Pipe or Platform?

For all the talk of the value of “platforms” in the internet ecosystem, platforms are much harder to create than the typical “pipes” business. So the “good advice” to “become a platform” might apply to only a smallish percentage of all connectivity provider businesses. 

A platform business model essentially involves becoming an exchange or marketplace. A pipe model requires a firm to be a direct supplier of some essential input in the value chain.

A platform functions as a matchmaker, bringing buyers and sellers together, but classically not owning the products sold on the exchange. A pipe business creates and then sells a product directly to customers. Amazon is a platform; telcos and infrastructure suppliers are pipes. 

Platform creation is not especially easy for a connectivity services provider. If you think about every business as either a “pipe” or a “platform,” then most businesses are “pipes.” They create a specific set of products and sell them to customers. That is a classic “one-sided market.”

It arguably is easier to build and sustain a business using a “pipe” model than a “platform” model for a number of reasons, but primary among those reasons is that a pipe model only requires knowing what problems the potential buyer faces, and then coming up with a workable and affordable solution. 

Creating a platform model requires understanding the problems that at least two sets of participants may have, and then devising a way to solve each participant’s problem. At a crude level, that means a platform’s business value challenge is twice as hard as that facing a pipe provider. 

Of course, for all the attractiveness of a platform model, it can be argued that creating a platform is much more capital and scale intensive than operating an alternative pipe model. In fact, small firms have almost no choice but to operate using a pipes model. 

Also, some businesses we think of as “platforms” might actually operate as pipes. Amazon’s e-commerce operations are a classic platform. But Amazon Web Services arguably operates as a pipe: it sells customers computing or storage services that actually are “pipe” products.

In other words, AWS is an example of one supplier (AWS) selling a specific product directly to one customer. One might say the same for a single retail brand selling online. That is a pipe model, not a platform, as it features one seller, making transactions with a single buyer. 

Another way of stating this is that a pipe model has customers interacting with a seller; a platform has users interacting with each other. 

Also, the value for different sets of participants might be asymmetrical, which is why platform providers sometimes have to create incentives for one side of the platform to participate. Uber pays its drivers, for example, to ensure adequate availability for riders. AirBNB compensates rental partners for their participation. Expedia pays booking commissions to its travel providers. 

Yet another way of illustrating the difference is that a platform orchestrates interactions and value. In fact, a platform’s value may derive in large part from the actions and features provided by a host of ecosystem participants. Facebook’s content is created by user members. Amazon’s customer reviews are a source of value for e-tailing buyers. 

Consumers and producers can swap roles on a platform. Users can ride with Uber today and drive for it tomorrow; travelers can stay with AirBNB one night and serve as hosts for other customers the next. Customers of pipe businesses--an airline, router or phone suppliers, grocery stores-- cannot do so

You might be aware that firms in an ecosystem often move into adjacent roles within the ecosystem, or challenge incumbents in other ecosystems. Though that can be done by either a pipe or a platform, it might be easier for a platform. 

Network effects and communities both have business value, and platforms might be able to more easily transfer scale advantages to new lines of business. Pipe-oriented firms can do this as well. The difference might be the ease of scaling platform assets, which by definition require wide participation by lots of different firms and industry segments.

A pipe-oriented business will have advantages in its current customer base, ex-customers and suppliers. A platform might already have relationships with supply chain, payment, logistics, marketing or user relationships that are far broader. 

In a pipe business, the seller “creates value” represented by the product sold. In a platform business, value can be created in different ways, such as recommendations. In that case, neither the platform itself or the sellers of products are creating the value, but the customers or users. 

The other issue is that any business using the internet is itself participating on a platform, though perhaps indirectly. And as is true for most products sold or exchanged over the internet, there is a tendency for excess profit to be removed, which means any product available on the internet--by pipe or platform--tends to move towards zero margin. 

Also, many businesses may operate partly as a pipe and partly as a platform. Perhaps the Apple iPhone provides a good example of that. 

