Consider the automobile manufacturer BMW, which in the 1970s used the slogan The Ultimate Driving ... [+] getty images
There are many reasons a customer might come back to a business again and again that have nothing to do with loyalty. A repeat customer can come about because of a convenient location, a lower price, a bigger selection and more. But those don’t create loyalty. It just looks as if the customer is loyal.
Actually, you could say that they are loyal—but not to the company. They are loyal to the price, convenient location, etc. The customer who comes back again and again for those types of reasons can deceive you. Not on purpose. It’s their behavior that imitates loyalty. Consider a retail store with repeat customers (not loyal customers), and ask this: If a competitor moves into the neighborhood, has a more convenient location and advertises lower prices, would the customer switch?
According to Dobrev, “Emotional connection creates preference over the competition. Customers don’t just come back out of convenience. They see a difference between doing business with your company and other companies.” His research has found that the amount of business a company gets is dependent on its relationships with customers.
The relationship you want with customers is rooted in emotion. A good experience creates a positive memory. Dobrev is a fan of Professor Daniel Kahneman, who says that people don’t choose between experiences. They choose between the memories of their experiences.
Often, memory is based on interactions customers have had with a salesperson, customer support or a process that a company has. Ideally, it’s a good memory. And when the customer comes back a second time and third time and has similar experiences, the memories of those interactions become an owned experience.
The customer expects it. They know it’s going to happen, just like last time. That’s where the relationship starts to solidify, with a consistent and predictable experience. It goes to an even higher level when the customer feels valued and appreciated. Ultimately, the brand becomes more important than just a place to stop and do business.
Dobrev surveyed more than 19,000 customers in the U.S. and UK and determined that emotional attachment was the biggest driver of value, being responsible for about 43% of business value. Compare that to a company that promotes product features, which came in second at 20%. “Customers don’t know what they really want,” says Dobrev. “They say they want a product, but what really drives business value is emotional attachment.”
Emotions can start to develop even before the customer chooses to do business with a company or brand. Emotions can be found in a marketing strategy. Consider the automobile manufacturer BMW, which in the 1970s used the slogan The Ultimate Driving Machine—a description of the car—until it switched its focus to the emotion of owning and experiencing the car with the slogan BMW is Joy.
While BMW still includes The Ultimate Driving Machine in its descriptors, today’s slogan is Sheer Driving Pleasure. Joachim Blickhäuser, head of corporate and brand identity at the BMW Group, says, “The ‘Sheer Driving Pleasure’ slogan delivers positive emotions and does exactly what a claim should.”
While an emotional connection may help create customer loyalty, you can’t ignore other competitive features. While loyalty makes price less relevant, there is a breaking point. Being easy to do business is also a big factor, so eliminate the friction that will potentially cause customers to run to your competition.
So, here is your assignment. Ask your customers, “Why do you do business with us?” Their reasons will help you define the differences between features and benefits compared to feelings and emotions. Once you have your features and benefits in place, work on creating emotional connections, and your customers will come back for the right reasons—because they love doing business with you.
Customer loyalty is a customer’s likelihood of doing repeat business with you. This stems from customer satisfaction and outweighs availability, pricing, and other factors that typically impact buying decisions. When a customer is loyal to a product, service, or brand, they are willing to wait for a restock or spend a little extra money for it.
“Customer loyalty means the difference between a one-time sale and a customer who comes back to you potentially for the rest of their lives,” said Tyler Read, CEO of personal training company PTPioneer. “If you put in the work necessary to build customer loyalty, those customers will … stay invested in your business. When your business is struggling, it’s the loyal customers who will help you stay afloat.”
This is especially important and evident amidst the global COVID-19 pandemic.
“I think the pandemic was a test of customer loyalty in that it forced consumers to honestly evaluate what service providers they trusted,” said Bill Zinke, senior vice president of marketing at BELFOR Franchise Group. “So, one of the key lessons from the pandemic has been [that], in good times, building customer loyalty can help you grow faster and more profitably, and in tough or challenging times, it can be the difference between surviving and going out of business.”
Customer loyalty is important for many reasons. These are the major ones:
Repeat customers typically spend more than new customers. Because they already trust your business and its products or services, existing customers tend to spend more money than new customers. In fact, the amount they spend typically increases with the duration of doing business with your brand.
Loyal customers yield higher conversion rates. Existing customers have an average conversion rate of around 60% to 70%, while new customers have a conversion rate of 5% to 20%. In other words, you get more value from loyal customers visiting your site.
