Flurry now measures apps used on more than 1 billion smartphones and tablets each month. As connected devices reach critical mass, marketers are more seriously incorporating mobile into the marketing mix. But there are pros and cons. While the collective size of the mobile audience is rivaling that of TV and other media, it still requires aggregating the audiences of many apps to reach what can be reached through a few TV programs. That said, the numbers are likely closer than you think. Additionally, mobile offers unique ways to engage consumers given its “always on, always present” characteristics.
In this report, we look into what it takes to reach comparably sized audiences across different media like television, print, online and mobile apps. We also drill down into how the size and engagement of the mobile app audience varies across days of the week and hours of the day, and how it presents unique opportunities.
Let’s start by considering when people use apps.
The chart above shows how app usage varies over the course of a day, cut by weekend versus weekday. Data used for this chart comes from the top 250 iOS and top 250 Android apps measured by Flurry Analytics during February 2013. Through the top apps Flurry sees, app usage spikes during primetime to a peak of 52 million consumers. Make a mental note of that number, because we’ll revisit it a little later.
Comparing weekday to weekend curves, the general shape is similar. App usage ebbs overnight and then grows throughout the day, peaking in the early evening. While weekends also have a distinct primetime window, they see higher daytime usage across the day between 9:00 AM – 5:00 PM, ostensibly when someone would normally be working. However, the overall difference in audience size during the day between weekdays and weekends is not substantially different. Let’s look at 11:00 AM, for example, when the number of people using apps varies the most between weekdays and weekends. The size of the audience during this time is only 25% greater on weekends. Looking at it another way, this means that during the normal workday, people use apps at least 75% as much as they do on weekends. This creates a unique opportunity for advertisers to reach desired audiences over the course of the day via mobile.
The App Audience: Big But Fragmented
Now, let’s return to that 52 million primetime app user number. To get to an audience of that size, you’d need to combine the circulation of the largest 200 weekend newspapers in the U.S. or combine the audiences for the 3 most highly rated primetime TV shows during a good TV week (e.g., The Big Bang Theory).
We believe this comparison says a couple of important things about the app audience: first that it has reached critical mass, and second that it is still highly fragmented relative to more traditional forms of media. Additionally, while we don’t compare costs in this study, it is far more affordable to reach an audience on mobile versus Print or TV.
Now let’s consider how the app audience compares to the audience that is reachable through larger digital devices like laptops and computers. Flurry measured 224 million monthly active users of mobile apps in the United States in February of this year. During the same month, comScore counted 221 million desktop and laptop users of the top 50 digital properties in the United States. From this, we conclude that the U.S. audience that is reachable through apps, albeit more fragmented, is now roughly equal to that which can be reached on laptops and desktops.
There’s An Audience for That, on Mobile.
Earlier this year, Morgan Stanley analyst Benjamin Swinburne showed that “There has been a 50 percent collapse in broadcast TV audience ratings since 2002.” As the prized 18 – 49 year old demo is further lured to digital media, marketers need to adjust. But the mobile industry also needs to do more to make media planning and buying more efficient for advertisers and agencies.
The more mobile ad networks increase their ability to deliver the right combination of reach and targeting, the easier it will be for advertisers to invest in mobile and leverage the unique value it offers. Mobile, in particular, can deliver different ads to different users within the same app or the same ad to similar types of people across different apps, based on the varying interests of those individuals. Dynamic segmentation is much more possible on mobile compared to earlier forms of broadcast media. Now, fast forward one year from now, by which time Flurry estimates the installed base of smartphones and tablets will have doubled to 2 billion active devices per month. That should leave marketers of nearly every product thinking: on mobile, there’s an audience for that.
Five years ago, the iPhone ushered in the era of mobile computing. Today, more than a billion consumers are “glued” to these devices and their applications, impacting nearly every aspect of their lives. For businesses, opportunities seem endless and disruption is everywhere. The list of disrupted industries is long, including communications, media and entertainment, logistics, education and healthcare, just to name a few.
The past five years at Flurry have been wildly exciting. We joined an industry just as gas was forming to ignite a Big Bang, and we’re still orienting ourselves within its rapidly expanding universe. Since early 2008, we’ve worked with tens of thousands of developers to integrate our analytics and ad platforms into their apps. Today our services have been added to more than 300,000 applications and we measure usage on more than 1 billion monthly active smart devices.
On the five-year anniversary of launching Flurry Analytics, we took some time to reflect on the industry and share some insights. First, we studied the time U.S. consumers spend between mobile apps and mobile browsers, as well as within mobile app categories. Let’s take a look.