Lumen Technologies Generates 75% of Revenue from Business, Wholesale Customers

Lumen Technologies, the former CenturyLink, has an unusual revenue profile for a tier-one service provider. About 75 percent of total revenues come from business, wholesale or international capacity sources, and just 25 percent of total from all services provided to consumer customers. 

source: Lumen Technologies

Other tier-one telcos--such as AT&T, Vodafone and Telstra--might generate as little as 30 percent of total revenue from business customers. 

Smaller, mobile-only or fixed-only operators might generate just 15 percent of total revenue from business customers, Deloitte researchers say.  

To the extent that mobile networks drive the majority of revenue in the global telecom business, and to the extent that most mobile accounts are consumer accounts, Lumen’s high concentration on enterprise or wholesale revenue is an outlier.

Pipes to Platforms? What is Required?

If the “dumb pipes” business model of a transport network is “broken,” the issue for connectivity service provider executives is “what replaces that model?” and “how do we get there?”

Beyond the throwaway platitudes, one possible model is to “become a platform.” But there is much misunderstanding about what a platform is, and what it takes to function as a platform. To the extent that scale is required, a legitimate issue is whether, realistically, every connectivity provider can become a platform.

A platform business model essentially involves becoming an exchange or marketplace, more than remaining a direct supplier of some essential input in the value chain. It is, in short, to function as a matchmaker. 

The platform facilitates selling and buying. The platform allows participants in the exchange to find each other. 

Platforms are built on resource orchestration; pipes are built on resource control. Value quite often comes from the contributions made by community members rather than ownership or control of scarce inputs vertically integrated by a supplier. 

A pipe business focuses more on efficiency in its value chain, where a platform focuses more on orchestrating interactions between members. 

A pipes business success metrics revolve around customer value. A platform emphasizes ecosystem value creation. 

So “lifetime customer value” is a reasonable metric for a pipes business. 

A platform often creates value because of the scale and scope of the interactions between members of the ecosystem, so the range and depth of interactions might be a better metric for a platform. In other words, the platform is easy to join, easy for participants to use and easy to federate. 

Effective application program interfaces are one aspect. But effective logistics, settlements, data exchange, payments and information on ecosystem participant behavior might be other important aspects for ecosystem transactions and interactions. 

Perhaps the best models are multi-product e-tail firms such as Amazon, Alibaba or eBay; ride hailing companies such as Uber or Lyft; content exchanges such as YouTube; payment services such as PayPal; lodging exchanges such as airBNB; food delivery services such as GrubHub;  messaging platforms such as WhatsApp or social networks such as Facebook. 

source: Innovation Tactics 

Platform revenue models also are known as multi-sided business models. In traditional usage, multi-sided business models also were those which earned revenue from buyers (users) and sellers. Newspapers which earned revenue from users (subscriptions) and sellers (advertising). 

The newspaper was a platform--or multi-sided business model--that connected advertisers with audiences; buyers with sellers. Virtually all ad-supported media also used multi-sided business models. The point here is that the platform business model is technology independent. 

As a corollary, just because a business is based on use of technology--such as the internet--does not automatically mean that business is a platform or uses a multi-sided business model. 

Also, “digitizing” business processes does not necessarily make an organization a platform, or change its business model. Virtually all businesses use technology and software. That does not make them platforms.  

In the pre-internet era, operating systems were platforms. Important caveat: platforms can lose their value over time. Few now believe operating systems are key platforms, or have the power they once had as platforms. 

In the internet era, app stores might be considered platforms. What all these business models tend to feature are the ability to match buyers and sellers, rather than direct ownership of some function in the value chain. 

There are some analogies to the traditional telecom business, which though a supplier of direct connectivity services to paying customers, also serves as an intermediary to connect end users on the network. 

The difference is that the telecom service provider business model is not based on facilitating transactions between end users and then building a revenue model on those transactions. Instead, the traditional “fee for service” telecom model involved supplying the function of communications itself. 

“Rather than playing a direct role in the supply chain, companies build digital ecosystems or marketplaces connecting consumers with producers of goods and/or services,”  says the TMForum. 

Almost as a byproduct, internet-era marketplaces are based on electronic marketplaces.

source: Lumen Technologies

If not, the further issue is whether being part of somebody else’s platform, and continuing mainly to supply connectivity, is a viable growth model. 