Customer loyalty boosts profits. The more customer loyalty you have, the better your profits will be. In fact, just a 5% increase of customer retention could increase business increase business profits by 25% to 95%.
Customer retainment is cheaper than customer recruitment. While recruiting new customers is important, it can be expensive – around five times more expensive than retaining a loyal one, actually. Simply retaining loyal customers is much more cost-effective, as they bring higher profits at a lower cost.
Loyal customers shop regularly. Because they’ve already had positive experiences with your brand, repeat customers tend to shop much more frequently than new customers. This is especially true around the holidays, when consumers are purchasing gifts and spending more than they typically would during the rest of the year.
Customer loyalty helps you plan ahead. When you have loyal customers, you can make better anticipatory decisions and effectively plan your finances and marketing efforts….to be continued
The story of the modern web is often told through the stories of Google, Facebook, Amazon. But eBay was the first conqueror. One weekend in September 1995, a software engineer made a website. It wasn’t his first. At 28, Pierre Omidyar had followed the standard accelerated trajectory of Silicon Valley: he had learned to code in seventh grade, and was on track to becoming a millionaire before the age of 30, after having his startup bought by Microsoft. Now he worked for a company that made software for handheld computers, which were widely expected to be the next big thing.
But in his spare time, he liked to tinker with side projects on the internet. The idea for this particular project would be simple: a website where people could buy and sell. Buying and selling was still a relatively new idea online. In May 1995, Bill Gates had circulated a memo at Microsoft announcing that the internet was the company’s top priority. In July, a former investment banker named Jeff Bezos launched an online storefront called Amazon.com, which claimed to be “Earth’s biggest bookstore”. The following month, Netscape, creator of the most popular web browser, held its initial public offering (IPO).
By the end of the first day of trading, the company was worth almost $3bn – despite being unprofitable. Wall Street was paying attention. The dot-com bubble was starting to inflate. If the internet of 1995 inspired dreams of a lucrative future, the reality ran far behind. The internet may have been attracting millions of newcomers – there were nearly 45 million users in 1995, up 76% from the year before – but it wasn’t particularly user-friendly. Finding content was tricky: you could wander from one site to another by following the tissue of hyperlinks that connected them, or page through the handmade directory produced by Yahoo!, the preferred web portal before the rise of the modern search engine.
And there wasn’t much content to find: only 23,500 websites existed in 1995, compared to more than 17m five years later. Most of the sites that did exist were hideous and barely usable. But the smallness and slowness of the early web also lent it a certain charm. People were excited to be there, despite there being relatively little for them to do. They made homepages simply to say hello, to post pictures of their pets, to share their enthusiasm for Star Trek. They wanted to connect. Omidyar was fond of this form of online life. He had been a devoted user of the internet since his undergraduate days, and a participant in its various communities. He now observed the rising flood of dot-com money with some concern.
The corporations clambering on to the internet saw people as nothing more than “wallets and eyeballs”, he later told a journalist. Their efforts at commercialisation weren’t just crude and uncool, they also promoted a zombie-like passivity – look here, click here, enter your credit card number here – that threatened the participatory nature of the internet he knew. “I wanted to do something different,” Omidyar later recalled, “to give the individual the power to be a producer as well as a consumer.” This was the motivation for the website he built in September 1995. He called it AuctionWeb. Anyone could put up something for sale, anyone could place a bid, and the item went to the highest bidder. It would be a perfect market, just like you might find in an economics textbook.
Through the miracle of competition, supply and demand would meet to discover the true price of a commodity. One precondition of perfect markets is that everyone has access to the same information, and this is exactly what AuctionWeb promised. Everything was there for all to see. The site grew quickly. By its second week, the items listed for sale included a Yamaha motorcycle, a Superman lunchbox and an autographed Michael Jackson poster. By February 1996, traffic had grown brisk enough that Omidyar’s web hosting company increased his monthly fee, which led him to start taking a cut of the transactions to cover his expenses. Almost immediately, he was turning a profit. The side project had become a business.
But the perfect market turned out to be less than perfect. Disputes broke out between buyers and sellers, and Omidyar was frequently called upon to adjudicate. He didn’t want to have to play referee, so he came up with a way to help users work it out themselves: a forum. People would leave feedback on one another, creating a kind of scoring system. “Give praise where it is due,” he said in a letter posted to the site, “make complaints where appropriate.” The dishonest would be driven out, and the honest would be rewarded – but only if users did their part. “This grand hope depends on your active participation,” he wrote.
