Winners and losers in the global app economy

Hint: Where you live matters

 

This post discusses a few of the findings from a recent research project I’ve done at Caribou Digital, which updates and extends my previous work on the geography of the app economy. This post also appeared on Medium.com.

The buying and selling of apps is becoming a big business, with Apple and Google paying out a combined $17 billion to developers in 2014.[1] But aside from a few public announcements by a handful of app developers, we really don’t know much about where all that money is flowing. The ubiquity of mobile technology and networks means there are now app developers and app consumers just about everywhere in the world. But what this research shows is that the vast majority of successful developers — and almost all the app store revenue — is located in the largest economies, leading to a very skewed global landscape of participation and value capture.

The app store model is interesting because it has some inherent contradictions. On the one hand, it is a meritocratic system that enables anyone to be successful — the digital nature of app production coupled with the global reach of telecommunications networks allows developers located anywhere to easily sell to consumers around the world (Exhibit A: Flappy Bird creator Dong Nguyen).[2] The app stores disintermediate previous value chains, allowing producers (developers) to connect directly to consumers and avoid hiring publishers or regional distributors. And all producers pay the same margin (30%); there are no backroom deals or favorable terms for the biggest producers or someone’s cousin. As Julien Codorniou, director of global platform partnerships at Facebook put it, “[I]t’s a flat world where everybody competes with everybody — everybody is treated equally in the app stores of the world.”[3]

Yet at the same time, the app store model leads to an uneven playing field. That disintermediation has the effect of shifting all risk to the producer, who is essentially selling on consignment, and has no protection if a product doesn’t sell after investing in its production (either way, the platform doesn’t lose). And some argue that a progressive margin would actually be more fair, since it would allow the smallest producers to keep more of their earnings when they are most critical for keeping the lights on. But most importantly, the model has led to winner-take-all markets, where a handful of apps rake in tremendous profits (Japan’s Mixi earned $4.2 million per day with Monster Strike) while the long tail of less-popular apps and developers struggle to support themselves.[4]

The research

I was able to explore these tensions in a 6-month research effort on the global app economy, with the goal of answering basic questions such as: Who is making apps? and Who is making money? As a geographer, I’m especially curious about how these dynamics are configured across space, so the research is anchored by a country-level analysis of 37 different national markets. For each market, the team recorded the 500 top-ranked apps in both the “Top Grossing” and “Top Downloads” categories, for both the iOS and Android platforms, and then performed a manual online search to identify the city and country location for the developer. To be clear, the sample represents only the tip of the iceberg in terms of developer population — there are hundreds of thousands of developers, and we captured only the ~8,000 that were ranked in the top 500 in the app stores. The resulting data reveal not only see which countries are successfully developing apps, but also where those apps are being “exported” into other national markets worldwide, painting a picture of the global flow of app commerce. I then used a simplified power law curve to estimate value capture across all developers in our sample, showing the often stark difference between app production and app revenues.[5]

Where are the developers?

The research shows pretty clearly that developer participation in the app economy is heavily skewed toward the largest and richest economies, with the United States, Japan, and China dominant. The winner-take-all nature of the market means that the top-ranked apps in the most-lucrative markets earn multiple orders of magnitude more revenue than low-ranked apps in markets of the Global South. The result is that 95% of the estimated industry value is being captured by just the top 10 producing countries.

For lower-income countries, the outlook is relatively bleak: Most have very few developers, and even those who had significant numbers of developers — for example, India — earned very little revenue; as a group, the 19 lower-income countries in our sample earned an estimated 1% of global app economy revenues.In fact, a small handful of firms earn so much that they outperform most of the countries; we estimate that Finland’s Supercell earns more than 28 of the countries in our sample, combined.

 

Participation and value capture: For each country in the sample, we show the percentage of the total developers and percentage of total estimated value. This reveals which countries are capturing an outsized percentage of the total app store revenue compared to the number of developers they have.

How markets shape participation and trade

While the data can’t reveal all of the causes of the skewed distribution, there was evidence of both market-based factors and platform-based factors. In the former category, it is clear from the analysis that home field advantage plays a role. That is, consumers in every market showed a preference for apps developed by local producers; the degree varied, but was most pronounced in the East Asian countries, with Japan, South Korea, and China all dominated by local producers (Japan was the highest, with about 70% of the market controlled by Japanese developers).