Today, the U.S. consumer spends an average of 2 hours and 38 minutes per day on smartphones and tablets. 80% of that time (2 hours and 7 minutes) is spent inside apps and 20% (31 minutes) is spent on the mobile web. Studying the chart shows that apps (and Facebook) are commanding a meaningful amount of consumers' time. All mobile browsers combined, which we now consider apps, control 20% of consumers' time. Gaming apps remain the largest category of all apps with 32% of time spent. Facebook is second with 18%, and Safari is 3rd with 12% Worth noting is that a lot of people are consuming web content from inside the Facebook app. For example, when a Facebook user clicks on a friend’s link or article, that content is shown inside its web view without launching a native web browser (e.g., Safari, Android or Chrome), which keeps the user in the app. So if we return to the chart and consider the proportion of Facebook app usage that is within their web view (aka browser), then we can assert that Facebook has become the most adopted browser in terms of consumer time spent.
The App World
Five years into its existence, the app economy is thriving, with The Wall Street Journal recently estimating annual revenue of $25 billion. Once again, we have to appreciate that this economy did not exist until 2008. As we looked for possible signs of slowing, we could not find any, largely due to the fast adoption of tablets just after smartphones.
In fact, not only is the installed base of devices growing, but also the number of apps consumers use. Our next insight comes from studying how many apps the average consumer launches each day. For this snapshot, we compared three years of worldwide data, taking the 4th quarters of 2010, 2011 and 2012.
From left to right, we see that the average number of apps launched per day by consumers climbs from 7.2 in 2010 to 7.5 in 2011 and finally to 7.9 in 2012. This is not a material change, which is a good thing. To us, the steady growth rate indicates that the app economy is not yet experiencing saturation, as consumers steadily use more apps over time. And while there are more apps in the store, large numbers of them have short lifespans, such as books, shows and games. Assertions that people are using fewer apps in 2012 than they did in 2010 appear to be incorrect. While one could observe that consumers use only 8 apps per day among the million+ available between the AppStore and Google Play, one also needs to remember that the 8 apps each consumer uses varies widely. This creates a marketplace that can support diversified apps.
Finally, we studied a sample of more than 2.2 million devices that have been active for more than 2 years to understand the mix of new versus existing apps people use over time. To do so, we compared Q4 2012 to Q4 2010.
The chart above shows that, on average, only 17% of the apps used in Q4 2010 were in use earlier in the year on a device compared to 37% in Q4 2012. That means that 63% of the apps used in Q4 2012 were new, and most likely not even developed in 2011 (or possibly poorly adopted). We believe that with consumers continuing to try so many new apps, the app market is still in early stages and there remains room for innovation as well as breakthrough new applications.
The Web World
Looking again at the first chart in this study, while also considering the latest numbers from IDC, which projects that tablets will outsell desktops this year and notebooks next year, we draw the conclusion that the web, as we know it, is already facing a serious challenge. Does this mean the web is dead? We don’t believe so. On the contrary, we believe that the web will change and adapt to the reality of smartphones and tablets. Websites will look and behave more like apps. Websites will be optimized for user experience first and search engine optimization second. This supports the trend of mobile first and web second, which brings both mobile app and user experience design to the mobile web. Simply compare Target’s app on iPhone to its mobile web site (target.com) accessed from the iPhone. The mobile web site looks and behaves similarly to the Target app, albeit a little bit slower.
… and Facebook
Continuing to think about the first chart, it appears that mobile, once perceived as Facebook’s Achilles' heel, has become Facebook’s biggest opportunity. Consumers are spending an average of nearly 30 minutes per day on Facebook. Add to that Facebook's massive reach, as well as their roughly billion mobile users per month and you have a sizable mobile black hole sucking up peoples' time. The 30 minutes a day is a worldwide average which means a large group spends even more time on Facebook (possibly hours) watching and participating in what has become the ultimate reality show in which the actors are you and your friends.
The disruptive force of the mobile app economy has created opportunities, rising stars, instant millionaires, dinosaurs and plenty of confusion. However, one undeniable truth is that tablets and smartphones are eating up desktops, and notebooks and apps (including the Facebook app) are eating up the web and peoples’ time.
Flurry now detects about 1 billion smartphones and tablets in use around the world every month. In the last 30 days, we saw activity on more than 2,000 unique device models. As the device base grows, we’re seeing an increasing variety of screen sizes, from sub-smartphones to full-size tablets and beyond. This poses both challenges and opportunities for developers who must consider how audiences, usage behavior and app category affinities vary by form factor.