The value of AuctionWeb would rely on the contributions of its users. The more they contributed, the more useful the site would be. The market would be a community, a place made by its members. They would become both consumers and producers, as Omidyar hoped, and among the things they produced would be the content that filled the site. By the summer of 1996, AuctionWeb was generating $10,000 a month. Omidyar decided to quit his day job and devote himself to it full-time. He had started out as a critic of the e-commerce craze and had ended up with a successful e-commerce company. In 1997, he renamed it eBay. Ebay was one of the first big internet companies. It became profitable early, grew into a giant of the dot-com era, survived the implosion of the dot-com bubble, and still ranks among the largest e-commerce firms in the world.
But what makes eBay particularly interesting is how, in its earliest incarnation, it anticipated many of the key features that would later define the phenomenon commonly known as the “platform”. Ebay wasn’t just a place where collectors waged late-night bidding wars over rare Beanie Babies. In retrospect, it also turned out to be a critical hinge in the history of the internet. Omidyar’s site pioneered the basic elements that would later enable Google, Facebook and the other tech giants to unlock the profit potential of the internet by “platformising” it.
None of the metaphors we use to think about the internet are perfect, but “platform” is among the worst. The term originally had a specific technical meaning: it meant something that developers build applications on top of, such as an operating system. But the word has since come to refer to various kinds of software that run online, particularly those deployed by the largest tech firms. The scholar Tarleton Gillespie has argued that this shift in the use of the word “platform” is strategic. By calling their services “platforms”, companies such as Google can project an aura of openness and neutrality. They can present themselves as playing a supporting role, merely facilitating the interactions of others.
Their control over the spaces of our digital life, and their active role in ordering such spaces, is obscured. “Platform” isn’t just imprecise. It’s designed to mystify rather than clarify. A more useful metaphor for understanding the internet, one that has guided its architects from the beginning, is the stack. A stack is a set of layers piled on top of one another. Think of a house: you have the basement, the first floor, the second floor and so on, all the way up to the roof. The things that you do further up in a house often depend on systems located further down. If you take a shower, a water heater in the basement warms up the cold water being piped into your house and then pipes it up to your bathroom.
The internet also has a basement, and its basement also consists largely of pipes. These pipes carry data, and everything you do further up the stack depends on these pipes working properly. Towards the top of the stack is where the sites and apps live. This is where we experience the internet, through the pixels of our screens, in emails or tweets or streams. The best way to understand what happens on these sites and apps – on what tech companies call “platforms” – is to understand them as part of the broader story of the internet’s privatisation.
The internet started out in the 1970s as an experimental technology created by US military researchers. In the 80s, it grew into a government-owned computer network used primarily by academics. Then, in the 90s, privatisation began. The privatisation of the internet was a process, not an event. It did not involve a simple transfer of ownership from the public sector to the private, but rather a more complex movement whereby corporations programmed the profit motive into every level of the network. A system built by scientists for research was renovated for the purpose of profit maximisation. This took hardware, software, legislation, entrepreneurship. It took decades. And it touched all of the internet’s many pieces.
The process of privatization started with the pipes, and then worked its way up the stack. In April 1995, only five months before Omidyar made the website that would become eBay, the government allowed the private sector to take over control of the network’s plumbing. Households and businesses were eager to get online, and telecoms companies made money by helping them access the internet. But getting people online was a small fraction of the system’s total profit potential. What really got investors’ capital flowing was the possibility of making money from what people did online. In other words, the next step was figuring out how to maximize profit in the upper floors, where people actually use the internet. The real money lay not in monetizing access, but in monetizing activity.
This is what Omidyar did so effectively when he created a place where people wanted to buy and sell goods online, and took a cut of their transactions. The dot-com boom began with Netscape’s explosive IPO in August 1995. Over the following years, tens of thousands of startups were founded and hundreds of billions of dollars were invested in them. Venture capital entered a manic state: the total amount of US venture-capital investment increased more than 1,200% from 1995 to 2000. Hundreds of dot-com companies went public and promptly soared in value: at their peak, technology stocks were worth more than $5tn.