This isn’t very surprising, as it’s always easier to design products and services for people like yourself rather than others, and in some places — such as East Asia — the linguistic and other cultural differences can be very strong. But one knock-on effect of home field advantage is that the most lucrative national markets, usually considered to be the U.S., Japan, China, South Korea, and U.K., are harder to penetrate by foreign firms. Put another way, Indian developers do very well in the Indian market. But there’s no money, relatively speaking, to be made there. And few Indian developers are able to enter the U.S. or other lucrative markets.

In fact, when we look at lower-income countries vs. higher-income countries in our sample, we see a sharp distinction in the ability of developers to enter foreign markets: in higher-income countries, about 29% of developers were unable to export and limited to their home market, while in lower-income countries, that figure rose to 69% of developers. When your home market has limited revenue, that constraint hurts.

The figure below illustrates some of these dynamics, showing the regional trade (and market insularity) of East Asia. The left column is the origin country of the app developer, with the width of the flows representing the number of apps that were “exported” to the national markets shown in the middle column. The width of the flows on the right side represent estimated financial value being captured by each country, based on the rank of the app and rank of the national market.

In this chart you can see how dominant local producers are in Japan, China, and South Korea, whereas Hong Kong is much more international. What is most surprising is the very low levels of intra-regional trade among the big three economies — despite their close spatial proximity, both Japan and South Korea export very little in the region. This is especially notable given the strong role of mobile gaming apps, and Japan’s history of exporting gaming technology and culture worldwide.

Regional trade and value capture for the East Asian markets. The full report has charts of all the major regions.

Why the platform matters

Not all of these effects are market-based — some are due to platform design and structure. I’ll describe three here. First, Google only allows developers in certain countries to monetize their products through the Google Play app store: Developers who want to earn revenue from Android apps on the Google Play app store are required to set up a merchant account, which is what links their bank account to Google so that they can receive funds from the app store. However, Google merchant accounts are not available in 74 countries, with half of sub-Saharan Africa and much of Latin America excluded.[6] While not every developer is trying to directly commercialize her apps, the inability to monetize one’s app directly through the app store is a deterrent for participation, and the data show very few developers from excluded countries.

The second factor is the structuring of the app stores into discrete national stores. This allows the platform to adhere to different tax laws, content regulations, and copyright licensing for music and other media. For example, Australia and Belgium have shown indications of wanting to ban gambling apps, and Apple banned apps related to the Dalai Lama in China. But a secondary effect is that each app store maintains its own top rankings, reflecting that country’s preferences for apps and content. Given the home field advantage trend discussed above, this results in higher visibility (and thus downloads/revenue) to domestically popular apps and content. Our analysis shows that if the national store structure were abolished in favor of a single, global market, we would see a sharp decrease of 20%-36% in developer diversity — that is, the Supercells and Tencents of the world would overshadow smaller, less popular producers. If the EU’s efforts toward establishing a Digital Single Market [7] result in consolidating all the national stores, it could have the unintended consequence of hurting smaller EU developers in favor of the largest global brands.

And finally, the app stores, like all digital markets, are constrained by the human-computer interface. On the human side there are cognitive limitations: while digitization has allowed product catalogs to grow to millions of items, our ability to process information and choices hasn’t scaled accordingly. Miller’s Law — named after the famous experiments by psychologist George Miller that identified 7 (plus/minus 2) as the number of objects a typical person can hold in short-term working memory — applies just as much today as it did in the 1950s.[8] On the computer side, even the largest screens have limited real estate, effectively constraining the number of choices or product options that a user can reasonably view. Whether faced with 1,000 or 1,000,000 choices, there is a limit to the cognitive load that users are willing or able to process, and they tend to employ behavioral heuristics, such as top ranking lists or user reviews, to aid in the decision-making. In the context of the app stores, these interface dynamics mean that those apps that occupy prime virtual real estate, typically because they are popular and thus highly ranked in categories or search results, are the mostly likely to be seen and selected for download or purchase, creating a virtuous cycle that reinforces the popularity of the top apps and winner-take-all effects.[9]

Parting thoughts

The global app economy is effectively controlled by the two largest platforms, belonging to what are at present the two most valuable companies in the world, located 9 miles apart in the most important technology cluster that exists today. Apple and Google have very different revenue models and core competencies, but their smartphone platform strategies have converged over time and are essentially similar, and represent a tremendous concentration of power.

The app economy has the potential to offer new economic opportunities to a wide range of independent digital producers, but its current state is skewed toward the largest firms and most lucrative markets. Proclamations that “everyone is treated equally in the app stores of the world” fail to acknowledge the very real institutional constraints, both market-based and platform-based, faced by independent developers — especially those in emerging economies. As more and more commercial activity is mediated by digital markets, it becomes increasingly important to identify and address these structural factors that shape outcomes. Because even when they are presented as open and meritocratic, technological systems are never neutral.