The platform business model aggregates consumers and producers through an ecosystem. “The real strategic value of a digital platform is to harness the service offerings from a diverse supplier base, and then to use shared orchestration, monetization and administration tools to offer new service bundles,” says BearingPoint.

For PCCW Global, this evolution means creating a “platform” that connects many trading partners in an ecosystem, and not simply providing connectivity services to other service providers, enterprises, data centers, mobile operators and consumers. 

source: PCCW Global, MEF 

As envisioned by PCCW Global, all suppliers of services to end users would be connected to those users over the platform. So application providers could reach their users and customers using the platform; but also other trading partners. 

That would allow for all sorts of innovative solutions to be created, built, modified and supported with something approaching near-real-time provisioning, with automated systems allowing low-cost charging models that essentially allow all sorts of value-generating products to be created, delivered and supported at costs impossible or difficult to support in a manual processes environment.

It will not be especially easy to create such a platform, given the rivalries between hyperscale cloud giants and faster and alternative ways of creating proprietary platforms.

Travel Restriction Impact on Telecom Revenue

Economic shutdowns and travel restrictions have been widely used during the Covid-19 pandemic to control the rate of infection. Sometimes it helps; sometimes it does not. Health policies should, when possible, disrupt economic activity as little as possible, a team of researchers says.

Though primarily affected travel-related industries, such travel bans also negatively affect mobile industry revenues by reducing the amount of roaming revenue. People who are not traveling also are not using their phones out of their home regions. Early March 2020 forecasts were that mobile operators globally could lose $25 billion in roaming revenue. 

In September 2020, research from roaming experts Kaleido Intelligence suggested a 53 percent fall in retail roaming revenues would happen in 2020. According to GSMA, that could represent a revenue hit of as much as four percent to eight percent. 

Combined with other revenue deceleration from reduced new customer acquisitions and upgrades, TBR estimates average revenue growth could dip about six percent in the first half of 2020 alone. Some estimates suggest revenue losses could be far greater, approaching 20 percent in some cases.  

source: TBR 

The issue, some might say, is striking a balance between public health and economic health, especially unemployment and recession, with economic contraction between five percent and eight percent in 2020, compared to 2019. 

The expected 2021 rate of recovery might also depend on how rapidly consumers are willing to resume “normal” life activities. 

Stringent travel restrictions might have little impact on epidemic dynamics except in countries with low Covid-19 incidence and large numbers of arrivals from other countries, or where epidemics are close to tipping points for exponential growth, a team of researchers reports.

“In May, 2020, imported cases are likely to have accounted for a high proportion of total incidence in many countries, contributing more than 10 percent of total incidence in 102 (95 percent credible interval 63–129) of 136 countries when assuming no reduction in travel volumes (ie, with 2019 travel volumes) and in 74 countries (33–114) when assuming estimated 2020 travel volumes. Imported cases in September, 2020, would have accounted for no more than 10 percent of total incidence in 106 (50–140) of 162 countries and less than 1 percent in 21 countries (4–71) when assuming no reductions in travel volumes,” say researchers Timothy Russell, Sam Clifford, W. John Edmunds, Adam J Kucharski and Mark Jit, working on behalf of the Center for the Mathematical Modelling of Infectious Diseases Covid-19 working group and published in the Lancet. 

“Countries should consider local Covid-19 incidence, local epidemic growth, and travel volumes before implementing such restrictions,” they note. “Although such restrictions probably contribute to epidemic control in many countries, in others, imported cases are likely to contribute little to local Covid-19 epidemics.”

As a matter of science, travel bans might or might not have much material impact on rates of new Covid-19 infections. And the benefit has to be weighed against the costs of movement bans on economic performance, as any other public policy should be evaluated, one might argue.

Much IoT Connectivity Upside will be Indirect

For all the talk of the importance of the internet of things for mobile and fixed network connectivity providers, it still is reasonable to ask how much revenue impact IoT actually will have for service providers in a direct sense. 

