Rethinking the Internet: A New User Experience (Part 3)
When the Internet was saddled with surveillance advertising as its default business model, the consumer stopped being the central focus of the US Consumer Internet. Advertisers became the customers and therefore the focus shifted to creating the best experience for them. In the process, platforms began a race to amass users, to dominate their time, to drive ever higher engagement, and to collect more and more data so that they could predict, and then influence/manipulate, user behavior. In pursuit of this data, the boundaries between self and market have been almost entirely eliminated, the Internet has become pervasive (connected everything), and nearly all friction will soon be removed (voice as the new UI.) This allows these platforms to be ever present in the background, nudging consumers in whatever direction their customers (advertisers) desire.

This business model has resulted in a terrible user experience, one in which the best products don’t win, but rather the ones that the most people use. User-generated data is used to create products that are not designed for the consumer’s own benefit, but rather for the benefit of third parties. With a new business model and improved infrastructure, might it be possible to improve the US Consumer Internet experience and restore the consumer’s central role in it? An improved user experience would restore consumer control over time, attention, and data. The current viability of each each is highlighted below.

How Can Consumers Get Their Time Back?

The time spent online used to be somewhat limited by pay-per-use or pay-by-time business models, but unlimited consumption plans have prevailed, and now there is essentially no limit to how much time we spend online. This is important because our time is not unlimited, our time (and attention) are zero-sum equations, meaning the more time we spend engaged in one activity, the less time we spend doing something else. comScore’s 2017 Cross-Platform Future in Focus report found that the average person spends almost three hours a day on their phone.¹⁴ That’s three hours a day they could be engaging in other activities- potentially more productive activities.

Importantly, consumers feel this paint point acutely. According to a recent Pew Study survey, 39% of US Internet users ages 18–29 reported being online “almost constantly” followed by 36% for ages 30–49.¹ They don’t seem happy about it either, as the percentage of US adults that are trying to limit the time they spend on their phones increased from 47% in 2017 to 63% in 2018.¹ Consumers clearly want to regain control of their time. They don’t need education to understand its value, the way they do with data, and both enterprises and parents are willing to pay to ensure that employees/students use their time in more focused and productive ways.

There are several companies that are working to build “mindful operating systems,” “time well spent” launchers / applications, or smart scheduling apps that allow users to remain focused, to remain present in their intention when engaging with technology, and to reach a flow state while in workflows. Siempo, Flipd, Thrive Away, and Mercury OS all attempt to allow users to focus, eliminating endless notifications, pop-ups, and distractions, and to re-direct the consumer towards their original goal when logging on.

While the consumer clearly feels this pain point and is searching for solutions, many of these apps are more akin to features than businesses. That makes it tough to compete with platforms like Apple which can simply add some of these elements to their own product suites (e.g. Screen Time.) To overcome this challenge, most of these start-ups are attempting to create a sense of community in their products in addition to adding more comprehensive health and wellness content. Still, many of these applications struggle to devise a business model that isn’t subject to the same problems that they are trying to solve.

Business model innovation will be key to the success of these applications since the end goal is actually dis-engagement.

How Can Consumers Re-Focus Their Attention?

Advertisers are the primary customers of US Internet platforms. This dynamic has led these platforms to become obsessed with driving “engagement.” These platforms now design products intended to keep users locked in endless loops and infinite scrolls, encouraging users to “binge” and promoting outrage to keep users engaged, and when users falter, they are barraged with notifications in order to re-direct their attention back to their screens. The average person checks their phone 150 times per day.²

The consumer experience suffers as a result. Luckily, there are solutions and consumers acutely experience this pain point, making them more incentivized to seek them out in the near term. Four approaches to addressing attention on the Internet are highlighted below:

Ad Blockers: Users are tired of annoying, distracting ads. According to e-Marketer ~28% of US Internet users (~80M people) now block ads, up from 21% in 2016.¹⁵ AdBlock Plus and Disconnect have over 100M¹¹ and 50M¹² users, respectively. The problem with ad blockers is that they have historically had a negative impact on publishers, which lose the opportunity to monetize their content, forcing many to enforce paywalls. Consumers are resistant to paywalls, so while the consumer regains control over their attention and enjoys an ad-free web experience, the publishers often suffer. Some companies, such as Scroll, take a different approach and are attempting to launch a paid ad blocker which allows for a fast, ad-free version of sites across a content partner network. Importantly, since these models are paid, their services could allow publishers to earn more than they would via an ad-supported model. However, the economics of this model are still to be determined (consumer willingness to pay versus revenue generation for publishers relative to advertising.)

Users are tired of annoying, distracting ads. According to e-Marketer ~28% of US Internet users (~80M people) now block ads, up from 21% in 2016.¹⁵ AdBlock Plus and Disconnect have over 100M¹¹ and 50M¹² users, respectively. The problem with ad blockers is that they have historically had a negative impact on publishers, which lose the opportunity to monetize their content, forcing many to enforce paywalls. Consumers are resistant to paywalls, so while the consumer regains control over their attention and enjoys an ad-free web experience, the publishers often suffer. Some companies, such as Scroll, take a different approach and are attempting to launch a paid ad blocker which allows for a fast, ad-free version of sites across a content partner network. Importantly, since these models are paid, their services could allow publishers to earn more than they would via an ad-supported model. However, the economics of this model are still to be determined (consumer willingness to pay versus revenue generation for publishers relative to advertising.) Explicit Compensation-Attention Exchange: This model converts the existing online advertising model into an explicit exchange of compensation for attention. Proponents of this model argue that it reduces click fraud, since the viewers of ads have verified identities, increasing the value of each ad. As each ad becomes more effective, advertisers can theoretically reduce ad load, reducing costs while improving the user experience. However, while attention is easier to monetize than data (see below), marketplaces built around both struggle to achieve minimum efficient scale. Advertisers want to reach millions of users, meaning start-ups targeting this space will need to partner with brands that already benefit from large user bases as opposed to trying to on-board users from scratch. Consumer feedback also indicates that many find this model to be dystopic and Ready Player One-esque. Ultimately, if consumers aren’t comfortable confronting the current exchange of attention for compensation, these products won’t succeed. One important difference relative to the current paradigm is that these solutions allow the consumer to control ad load, which is also non-interruptive. Still, these applications will struggle to gain traction if the consumer cannot appreciate this nuance.

This model converts the existing online advertising model into an explicit exchange of compensation for attention. Proponents of this model argue that it reduces click fraud, since the viewers of ads have verified identities, increasing the value of each ad. As each ad becomes more effective, advertisers can theoretically reduce ad load, reducing costs while improving the user experience. However, while attention is easier to monetize than data (see below), marketplaces built around both struggle to achieve minimum efficient scale. Advertisers want to reach millions of users, meaning start-ups targeting this space will need to partner with brands that already benefit from large user bases as opposed to trying to on-board users from scratch. Consumer feedback also indicates that many find this model to be dystopic and Ready Player One-esque. Ultimately, if consumers aren’t comfortable confronting the current exchange of attention for compensation, these products won’t succeed. One important difference relative to the current paradigm is that these solutions allow the consumer to control ad load, which is also non-interruptive. Still, these applications will struggle to gain traction if the consumer cannot appreciate this nuance. Compensation for Content Contributions: In this model, consumers monetize the content they generate and contribute to online platforms and social networks.This allows the benefits of engagement with a platform to accrue (at least partially) back to the consumer. As expressed by Nick Sullivan, CEO and founder of ChangeCoin (acquired by AirBnb), “We’ve lazily accepted ads are the best way to monetize content online.… What we’ve been missing is a way for people to express their appreciation and vote with their dollars for the things that they find good — in a very low-friction way.”⁴ Devising such a business model was challenging before technologies that enabled micropayments. Three social network variants that leverage micropayments to compensate content creators are Steemit (launched in 2016), Voice (in beta), and Coil (in beta.) The key innovation with Coil is that it leverages the interledger protocol (ILP) to pay out a fixed bandwidth / second rate that is sent to the creator instantly. Thus far, these networks have struggled to gain users as they have not been able to create enough value to incentivize users to switch from existing networks. As of the end of 2018, Steemit reported ~500,000 active users (relative to Facebook’s +2B.)