This research was supported by the excellent team at Mozilla Foundation. You can download the full report, free of charge, from the Caribou Digital website.



[1] Google paid out $7 billion, and Apple $10 billion, in 2014; this accounts for paid downloads and in-app purchases, but not advertising revenue from 3rd-party networks.

[2] Kushner, David. “The Flight of the Birdman: Flappy Bird Creator Dong Nguyen Speaks Out.” Rolling Stone, March 11, 2014

[3] Lauren Davidson, “How Facebook Is Fuelling the Growth of the Super Start-Up,” The Telegraph, August 9, 2015

[4] Craig Chapple, “Japanese Mobile Game Monster Strike Making $4.2m a Day,” Develop, August 19, 2015.
Vision Mobile (“Developer Megatrends: H1 2015”) estimates 60% of developers make $500 or less per month.

[5] See the full report for the complete methodology and its limitations

[6] Google website. https://support.google.com/googleplay/android-developer/table/3539140?hl=en

[7] “Digital Single Market — European Commission,”http://ec.europa.eu/priorities/digital-single-market/index_en.htm

[8] George A. Miller, “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information,” Psychological Review(1956).

[9] For a developer perspective, see Alex Austin, “Mobile App Developers Are Suffering,” https://medium.com/swlh/mobile-app-developers-are-suffering-a5636c57d576#.wv6ecgk7i

41 shades of Uber

While there's probably plenty of real Uber drama that would be worthy of that scintillating title, this story is about something decidedly less sexy: experiments.

Specifically, data-driven experimentation and testing that can only occur in a digital environment. At its most basic, this kind of experimentation is often framed as A/B testing, or split testing, where a content owner or publisher tests two different types of content--version A and version B--and then measures the response. Online marketers are famous for using this kind of test to evaluate different subject lines of marketing emails. They send out a few thousand using each version, measure the open rate or click-through-rate of each, and then select the best message to use for the rest of the emails.

In perhaps the most famous example of this kind of experiment, Google infuriated designers everywhere when it disclosed that it had actually tested 41 different shades of blue on users in order to determine the clinically optimal hue for encouraging users to click on links.  While this fealty to hard data over intuition caused a lead designer to quit ("I had a recent debate over whether a border should be 3, 4 or 5 pixels wide, and was asked to prove my case. I can’t operate in an environment like that. I’ve grown tired of debating such minuscule design decisions. There are more exciting design problems in this world to tackle.") this isn't that surprising for Google. It's core competencies have always been about collecting and sorting information, and using algorithms to determine optimal outcomes.

And while this degree of quantification--41 different shades?--seems extreme, there are obvious benefits to user testing. As information and content have become digitized and served through progressively more sophisticated new forms of displays, we have had to figure out (or listen to Jakob Nielson declare) how to apply design principles to new problems, new use cases. Things that seem obvious now--pinch to zoom, colored text is a link, menus and navigation expand to show more choices--weren't so in the early days of software and web user interfaces. Firms continue to spend a lot of resources conducting user experience research to determine how well potential users are able to complete specific tasks, learning in the process the barriers or bugs or wrong assumptions about how users will behave.

This kind of optimization is viable with digital products because the testing is typically very cheap, feedback close to immediate, and implementing the changes is often relatively easy. Change a few digits of the color value in Google's style sheets, and voila, the change is everywhere. Update your mobile application and push out a new version that updates all existing copies. The near-zero marginal cost of digital reproduction enables fast, cheap iteration, leading to incremental development approaches that continuously optimize.

These same characteristics of digital products are what allow Uber--essentially a software company--to conduct experiments with driver commissions. The company rolled out new tiered commissions for its drivers in San Francisco in April as part of a test to evaluate drivers' willingness to work for less. According to Forbes: "a small percentage of new UberX drivers will pay a 30% commission on their first 20 rides in a week, 25% on their next 20 rides, and then 20% on any rides beyond that. Uber is also testing the same commission in San Diego, except that the tiers are for the first 15 and next 15 rides in a week."

Because Uber tracks all aspects of driver performance and manages their accounts completely digitally, it is relatively easy for the company to filter out a subset of drivers (San Francisco-based), create rules (based on rides per week), and apply the tiered commission structure directly to their pay checks. Of course, recording lots of data and using it to manage your internal operations more effectively makes business sense.

