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.

Consumer Connectivity Wallets are the Constraint on Revenue Growth

Some will lament the fact that even when gigabit (or any other very-fast) internet access service is available, most consumers do not seem to buy them, when there also are choices of other services that cost less, but are not as fast. In other words, there typically is some gap between the availability of a service and consumer willingness to buy.

In the United Kingdom, for example, 94 percent of U.K. homes and businesses are in areas where fixed network broadband operating at 30 Mbps or faster is available, according to Ofcom. In such areas, just 45 percent of homes buy a service operating at 30 Mbps or faster.

According to Ofcom, perhaps six percent of U.K., homes can buy a service operating at speeds in three or four digits (300 Mbps to 1,000 Mbps, for example). If take rates resemble those of the United States, single digits are the take rates where gigabit services are available.

And such data underscores an “iron law” of the consumer connectivity business. People are only going to spend so much for connectivity services.

Average monthly U.K. household spend on telecom services fell in 2017, down by one percent in real terms to £87, equivalent to 3.5 percent of total household spend, even as households were upgrading to faster fixed network services with higher recurring prices.

That is a typical spending pattern for consumers in developed markets. Australian consumers spend about 3.5 percent of disposable income on connectivity services, for example. That same pattern can be seen in entertainment spending as well: households will only spend so much on communications or entertainment.  

Wallets are only so big, and get bigger only about as fast as overall income increases.

Average monthly spend on communications services fell by 1.2 percent from £126.18 in 2016 to £124.62 in 2017, an annual decrease of £18.72 in real terms, Ofcom notes.

Average U.K. monthly household spend on mobile voice and data services has decreased by eight percent (£4.02) since 2012, to £45.99 per month in 2017, Ofcom says.

In contrast, average monthly spend on fixed voice and internet services increased by 14.3 percent over the same period to £41.13. This is largely because consumers have migrated to faster broadband services, which tend to be more expensive than standard broadband services.

Mobile voice and data spending fell by 98p (2.1 percent), Ofcom says.  

source: Ofcom

The number of landlines fell by one percent to 33.1 million as a result of businesses switching to mobile and VoIP-based voice services. The fall in business lines was partly offset by a one-percent increase in the number of residential landlines, attributed to growing fixed broadband take-up, as most households in the United KIngdom need to buy a landline service to use fixed broadband services.

The main casualty of growing smartphone take-up has been traditional messaging (i.e. SMS and MMS), as users switch to more feature-rich internet-based messaging services, such as WhatsApp and Facebook Messenger, and the messaging services offered on other social networking sites.

By 2017, average outgoing messages (including SMS and MMS) per mobile phone subscription had fallen to 82 per month, having peaked in 2012 at 162 per month. And while average outgoing mobile call volumes per subscription have risen since 2007, reaching 157 minutes per month in 2017, this was two minutes per month less than in 2016, Ofcom says.

Global Service Provider Revenue to Grow, Free Cash Flow to Shrink

There is modestly good news for global connectivity service providers on the revenue front, through 2022: revenue growth rates are going to tick up modestly. The bad news is that free cash flow is going to dip about two percent on higher capex and opex.

And while mobile internet access can be counted on to boost revenue in developing markets, developed markets will have to rely on revenues from new 5G use cases. So there is more risk in the latter; less risk in the former.

source: A.D. Little

According to a forecast by Arthur D. Little consultants, the North American, Latin American and Middle East and Africa regions will experience service provider revenue growth above global averages.

Global service provider revenue growth will average three percent per year from 2017 to 2022, a slight uptick from growth rates between 2014 and 2016.  

“In North America, we expect total revenue CAGR of 4.6 percent from 2017 to 2022,” A.D. Little says. Latin America will see 5.2 percent CAGR, while MEA will a 4.9 percent CAGR.

The revenue growth can be attributed primarily to increased high-speed wireless data coverage in emerging markets, higher revenue from fixed infrastructure required to support aggressive increases in data consumption and new use cases enabled by 5G deployments in more mature markets.

“New revenues from 5G are expected to arise from new B2B and B2B2X use cases, as significant incremental revenues are not immediately expected from consumer 5G services,” the consultants say.  

source: A.D. Little

The baseline forecast predicts global telecoms capex growing at a CAGR of seven percent from 2017 to 2022, a rate that is more than double the historical CAGR and also outpaces the forecast growth in global telecoms revenue for the same period.

As a result, free cash flow will dip about 1.7 percent

Vodafone Upgrades 1 Million Berlin Homes to Gigabit in 3 Months

Vodafone has upgraded--in just three months--one million Berlin households to gigabit internet access speeds. Already having upgraded six million German household passings, Vodafone expects to reach 11 million gigabit passings by the end of 2019 and more than 12 million households passed by 2021.

That the gigabit upgrade is happening so fast is testament to the use of hybrid fiber coax cable TV facilities, among whose advantages has been lower cost and faster upgrades than switching from copper telephone networks to fiber-to-home.

Liberty Global has estimated the cost to upgrade to gigabit speeds at about €20 ($22) per passing.

source: Vodafone

Ofcom Wants Customers to Get Best Price

Unless they work at it, most consumers are probably unaware whether they are getting the best deal on their mobile or fixed network services, when not under contract for those services.

That can result in an anomaly: the more-loyal customers pay more for the same services than new customers just acquired on promotions.

Ofcom notes that out-of-contract prices vary based on the type of products purchased.

Mobile phones and subscriber identity modules, as well as fixed network voice prices, can range from six percent (fixed network voice) to 27 percent lower when contracts have expired.

But costs for out-of-contract dual-play or triple-play services on fixed networks can range between 19 percent and 26 percent higher, Ofcom notes.

source: Ofcom

That is the backdrop for possible Ofcom action requiring that service providers notify customers when their contracts have ended, as well as notifying such customers of the best prices available for the types of service they already are buying.

In some other markets, the potential for overpayment has been largest in instances where mobile handset sales are bundled with service contracts, when those service contracts continue even after the handsets have been paid off.

That is less a problem in markets where most handset sales are separate from service charges, of course, and where device installment plans are separate from service plan contracts.

There also is more transparency, and greater freedom to choose, when no service contracts are a typical retail billing practice.

The Ofcom proceeding is just one more example of why average revenue per user or account keeps dropping in the connectivity business. In addition to government-mandated actions that reduce consumer prices,  competition and new technology, plus changes in end user demand, combine to push prices lower over time.