When eBay went public in 1998, it was valued at more than $2bn on the first day of trading; the continued ascent of its stock price over the next year made Omidyar a billionaire. Yet most of the startups that attracted huge investment during these years didn’t actually make money. For all the hype, profits largely failed to materialize, and in 2000 the bubble burst. From March to September, the 280 stocks in the Bloomberg US Internet Index lost almost $1.7tn. “It’s rare to see an industry evaporate as quickly and completely,” a CNN journalist remarked. The following year brought more bad news. The dot-com era was dead.
Today, the era is typically remembered as an episode of collective insanity – as an exercise in what Alan Greenspan, during his contemporaneous tenure as chair of the Federal Reserve, famously called “irrational exuberance”. Pets.com, a startup that sold pet supplies online, became the best-known symbol of the period’s stupidity, and a touchstone for retrospectives ever since. Never profitable, the company spent heavily on advertising, including a Super Bowl spot; it raised $82.5m in its IPO in February 2000 and imploded nine months later.
Arrogance, greed, magical thinking and bad business decisions all contributed to the failure of the dot-com experiment. Yet none of these were decisive. The real problem was structural. While their investors and executives probably wouldn’t have understood it in these terms, dot-com companies were trying to advance the next stage of the internet’s privatisation – namely, by pushing the privatization of the internet up the stack. But the computational systems that could make such a push feasible were not yet in place. Companies still struggled to turn a profit from user activity.
In his analysis of capitalist development, Karl Marx drew a distinction between the “formal” and “real” subsumption of labour by capital. In formal subsumption, an existing labour process remains intact, but is now performed on a capitalist basis. A peasant who used to grow his own food becomes a wage labourer on somebody else’s farm. The way he works the land stays the same. In real subsumption, by contrast, the labour process is revolutionised to meet the requirements of capital. Formerly, capital inherited a process; now, it remakes the process. Our agricultural worker becomes integrated into the industrialised apparatus of the modern factory farm.
The way he works completely changes: his daily rhythms bear little resemblance to those of his peasant predecessors. And the new arrangement is more profitable for the farm’s owner, having been explicitly organised with that end in mind. This is a useful lens for thinking about the evolution of the internet, and for understanding why the dot-coms didn’t succeed. The internet of the mid-to-late 1990s was under private ownership, but it had not yet been optimised for profit. It retained too much of its old shape as a system designed for researchers, and this shape wasn’t conducive to the new demands being placed on it. Formal subsumption had been achieved, in other words, but real subsumption remained elusive.
Accomplishing the latter would involve technical, social and economic developments that made it possible to construct new kinds of systems. These systems are the digital equivalents of the modern factory farm. They represent the long-sought solution to the problem that consumed and ultimately defeated the dot-com entrepreneurs: how to push privatisation up the stack. And eBay offered the first glimpse of what that solution looked like.Ebay enlisted its users in its own creation. They were the ones posting items for sale and placing bids and writing feedback on one another in the forum. Without their contributions, the site would cease to exist.
Omidyar was tapping into a tradition by setting up eBay in this way. In 1971, a programmer named Ray Tomlinson invented email. This was before the internet existed: Tomlinson was using its precursor, Arpanet, a cutting-edge network that the Pentagon created to link computers across the country. Email became wildly popular on Arpanet: just two years after its invention, a study found that it made up three-quarters of all network traffic. As the internet grew through the 1980s, email found an even wider reach. The ability to exchange messages instantaneously with someone far away was immensely appealing; it made new kinds of collaboration and conversation possible, particularly through the mailing lists that formed the first online communities.
Email was more than just a useful tool. It helped humanize the internet, making a cold assemblage of cables and computers feel inhabited. The internet was somewhere you could catch up with friends and get into acrimonious arguments with strangers. It was somewhere to talk about politics or science fiction or the best way to implement a protocol. Other people were the main attraction. Even the world wide web was made with community in mind. “I designed it for a social effect – to help people work together,” its creator, Tim Berners-Lee, would later write.
Community is what Omidyar liked best about the internet, and what he feared the dot-com gold rush would kill. He wasn’t alone in this: one could find dissidents railing against the forces of commercialisation on radical mailing lists. But Omidyar was no anti-capitalist. He was a libertarian: he believed in the liberating power of the market. He didn’t oppose commercialisation as such, just the particular form it was taking. The companies opening cheesy digital storefronts and plastering the web with banner ads were doing commercialisation poorly. They were treating their users as customers. They didn’t understand that the internet was a social medium.