But there's a fundamental difference with what Uber is doing with this commission experimentation. It's not trying to optimize the performance or satisfaction of its users. Uber is experimenting to see how low it can set wages before too many of its drivers quit. And it can play around with wages in very granular ways to optimize to the nth degree--that is, reduce as much as possible--how much it pays different drivers. In this example, new drivers and part-time drivers are penalized with higher commissions, but we can also imagine that Uber could pay less to, say, drivers who refuse to work on Sundays, or who live in certain neighborhoods. 

Some business owners may look at this situation with envy. Being able to know exactly how low you can set wages before employees quit could be a valuable cost-reduction tool. But most businesses aren't completely digital, and the cost of doing this kind of testing is prohibitive. That's a good thing. Because unlike testing for end-user experience, changing up peoples' wages (especially when payments are confusing, and many drivers don't even know how much they're getting paid) results in a more unpredictable income, which has very real negative economic consequences. From the Economic Policy Institute:

"Much of tipped employment is the epitome of “just-in-time” employment—adjusting staffing levels on an immediate basis in response to customer flows. While this may be good for the employer, it is far less beneficial for workers because it can produce highly unpredictable work hours, and thus highly unpredictable pay. Wage volatility is further exacerbated by workers’ reliance on tips from customers, which also vary considerably. A tipped worker’s paycheck can vary wildly depending on the fluctuations of customer tips and assigned shifts, making it difficult for tipped workers to budget, or make investments that require more stable and predictable income levels—such as buying a home or a car, or seeking further education."

Where does the wage experimentation end? How "optimal" can Uber become?  Consider the type of experimentation Uber is most (in)famous for: its "surge" pricing model, where the cost of rides to the end-user goes up during peak demand, creating a highly dynamic market for rides. Although Uber agreed to cap surge pricing during emergencies emergencies, it still vehemently defends its practice of balancing supply and demand. What, then, would stop Uber from more aggressively experimenting with the supply side via changes in wages? Given that the majority of Uber drivers are only working part-time for Uber, it could tie wages to the national unemployment index, knowing that as unemployment ticks higher, under-employed workers are more willing to take low-paying jobs. What if a large factory closes in a city, leaving hundreds of people without employment? Uber could quickly lower wages for drivers in that city, knowing that demand for work just skyrocketed. If Uber integrates actuarial risk models, credit scores, crime statistics, and so on, it could implement dynamic, real-time wages to its drivers based on their profiles. Anti-discrimination laws protect workers from bias based on race, age, and religion. But Uber might experiment with paying less to a male driver who lives in a poor neighborhood and drives a Buick compared to a female Prius driver who lives uptown. Discrimination could be very hard to prove.

Uber executives defended surge pricing with the logic of free market choice ("Nobody is required to take an Uber"), but the reality of some situations--natural disasters, emergencies--means that choice is conditional and relative. And as the nature of the workforce shifts from full-time employment with the concomitant legal benefits to part-time contractors with fewer rights--a debate that Uber is squarely in the middle of--we need to recognize the leverage and power that digitally based businesses have in controlling wages in a more dynamic fashion than ever before. Companies like Google, Facebook, and Amazon have become extremely sophisticated in their ability to process our digital lives and tailor their offerings accordingly. When companies like Uber use this same apparatus on their workers, we need to think about whether this kind of granular experimentation and control should be regulated.

Preliminary view of top 10 national app markets

This is an early glimpse into a larger analysis of app developers and the global app economy. The idea is to paint a picture of where value creation and capture are occurring in the major app markets (Apple and Google). This chart looks at the top 10 national markets (in terms of revenue), and shows where developers from each country "export" their apps to the other top national markets.  The data represents the top-grossing and top-downloaded apps in the iOS and Android app stores in March 2014.

The data show that U.S. developers are able to export into almost all the top 10 markets successfully. The East Asian countries have just as many developers, but they tend to stay in their domestic markets, exporting much less globally. 

Facebook's Internet.org: The walls aren't the problem

(This was originally written for Caribou Digital's website, and then picked up by Next Billion.)

Facebook launched Internet.org in 2013 to make free Internet access available everywhere, via pared-down web services focused on job listings, and agricultural, health care and education information - along with Facebook's own social network and messaging services. Until recently, Internet.org users could only access a few select websites, and Facebook determined which sites made the cut. But early this month, Facebook opened the Internet.org service to any developers that meet its criteria, stirring a new wave of debate and criticism about the initiative, which has been billed as a way to provide web access in low-income markets around the world. This new move is largely a response to the growing backlash from net neutrality supporters, especially in India, where some content partners pulled out of participating in Internet.org due to public outcry. (A good background article can be found here).