The biggest impact might continue to be indirect, as IoT deployments rely on some form of local distribution (local area network). Connections to the wide area network still will be necessary, but general-purpose broadband connections might suffice. 

 

source: IoT Analytics 

Mobile networks and local untethered networks such as Wi-Fi presently represent the majority of internet of things device connections, according to GlobalData. In 2020, mobile operators supplied about 32 percent of connections other than narrowband IoT. Add NB-IoT and mobile operators had about 33 percent of connections, while short-range networks supported 51 percent of connections. 

source: GlobalData 

At least so far, most IoT connections supported by mobile operators have relied on 2G or 3G. Going forward, narrowband sensors and devices are likely to be supported by either NB-IoT or Category M networks, while higher bandwidth devices use 4G or 5G, predicts Ericsson. 

source: Ericsson 

“Narrowband” in Ericsson’s view includes all apps requiring up to 7 Mbps upstream or as much as 4 Mbps downstream. Some of us might view that as something more than narrowband, but not broadband (using a 25 Mbps downstream rate as the minimum). We used to call that space in the capacity dimension “wideband.” 

source: Wikipedia 

The nomenclature has gotten a bit confused as some suppliers try to create a marketing platform using “wideband” as “more than broadband.” Personally, I’ll stick with the traditional usage. Narrowband (less than 1 Mbps, wideband (everything between 1 Mbps or 1.5 Mbps and 25 Mbps) and broadband (25 Mbps and above). 

The biggest wildcard is 5G network slicing. Eventually, 2G and 3G will not be available to support narrowband use cases, so the issue is whether the substitute is NB-IoT or LTE-M using the 4G platform, or possibly a 5G network slice optimized for narrowband communications, or relatively “native” support for NB-IoT and LTE-M on the 5G network. 

Latency, not simply bandwidth, is the other crucial issue. In some settings, latency performance--rather than bandwidth--will be the key requirement. In such cases, 5G might be the only real choice, with some form of edge computing also required. 

Range and battery life considerations also will affect connectivity platform choices. Also, cost always is an issue. Wi-Fi connections will cost significantly less than mobile connections (both device capability and the recurring cost of service). 

The larger point is that indirect feature benefit often is quite important for connectivity service provider direct benefit connection revenue. Any features that improve market share, aid new account acquisitions, reduce churn or improve the customer's sense of value contribute to overall revenue opportunities.

Still, the direct value of new IoT connections might be less significant than some expect.

Jefferson Wang on 5G, AI, IoT Relationships

In his talk at PTC’21, (register here) Jefferson Wang, Accenture managing director for network and connected solutions, will explain how 5G, artificial intelligence and the internet of things are directly related. 

At a high level, Wang sees AI as a mediating capability between networks (public and private) and the IoT devices that use those networks. Basically, AI and edge computing orchestrate the interactions between networks, devices and analytics that provide the business value. 

Source: Jefferson Wang, Accenture

Internet Access Too Expensive; Too Slow?

One often hears complaints that U.S. broadband is “too expensive” or “too slow,” as one hears that municipal internet access services are needed to do the job internet service providers will not do. It always is worth evaluating such claims.

Is U.S. broadband “too slow.” Maybe not. About 80 percent of U.S. households can buy gigabit per second service if they choose, looking only at coverage by cable TV networks. Yes, households in rural areas often cannot buy service at such speeds, but speeds improve all the time, for a greater number of locations. 

According to comparethemarket.com, the United States ranks fifth among 50 for downspeeds.

Is U.S. internet access too expensive? Maybe not. According to a new analysis by NetCredit, which shows U.S. consumers spending about 0.16 percent of income on internet access, “making it the most affordable broadband in North America,” says NetCredit.  

In Europe, a majority of consumers pay less than one percent of their average wages to get broadband access, NetCredit says. In Singapore, Hong Kong, New Zealand and Japan,  10 Mbps service costs between 0.15 percent and 0.28 percent of income. 

Back in 2017, actual U.S. broadband speed was more than 100 Mbps, on average, according to Akamai. Upstream speeds varied by location, but are at or above plan goals in most cities, with performance varying by provider.   