Ebay, by contrast, would be firmly rooted in this fact. From its first days as AuctionWeb, the site described itself as a community, and this self-definition became integral to its identity and to its operation. For Omidyar, the point wasn’t to defend the community from the market but rather to recast the community as a market – to fuse the two. No less a figure than Bill Gates saw the future of the internet in precisely these terms. In 1995, the same year that Omidyar launched AuctionWeb, Gates co-authored a book called The Road Ahead. In it, the Microsoft CEO laid out his vision for the internet as “the ultimate market”: “It will be where we social animals will sell, trade, invest, haggle, pick stuff up, argue, meet new people, and hang out.
Think of the hustle and bustle of the New York Stock Exchange or a farmers’ market or of a bookstore full of people looking for fascinating stories and information. All manner of human activity takes place, from billion-dollar deals to flirtations.” Here, social relationships have merged so completely with market relationships as to become indistinguishable. The internet is the instrument of this union; it brings people together, but under the sign of capital. Gates believed his dream was at least a decade from being realised. Yet by the time his book came out, AuctionWeb was already making progress toward achieving it.
Combining the community with the market was a lucrative innovation. The interactions that occurred in the guise of the former greatly enhanced the financial value of the latter. Under the banner of community, AuctionWeb’s buyers and sellers were encouraged to perform unpaid activities that made the site more useful, such as rating one another in the feedback forum or sharing advice on shipping. And the more people participated, the more attractive a destination it became. More people using AuctionWeb meant more items listed for sale, more buyers bidding in auctions, more feedback posted to the forum – in short, a more valuable site.
This phenomenon – the more users something has, the more valuable it becomes – is what economists call network effects. On the web, accommodating growth was fairly easy: increasing one’s hosting capacity was a simpler and cheaper proposition than the brick-and-mortar equivalent. And doing so was well worth it because, at a certain size, network effects locked in advantages that were hard for a competitor to overcome. A second, related strength was the site’s role as a middleman. In an era when many dot-coms were selling goods directly – Pets.com paid a fortune on postage to ship pet food to people’s doors – Omidyar’s company connected buyers and sellers instead, and pushed the cost of postage on to them.
This enabled it to profit from users’ transactions while remaining extremely lean. It had no inventory, no warehouses – just a website. But AuctionWeb was not only a middleman. It was also a legislator and an architect, writing the rules for how people could interact and designing the spaces where they did so. This wasn’t in Omidyar’s plan. He initially wanted a market run by its members, an ideal formed by his libertarian beliefs. His creation of the feedback forum likely reflected an ideological investment in the idea that markets were essentially self-organising, as much as his personal interest in no longer having to mediate various disputes.
Contrary to libertarian assumptions, however, the market couldn’t function without the site’s ability to exercise a certain kind of sovereignty. The feedback forum is a good example: users started manipulating it, leaving praise for their friends and sending mobs of malicious reviewers after their enemies. The company would be compelled to intervene again and again. It did so not only to manage the market but also to expand it by attracting more buyers and sellers through new categories of goods and by expanding into new countries – an imperative that shareholders imposed after eBay went public in 1998.
“Despite its initial reluctance, the company stepped increasingly into a governance role,” writes the sociologist Keyvan Kashkooli, in his study of eBay’s evolution. Increasing profitability required managing people’s behaviour, whether through the code that steered them through the site or the user agreements that governed their activities on it. Thanks to network effects, and its status as both middleman and sovereign, eBay easily turned a profit. When the crash of 2000–01 hit, it survived with few bruises. And in the aftermath of the crash, as an embattled industry, under pressure from investors, tried to reinvent itself, the ideas that it came up with had much in common with those that had formed the basis for eBay’s early success.
For the most part, eBay’s influence was neither conscious nor direct. But the affinities were unmistakable. Omidyar’s community market of the mid-1990s was a window into the future. By later standards it was fairly primitive, existing as it did within the confines of an internet not yet remodelled for the purpose of profit maximisation. But the systems that would accomplish that remodelling, that more total privatisation of the internet, would do so by elaborating the basic patterns that Omidyar had applied. These systems would be called platforms, but what they resembled most were shopping malls.
The first modern shopping mall was built in Edina, Minnesota, in 1956. Its architect, Victor Gruen, was a Jewish socialist from Vienna who had fled the Nazis and disliked American car culture. He wanted to lure midcentury suburbanites out of their Fords and into a place that recalled the “rich public social life” of a great European city. He hoped to offer them not only shops but libraries and cinemas and community centres. Above all, his mall would be a space for interaction: an “outlet for that primary human instinct to mingle with other humans”. Unlike in a city, however, this mingling would take place within a controlled setting. The chaos of urban life would be displaced by the discipline of rational design.