Critics complained that not only was “zero-rating” (in which telecom providers agree to absorb the costs of handling the data traffic so that consumers can receive services for free) fundamentally against net neutrality principles, but that Facebook would control which services (including which web sites) were offered for free as part of the Internet.org portfolio, creating a classic walled garden model. Given the expected advantages of a service on Internet.org versus a competitor not on Internet.org, Facebook would essentially be picking the winners and losers for these markets.

By opening Internet.org to all developers, Facebook is addressing this second criticism. Facebook VP Chris Daniels stated clearly that its intent with Internet.org is not to create another walled garden. If we leave aside the fundamental conflict between zero-rating and net neutrality, the underlying strategic intent and potential implications of this move are important to consider. Because while Facebook is only creating the rules for Internet.org, if the platform becomes a significant part of the mobile Internet landscape in emerging markets, the model that it establishes may become the de facto standard – just as Apple’s iTunes and App Store model set the standard for the first wave of the mobile app ecosystem, including the now-universal 30 percent margin that the platform owners take on apps.

The Internet.org platform will be open to any developer, but not any app, as there are three eligibility requirements all apps must meet: First, the app/service has to encourage users to “explore the broader internet” by limiting what is available for free through the app; in essence, Facebook is encouraging a freemium model. Second, the service has to be efficient with data, and work well on more basic phones, which means more text and fewer images. Third, the service has to comply with a set of technical requirements, including not encrypting traffic via HTTPS, or using JavaScript, Flash or SSL. The lack of encryption has generated criticism of how Facebook is handling privacy, especially given that all traffic will be served through Facebook’s proxy servers.

While on the surface Facebook seems to be creating another app store similar to Apple’s and Google’s, there are a few structural differences that are important to call out. First, this would be a mobile app store based not on an operating system, but on a service, signalling the increasingly powerful role that services – especially those like Facebook that capture user data – are taking in the mobile industry. Traditionally it has been Apple, Google and Microsoft that have been able to establish power through their respective platform ecosystems and app stores, all of which are based on an operating system. But the OS is losing its importance as the critical asset, in part because cross-platform interoperability is easier and cheaper, but especially because we are increasingly relying on cloud-based services - accessed through an app - and it is these services that are able to lock-in users. Simply put, most people who are heavy users of Facebook, Gmail, Dropbox and so on would rather switch to a different operating system/device than switch to a competing service; we are locked in by our personal data and will follow it.

Perhaps even more importantly, this new app store structure would reframe Internet and web access for the millions of people in the developing world who are its potential users. In the industrialized countries, mobile Internet usage has been steadily shifting away from the web and mobile browser, and toward apps, mostly because they’re purpose-made for specific tasks or entertainment functions, making them easier and faster to use. Compared to the open web, which is unrestricted and free-to-use, apps are more directly controlled (via the app store owner, who can determine access to the store) and monetized (via the app developer and the app store owner).

This gets flipped on its head in the new Internet.org platform, where the open and unrestricted web costs money, and it’s the directly controlled apps that are free-to-use. Given these options, how many new users are going to choose the open web?

Of course, this restructuring of controlled apps vs. open web affects producers as well. New firms seeking to enter the space will have to abide by Facebook’s terms of service and eligibility requirements, limiting the scope of innovation. For one, all apps/services on the Internet.org platform must agree to sharing user data with Facebook, which in turn will share it with mobile operators. More constraining is the “freemium” requirement, which, according to Facebook VP Daniels, encourages those services that “simplify features of their applications such that in order to access the entirety of their websites or their applications, one has to pay for it.”

Would a new form of Wikipedia be able to meet this requirement? How about Facebook itself? Facebook enjoyed tremendous user growth as a standalone website on the open web, with no requirement to limit its functionality or force users to pay for key features (it mostly sold ads). And when users signed up, there was no third-party entity above Facebook that was collecting data and sharing it with the user’s Internet service provider.

So it’s with some irony that Facebook is now using its dominant market position to push free access to Internet.org, and thereby directly weakening the role of the open web–the same innovative and unrestricted environment that helped it grow into a $200 billion company–in favor of a tightly managed ecosystem that it controls and mines data from.   

Opening Inernet.org to developers will probably improve public opinion of the initiative, and may have been a step that Facebook had planned all along. Because while the perception is that this opens the gates to the walled garden, Facebook remains firmly in control of regulating participation – and thus innovation – only now at a larger scale. But the more insiduous issue is that the new Internet.org will continue to position the open web as costly, and controlled apps as free, using affordability as a form of platform lock-in to a limited Internet experience with real data-privacy trade-offs. 

This garden has real dangers, and taking down the walls may only encourage more people to enter.