to meet changing business and market requirements, has been going on since the early 1980s, for example. 

Few students today realize that, in 1977, nobody used a personal computer at school. Nobody used the internet or a mobile phone. In 1984 perhaps eight percent of U.S. homes had a computer.  

Businesses did not routinely begin using PCs until after IBM introduced its first IBM PC in 1981. 

The point is that AI, as have other forms of digital transformation, starts slow. But transformation does eventually happen. People do not work, learn or live the same way. Eventually, a wide range of tasks now performed by trusted advisors will be handled by AI-based apps and services. 

“Advice” will be among those tasks. The only issue is how much advice can be provided by algorithmic systems.

Videoconferencing Makes a Breakthrough

If you have followed predictions for use of videoconferencing or telepresence for any length of time, you know that expectations have been high, both for services as well as business instructure. AT&T marketed its Picturephone service as early as the 1970s, with modest uptake. 

By some estimates, AT&T invested $6.6 billion to $6.7 billion in current currency to develop Picturephone, only to shut it down because there was no sustainable business model. 

These days, much videoconferencing--both consumer and business use cases--is an app, not a service. But it is noteworthy that consumer and business use of videoconferencing made a huge leap during the Covid-19 stay-at-home and work-from-home policies, virtually doubling in the spring of 2020.

source: U.K. Office of National Statistics, Benedict Evans  

You would be hard pressed to find any single quarter, in the history of videoconferencing, when usage climbed that much.

Good Illustration of Covid-19 Impact on Underlying Trends

This is a good example of the impact of Covid-19 economic lockdowns on technology adoption in general, which is that the economic lockdowns and work-from-home policies pushed any number of existing trends to a sharply-higher level. 

This data shows e-commerce share of total retail sales, excluding autos, fuel, restaurant activity, got a sharp bump because of the pandemic, presumably shifting the curve upwards, but possibly not altering the long-term growth rate. 

source: Benedict Evans 

This illustrates what is meant when observers say “we saw a year’s worth of growth in a few months.” 

What is not clear is whether the underlying long term rate of growth has been altered, or not.

Some Covid-19 Winners Boosted AI Spending Because Financial Advantage was Seen

In virtually every recession or economic panic, some firms, in some industries, are able to boost sales, take market share and maintain or even boost profit margins. Often known as high-performing firms, there might not be a single clear reason for success.

The firms could be in growing young markets, have leadership or capital access advantages, have made astute technology investments, or simply be in industries that benefit from the particular crises or recessions.

Likewise, some firms, across industries, seem to have continued to invest in artificial intelligence during the Covid-19 pandemic, and those firms seem to have done so because they saw clear advantages to use of AI in ways that helped their business models.

If the Covid-19 pandemic affected enterprise information technology investments, it arguably has slowed such investments at some firms, which have had to shift support to remote workers. On the other hand, some firms who already have found use cases, and invested more heavily prior to the pandemic, seem to have increased their investment level, a  McKinsey survey found.

Respondents from 61 percent of firms who report success with AI also say their firms increased investment in 2020. Patterns across industries show big variations.

 source: McKinsey

Firms in healthcare, pharma, medical products; as well as companies in the automotive industry were most likely to have increased AI investments in 2020. 

 source: McKinsey

Most enterprises likely have not yet found clear financial benefits from AI deployment, though. A survey of more than 3,000 company managers about their AI spend found just 10 percent had gotten significant financial benefits from their investment so far, a report from MIT Sloan Management Review and Boston Consulting Group found. 

A separate survey of U.K. firms found that 40 percent of 750 surveyed U.K. executives plan to invest in artificial intelligence in 2021, a survey by Fountech Solutions finds. 

  

Some 30 percent of respondents say their firms piloted an AI solution for the first time since the onset of the Covid-19 pandemic. New AI specialists will be hired by 41 percent of respondent firms. Also, 48 percent of respondents say their companies will seek AI training for existing staff.

As a rule, some firms managed to grow revenue and profit during recessions and crises, Boston Consulting Group data suggests. While 44 percent of firms might experience shrinking profit margins and sales growth in a recession or crisis, 14 percent have shown growth in both sales and profits. 

Some 28 percent of firms see lower sales but manage to increase profit margins. About 14 percent of firms see higher sales and lower profit margins. 

Firms in health and consumer staples are most likely to see winners in recessions. Companies in energy, information and communications technology and financial industries are least likely to emerge with higher sales and profits in a recession.

source: Boston Consulting Group 

It is possible--even likely--that firms continuing to invest in AI during the Covid-19 pandemic were already finding themselves gaining market share, increasing sales volume and maintaining or increasing profits. 

The McKinsey survey found, for example, that a small number of respondents at some firms attributed 20 percent or more of their firm earnings before interest and taxes (EBIT) to AI. Those companies planned to invest even more in AI during the COVID-19 pandemic. 

That is in keeping with the BCG data suggesting some firms gain market share and boost sales during recessions and crises. If such gains are attributed to AI, it makes sense that firms would maintain or boost such investments.