In this model, consumers monetize the content they generate and contribute to online platforms and social networks.This allows the benefits of engagement with a platform to accrue (at least partially) back to the consumer. As expressed by Nick Sullivan, CEO and founder of ChangeCoin (acquired by AirBnb), “We’ve lazily accepted ads are the best way to monetize content online.… What we’ve been missing is a way for people to express their appreciation and vote with their dollars for the things that they find good — in a very low-friction way.”⁴ Devising such a business model was challenging before technologies that enabled micropayments. Three social network variants that leverage micropayments to compensate content creators are Steemit (launched in 2016), Voice (in beta), and Coil (in beta.) The key innovation with Coil is that it leverages the interledger protocol (ILP) to pay out a fixed bandwidth / second rate that is sent to the creator instantly. Thus far, these networks have struggled to gain users as they have not been able to create enough value to incentivize users to switch from existing networks. As of the end of 2018, Steemit reported ~500,000 active users (relative to Facebook’s +2B.) Solutions Designed to Improve the Quality of Time Spent: There are many other companies working on creating a more balanced and trustworthy Internet, including companies that show the counter side to every news story, companies working on proving the provenance of digital media utilizing blockchain technology, companies focused on facilitating constructive online debate (TruStory), and companies focused on detecting political bots and fake news (RoBhat Labs, a Dorm Room fund community company.)

How Do We Value Data?

Amid a growing number of data privacy violations and breaches, data protection, ownership, and monetization have recently become a part of the public discourse. Governments have publicly acknowledged that “data has value, and it belongs to you” and Senator John Kennedy introduced a bill called the “Own Your Data Act.” This sentiment is echoed in the private sector, with a slew of start-ups launching to create data marketplaces in which users own, control, and monetize their data directly.

The truth is that valuing data is tough. It’s nuanced. Not all data is equally valuable and much of its value depends on the context in which it is used, which may be unknowable. It’s complex. Much data is interpersonal, making ownership complicated since there are multiple parties involved in these data points (e.g. I am my mother’s daughter.) It’s a relatively new resource and we aren’t exactly sure how to handle it. It’s also plentiful (not scarce) and non-rivalrous (consumption by one doesn’t prevent consumption by another and one’s own data is not particularly valuable in and of itself.) It’s sensitive, personal, and tied to digital identity. Most importantly, we don’t understand its worth. Price is woefully insufficient in encapsulating its value and therefore markets are a poor fit for its exchange.

The recent Netflix movie, The Hack, illustrates the difficulty in valuing data poignantly. What is the value of our data? Well….what is the value of democracy?

Direct Data Monetization via Marketplace

There are many start-ups that aim to 1.) restore consumer control over data 2.) allow consumers to directly monetize their data via a data marketplace. The first is now feasible given blockchain technology and data portability regulation. The second has proven very challenging. To start, valuing user data is nearly impossible. Several approaches are outlined below.

Senators Mark Warner and Josh Hawley have introduced a bill requiring large tech companies to publicly put a price on their users’ data. Amazon has apparently decided that unlimited access to a Prime member’s browsing activity is worth a $10 coupon for those spending at least $50.⁵ Amazon also offered consumers a $25 gift card in exchange for an in-person, 3D, full body scan.⁶ For an idea of how ridiculous that seems, Hu-Manity estimates the “human data market” generates between $150 to $200 billion annually.⁷

Do not ask the buyer to set the price.