As Gruen’s invention caught on, the grander parts of his vision fell away. But the idea of an engineered environment that paired commerce with a public square remained. Gruen’s legacy would be a kind of capitalist terrarium, nicely captured by what urban planners call a “privately owned public space”. The systems that dominate life at the upper end of the stack are best understood, to borrow an insight from the scholar Jathan Sadowski, as shopping malls. The shopping malls of the internet – Google, Facebook, Amazon – are nothing if not privately owned public spaces. Calling themselves platforms, they are in fact corporate enclosures, with a wide range of interactions transpiring inside of them.
Just like in a real mall, some of these interactions are commercial, such as buying clothes from a merchant, while others are social, such as hanging out with friends. But what distinguishes the online mall from the real mall is that within the former, everything one does makes data. Your clicks, chats, posts, searches – every move, however small, leaves a digital trace. And these traces present an opportunity to create a completely new set of arrangements. Real malls are in the rental business: the owner charges tenants rent, essentially taking a slice of their revenues. Online malls can make money more or less the same way, as eBay demonstrated early on, by taking a cut of the transactions they facilitate.
But, as Sadowski points out, online malls are also able to capture another kind of rent: data rent. They can collect and make money from those digital traces generated by the activities that occur within them. And since they control every square inch of the enclosure, and because modifying the enclosure is simply a matter of deploying new code, they can introduce architectural changes in order to cause those activities to generate more traces, or traces of different kinds. These traces turn out to be very valuable. So valuable, in fact, that amassing and analysing them have become the primary functions of the online mall. Like Omidyar’s community market, the online mall facilitates interactions, writes the rules for those interactions, and benefits from having more people interacting with one another.
But in the online mall, these interactions are recorded, interpreted and converted into money in a range of ways. Data can help sell targeted advertising. It can help build algorithmic management systems that siphon more profit out of each worker. It can help train machine learning models in order to develop and refine automated services like chatbots, which can in turn reduce labour costs and open new revenue streams. Data can also sustain faith among investors that a tech company is worth a ton of money, simply because it has a ton of data. This is what distinguishes online malls from their precursors: they are above all designed for making, and making use of, data. Data is their organizing principle and essential ingredient.
Data is sometimes compared to oil, but a better analogy might be coal. Coal was the fuel that powered the steam engine. It propelled the capitalist reorganization of manufacturing from an artisanal to an industrial basis, from the workshop to the factory, in the 19th century. Data has played a comparable role. It has propelled the capitalist reorganization of the internet, banishing the remnants of the research network and perfecting the profit engine. Very little of this vastly complex machinery could be foreseen from the vantage point of 1995.
But the arrival of AuctionWeb represented a large step toward making it possible. The story of the modern internet is often told through the stories of Google, Facebook, Amazon and the other giants that have come to conquer our online life. But their conquests were preceded and prefigured by another, one that started as a side project and stumbled into success by coming up with the basic blueprint for making a lot of money on the internet.
Digital empathy is “traditional empathic characteristics such as concern and caring for others expressed through computer-mediated interactions.” A recent study suggests that clients felt their psychotherapist was more empathic and supportive in a remote setting than an in-person setting.
Another study found that virtual group therapy can be as effective as in-person group therapy. Since the COVID-19 pandemic, much of psychotherapy has moved online. Two new studies take a look at whether teletherapy and video conference therapy are helpful. Can empathy connect clients with their therapists despite the virtual divide? Has psychotherapy adapted to moving online? The results may be surprising for some.
In one study, published in Frontiers in Psychology, researchers found that clients felt like their psychotherapist was significantly more empathic and supportive in the remote setting compared to in person. This is important because, depending on the type of psychotherapy, whether a client feels connected to the psychotherapist can be an essential factor in a positive outcome in treatment.
“Digital empathy” has been defined as “traditional empathic characteristics such as concern and caring for others expressed through computer-mediated communications.” Further models of digital empathy have expanded the characteristics of “digital empathy”:
Ability to analyze and evaluate another’s internal state (empathy accuracy)
Recognize, understand and predict other’s thoughts and emotions (cognitive empathy)
Feel what others feel (affective empathy)
Role play (imaginative empathy)
Be compassionate to others (empathic concern) via digital media
The study examines online therapy sessions that took place via Skype and WhatsApp video calls. About half the clients used desktops or laptop computers, with the other half using a mix of tablets or smartphones. Almost 90% of the therapists used a computer.