Another study by Deutsche Bank, looking at cities in a number of countries, with a modest 8 Mbps rate, found  prices ranging between $50 to $52 a month. That still places prices for major U.S. cities such as New York, San Francisco and Boston at the top of the price range for cities studied, but do not seem to be adjusted for purchasing power parity, which attempts to adjust prices based on how much a particular unit of currency buys in each country.

source: McKinsey  

This chart from McKinsey compares cost trends for various products purchased by consumers in the 22 Organization for Economic Cooperation and Development countries. It shows price changes, indexed to inflation, between 2002 and 2018, covering nearly two most-recent decades. 

Here is a Bureau of Labor Statistics analysis of U.S. prices for about the same time period.  It shows that U.S. mobile communications prices have dropped almost identically with the OECD data for communications services. 

That matters since mobile phones are the clear consumer choice for using voice services . 

source: BLS 

One can see the same general downward price trend for U.S. internet access, normalizing for higher consumption over time. 

source: Strategy Analytics 

Some will argue that is the wrong way to look at consumer internet access prices. Some will point to non-inflation-adjusted retail prices, or note that posted U.S. retail prices are high by global standards. Without adjusting for different costs of living in different countries, one can conclude U.S. Internet prices are too high. 

Adjusting for local prices, as when comparing the cost of an Internet access subscription to national income statistics, yields a different answer, namely that prices are quite low in developed countries. 

Granted, there always are challenges. Rural areas are harder to serve than urban areas. Poorer countries have a harder time supplying access at low prices than richer countries. 

A normalization technique used by the International Telecommunications Union is to attempt to compare prices to gross national income per person, or to adjust posted retail prices using a purchasing power parity method. 

There are methodological issues. Gross national income is not household income, and per-capita measures might not always be the best way to compare prices, income or other metrics. But at a high level, measuring prices as a percentage of income provides some relative measure of affordability. 

Looking at internet access prices using the PPP method, developed nation prices are around $35 to $40 a month. In absolute terms, developed nation prices are less than $30 a month. 

source: ITU

That is worth keeping in mind.

According to BroadbandNow, less than half of U.S. households have access to fixed network internet access at prices of $60 or less per month. The implication is that this is a problem. 

Maybe it is not generally a problem. The average global price of a fixed network internet access connection is $73 a month. So average U.S. prices are significantly lower than the global average.

AI in the Telecom Business

Very few connectivity service provider executives pay much actual attention to advanced technologies. They get paid to run the business in the here-and-now.

Artificial intelligence or machine learning have value for customer service platforms or network management, but are purchased and supported as features of infrastructure purchases, not discrete products, so concrete AI applications occur within the context of other business and network functions. 

But artificial intelligence is expected to gain usage in the 5G era and be commonplace in network design and operations by the time of 6G. Applied AI will be common in communications networks, in large part because edge computing will be required, and AI is viewed as necessary for wringing value out of huge raw data sets.

Some areas where AI likely will be incorporated include:

• Network operations monitoring and management

• Predictive maintenance

• Fraud mitigation

• Cybersecurity

• Customer service and marketing virtual digital assistants

• Intelligent CRM systems

• CEM

• Base station profitability

• Preventive maintenance

• Battery Capex optimization

• Trouble price ticket prioritization

Reducing the need to transport these data sets to a central location requires intelligent processing at the edge, 5G Americas argues. “AI can be used to extract useful patterns and events out of a sea of raw data.”

Smart farming applications can spot dry patches or insect infestations while a video surveillance system can pinpoint areas with suspicious looking activity. 

Edge computing and processing are required for any number of real-time use cases, especially when huge amounts of raw sensor data are ingested, such as for visual recognition use cases used by autonomous or digital-assisted vehicle safety operations. 

source: Netscribes 

source: 5G Americas 

Beyond that, AI is expected to assist with real-time air interface design and optimization. In other words, the radio systems will use AI to “learn” traffic patterns and adjust the network to correspond. 

In the core of the network, functions and workloads would be dynamically scheduled based on current  connectivity needs, latency requirements and energy consumption targets.