Unfortunately, consumers are not much better at valuing their data. They have been giving their data away, without direct monetary compensation, for over a decade. This makes it very difficult for consumers to value their data or to determine a consumer’s willingness to pay for privacy. Most data marketplace start-ups have found that they have to use a simple “give this, get that” model to help consumers conceptualize the value of their data. Most companies attempting to allow consumers to directly monetize their data have settled on a three tier model, whereby data is classified into low, medium, and high value tiers. The lowest tier can be valued as low as $0.03 cents / month.

Expert calculations place the value of data for a “typical person” between $100 to $1,000 a year.⁸ For reference, crude calculations indicate that Facebook generates~$35 / year per monthly active user in the US. However, it remains unknown whether restoring user control over data might have a “deflationary” effect on its value. If this data is no longer controlled by rent seeking gatekeepers with outsized bargaining power, it is logical to assume that the willingness to pay for that data might then decrease.

Apart from valuation, UI/UX friction is high with most of these products. The on-boarding process remains a point of high friction (some products require a minimum of ten minutes to import all accounts and download new apps), although innovation is happening post-GDPR. More importantly, most of these applications seem to focus on the moral argument that users have a right to own their data (#31) rather than creating a user experience that attracts users at scale.

It’s unclear how the value an individual consumer would derive from their data compares to the compensation threshold that would be needed to offset the current friction in the user experience of actively managing and monetizing one’s own data (key management, porting over data from multiple silos, etc.)

Enterprises also run into adverse selection issues, wherein the consumers that are more likely to actively manage and monetize their own data are not necessarily the consumers that enterprises want to reach. Furthermore, it is nearly impossible to prevent the formation of a secondary market after data is shared with a third party since once information is known it is hard to prevent duplication. The terms and conditions of most data marketplace start-ups stipulate that once the data is sold to a third party, they are not responsible for what the purchaser does with that data. Zero-knowledge proofs are a potential solution, but are best suited to questions that can accommodate binary answers and are not operational at scale.

Finally, as mentioned above, user data points in isolation are not terribly valuable and many of these solutions struggle to reach minimum efficient scale. Industry professionals estimate that the minimum threshold in a data marketplace is 100k-200k users. That is what is needed to get most vendors interested in a given data set, which in turn allows users to monetize their data at higher rates, creating the necessary, but thus far elusive, flywheel.

In short, data marketplaces are one solution to the data privacy problem, but it’s not a solution that seems to resonate with consumers. That could begin to change with Gen Z. Digital natives (or the generations that have never lived without smartphones) understand that their identity and world are more digital than not, and they care more about protecting them.

Privacy Preserving Data Analysis and Exchange

Other solutions enable individuals to bring their data together in one secure location, but rather than creating a marketplace for this data, they enable privacy preserving analysis. This facilitates local analysis (on a consumer’s device, for example) without comprising privacy, eliminating the need for a consumer to send data to a centralized server. Some of theses applications use this analysis to improve personalization or to provide services without comprising privacy.

Data points in isolation are not very valuable. In contrast, having access to a fuller set of consumer data, or the results of analyzed data, is valuable and actionable. The consumer doesn’t need to understand, conceptualize, or value their own data and the enterprises purchasing this data don’t need to change their operations dramatically or shift their mindset. The whole process occurs similarly to the way it currently does, except it takes place on privacy preserving infrastructure.These models generally utilize revenue share type agreements, which are easier for a consumer to understand and which require less active management than the micropayment model common with data marketplaces.

These solutions also provide clear benefits to both sides of the exchange. They reduce anxiety for consumers concerned about data breaches and privacy violations since the raw data resides locally on their phones. Enterprises avoid the liability of holding sensitive data and demand channels looking to purchase GDPR compliant data sets are provided with a reliable supply. Healthcare seems to be one of the strongest near term demand channels. This is beneficial since it reduces the scale barrier outlined above, as clinical trial data sets require as little as 300 users. Valuation in this context is also easier. There are methods (some of which are being pursued at Microsoft) to determine the marginal effect of new data on machine learning models, though many conceptual and computational challenges remain.¹³
['Justine Humenansky']
['Internet', 'Technology', 'Blockchain', 'Advertising', 'Innovation']