The research found that therapists felt like they could offer the same amount of empathy whether in person or virtually. Surprisingly, patients felt more empathetically connected to and supported by their therapist in the virtual setting, compared to in person. These findings build upon prior therapy research conducted before the pandemic, which found that empathy can indeed reach across virtual borders and be effective in virtual psychotherapy.
Another study from 2021 confirms that group psychotherapy can be done effectively virtually. In fact, some clients found remote group work even more helpful than in person, but that this is not the case for everyone.
These studies do raise the point that personal preference and self-selection may have a lot to do with how comfortable people are with virtual psychotherapy and teletherapy and positive treatment outcomes. Clients who respond well in virtual settings are likely those already at ease with video conferencing technology and are able to feel comfortable and have privacy at home.
The same goes for the therapist. Research has found that therapists who feel most comfortable and effective in offering virtual psychotherapy typically had offered it previously, even before the pandemic.
Psychotherapy has transitioned online effectively for many people, in spite of the limitations of technological issues, sound delays, and the difficulty with perceiving micro-expressions. Clients should feel empowered to assess whether virtual therapy is a good fit for their needs.
It is likely that many clients and therapists will continue to choose to stay online, given the positive results and ability for digital empathy to exist alongside the convenience of scheduling, less commute time, and being able to communicate safely without masks. The good news is that virtual psychotherapy can be offered in a way that clients feel is supportive and effective and will likely remain a mainstay platform for the delivery of psychotherapy.
Marlynn Wei, M.D., J.D., is a board-certified Harvard and Yale-trained psychiatrist and therapist in New York City.
Beijing is building a system to ensure that the automated processes of Internet platforms are fair, transparent and in line with the ideology of the Communist Party
Regulators called for the algorithms to be fair and transparent, following the ideology of the Communist Party of China.
The campaign puts China one step ahead in policing tech forums, as governments around the world grapple with how to respond to automated technologies that reshape business, social interactions and politics.
Earlier this year, the European Union proposed restricting certain uses of artificial intelligence to reduce potential harm. In the US, lawmakers are investigating Facebook’s influence Inc. NS
Algorithm-driven content on users, after Businesshala reported that the company’s Instagram app has a negative impact on children’s mental health.
China has targeted algorithms more aggressively under the close watch of its domestic tech sector. Draft guidelines released this summer would require algorithms to protect the rights of workers and consumers, and restrict the use of algorithms to manipulate user accounts, online traffic or search results.
“We don’t necessarily see China as a regulatory innovator, but in this case they are,” said Rogier Creamers, an assistant professor at Leiden University in the Netherlands, which focuses on Chinese technical policy.
Under a three-year plan released last week, Chinese regulators outlined steps to monitor algorithms, including a registration process and the establishment of a technical team to evaluate the mechanisms and risks of an algorithm.
The latest campaign builds on a broad regulatory push in China’s tech sector that has prompted investigations into some of the country’s biggest companies, including e-commerce giant Alibaba Group Holding. Ltd.
The push is partly directed at business practices that regulators deem harmful so workers or consumers.
Companies such as Meituan and Didi have faced heat over the working conditions of drivers, as well as calls for creating algorithms that schedule workers’ tasks and pay more transparently. Officials have also warned tech companies this year against exploiting personal data and using algorithms to charge discriminatory prices from customers.
China’s Cyberspace Administration, Alibaba and Didi did not respond to requests for comment. China is currently celebrating its National Day holiday.
Meituan declined to comment. The company previously published an explanation of its delivery algorithm and said it is making changes to give delivery drivers more flexibility.
Experts said it would be a challenge for regulators to tighten controls on algorithms without hindering development or innovation in one of China’s most successful sectors. Internet companies rely on complex mathematical instructions for tasks ranging from analysis of social-media behavior to mapping optimal distribution routes.
While algorithms have contributed to technological advancement and societal development, the CAC said in last week’s announcement, they have also brought “challenges to ideological security, a fair and equal society, and the protection of the legal rights of Internet users.”
Beijing-based partner at law firm Bird & Bird, James Gong, said tighter regulatory oversight of algorithms is likely to impact China’s internet industry.
Mr. Gong said of the country’s Internet companies, “Almost all of them use algorithms and automated decision-making and profiling to ensure that their marketing is more accurate and to improve business efficiency and increase profits.” Is.”
A senior manager at ByteDance Ltd said the requirement to register the algorithm would only add a step, restricting the learning of user behavior and recommendation services, as well as requiring disclosure of proprietary technology that could hurt the company’s business. .
ByteDance, which owns social-media sensation TikTok and its Chinese sister app Douyin, is known for its powerful algorithms that drive user recommendations and content.
“The regulatory environment is clear, and we need to start thinking about how to adjust accordingly,” the ByteDance manager said. He said that since most of the new regulation is still under debate, it is difficult to say what the immediate commercial impact will be.
ByteDance did not respond to a request for comment.
Sam Sachs, senior fellow at Yale Law School’s Paul Tsai China Center, said China’s approach could appeal to other countries that want a thriving digital economy while maintaining a firm grip on political and social discourse. However, she said there is still a lot of uncertainty over the details and enforcement of these new rules.
“I think they understand that this is an impossible task that they have set for themselves,” Ms Sachs said. “I would also say that three years can be ambitious.”
The CAC guidelines also state that algorithms used by Chinese companies must uphold core socialist values and promote “positive energy” in content provided to users.
China is taking more control of online content and communities. In recent months, it has severely restricted online-videogame time for players under the age of 18, banned pop-idol rankings and criticized online male personalities for being too sacrilegious. are visible.
“It’s almost taking online censorship up a notch,” Ms Sachs said. “It is saying that you have an obligation to ensure that any content that is algorithmically driven that you feed into the online space is to shape socialist values.”
On Wednesday, the Sunnyvale, California-based networking platform released its fifth annual list of 50 U.S. companies on the rise. The list tracks growth in employee count, interest from people looking for jobs, and how people interact with the online presence of the company and its employees. It also measures the startups’ ability to bring in employees from LinkedIn’s Top Companies list, which includes more established businesses like Amazon and Alphabet.
All startups on the list are less than seven years old, headquartered in the U.S., and have at least 50 employees. LinkedIn used data from July 1, 2020 to June 30, 2021. The ranking features some of the year’s breakout companies, like Clubhouse, and others that flourished in the pandemic, like Discord. Several of them have succeeded through their use of emerging technologies such as artificial intelligence and robotics. Here are six of the most innovative from LinkedIn’s list.
Coming in at No. 2 on LinkedIn’s list, Gong uses artificial intelligence to analyze all of a company’s interactions with customers — calls, meetings, and emails — to improve their sales and marketing. The San Francisco-based company boasts clients including LinkedIn and Pinterest and was a 2021 Inc. 5000 honoree, ranking No. 99 with over $37 million in 2020 revenue.
The Seattle-based sales management platform, an Inc. Best Workplaces company in 2021, uses machine learning to optimize customer communications, from social media to text to email. It ranked No. 9 on LinkedIn’s list and counts customersincluding Zoom and Adobe.
ScaleAI helps clients process data faster via what it calls scaled artificial intelligence. The goal is to manage the swath of data that A.I. can generate, founder Alexandr Wang told Inc. The San Francisco-based company’s products can track visual data for AR companies or autonomous driving and provide complex models and results displays. Ranked No. 29 on LinkedIn’s list, the startup has a $7 billion valuation.
Elon Musk co-founded this startup, and its mission, predictably, is futuristic: It’s developing technology to connect the human brain to devices that can translate thoughts into speech or text, which could have wide applications for people who are paralyzed, for example. Neuralink is based in Fremont, California and ranked No. 33 on LinkedIn’s list. Its eventual goal is to merge mankind with computers, Musk said in 2017.
Nuro sells self-driving cars, but not ones meant to ferry humans around. Nuro cars just deliver goods — and are programmed to avoid loss of life. The Mountain View, California-based startup, which became a unicorn in 2019, now delivers for the likes of Walmart, FedEx, and CVS Pharmacy. Nuro says it is the first self-driving, driverless car to get permission to operate from the National Highway Traffic Safety Administration (NHTSA). The number of cars is still limited, but they are now available in San Jose, California; Houston; Silicon Valley; and Phoenix.
Relativity Space builds rockets. In the future, it hopes to establish a society on Mars. The Long Beach, California-based company produces a 3-D printed, reusable rocket called Terran 1, using robotics and artificial intelligence for its development. Mark Cuban was an early investor, as was Y Combinator. LinkedIn ranked the company No. 45 on its list.