This report reveals which form factors and screen sizes consumers use most, and for what categories. For this study, we focused on the top 200 device models, as measured by active users in Flurry’s system, which represent more than 80% of all usage. Doing so, five groups emerged based on screen size:
1. Small phones (e.g., most Blackberries), 3.5” or under screens
2. Medium phones (e.g., iPhone), between 3.5” - 4.9” screens
3. Phablets (e.g., Galaxy Note), 5.0” - 6.9” screens
4. Small Tablets (e.g., Kindle Fire), 7.0” - 8.4” screens
5. Full-size tablets (e.g., the iPad), 8.5” or greater screens
Mid-Sized Smartphones Dominate. Phablets are a Fad.
The top bar in the chart below shows how the top 200 device models break down by form factor in the market. Starting from the left, 16% of devices have screen sizes that are 3.5 inches or fewer in diagonal length. 69% of devices are between 3.5 inches and 4.9 inches, which includes iPhone. The light gray are made up of “phablets” such as the Galaxy Note. The orange are small tablets such as the Kindle Fire and iPad Mini. Finally, the far right shows that 7% of the device models in use are full sized tablets such as the iPad. The two bars below show distributions by active devices (taking into account that some device models have more users than others) and the number of app sessions (taking into account that some device models get used for more app sessions per user than others), respectively.
Notice that while 16% of the device models in the market are small phones, they account for only 7% of active devices once users per device are taken into account and 4% of overall app sessions. The opposite is true for tablets, which account for 7% of the top 200 device models yet 15% of all active users and 13% of all app sessions. On the small end, we believe this is because smaller device models, including most BlackBerry devices, are older and therefore have fewer active users per model. They are also not as well-suited to apps because of their small screen sizes. Full-size tablets, however, are ideal for using applications and therefore see a disproportionately higher percent of sessions. They also tend to have more users per device model since this class of devices has been dominated by iPad.
The ‘Is it a phone or is it a tablet’ devices otherwise known as phablets have attracted interest, but currently command a relatively small share (2%) of the device installed base, and their share of active users and sessions is also relatively small.
Form Factor Varies by OS
Not surprisingly, the form factor share of device models and active device users varies by operating system. The chart below shows share of active users by form factor for the different OSs. Inspecting the chart, medium phones are the dominant form factor on all operating systems, except Blackberry, which still has more active users on small phones. Android owns the phablet market and also has the greatest proportion of devices using small tablets. iOS has the greatest share of active devices using large tablets. The only Windows device models that are in the top 200 device models in terms of active users are medium-sized phones.
Tablets Are Gaming Machines
The chart below shows how total time spent in select popular categories is distributed across form factors.
Starting at the top, notice that nearly a third of time spent playing games take places on larger devices, namely full-sized tablet, small tablets and phablets. And while they command consumer time spent, they represented only 15% of device models in use in February and 21% of individual connected devices. These differences are statistically significant.
Studying books and videos, it’s somewhat surprising that tablets, which possess larger screens, do not see a larger proportion of time spent. An explanation for the high concentration in time spent in smartphones could be that consumers watch videos from their smartphones on-the-go (e.g., commuting to work on public transit), whereas they opt for a bigger screen to watch video (e.g., computer or TV) when at work or home. We expect that tablets may represent a greater share of time spent in book and video apps in the future as tablet ownership expands and tablet owners branch out into more types of apps.
Consumers Signal Preference for Smartphones & Tablets
As OEMs experiment with an ever-expanding array of form factors, developers need to remain focused on devices most accepted and used by consumers. From our study, consumers most prefer and use apps on medium-sized smartphones such as the Samsung Galaxy smartphones and full-sized tablets like the iPad. In particular, smaller smartphones under-index in terms of app usage compared to the proportion of the installed base they represent, and would suggest they are not worth developers’ support. Phablets appear to make up an insignificant part of the device installed base, and do not show disproportionately high enough app usage to justify support. Tablets, on the other hand show the most over-indexing of usage, especially in games. The success some game developers are having with a tablet-first strategy, like dominant game maker Supercell, may also inspire developers of other types of apps to consider focusing on tablets.
GDC is in San Francisco this week, just next to Flurry’s headquarters. By the size of the crowds, we (very scientifically) estimate that attendance should easily surpass last year’s record of 22,500. Having tracked the growth of mobile games for several years, we weren’t surprised to see more than 30 sessions during the week focused on smartphone and tablet gaming.
Here’s the big picture, based on our estimates: There are now over 1 billion active smartphones and tablets using apps around the world every month. And of all the apps consumers use, games command more than 40% of all time spent. Looking at revenue, games also dominate. Today, for example, 22 of the top 25 grossing apps in the U.S. iTunes App Store apps are games. Gamers spend money, and game makers are in love.
In this installment of research, Flurry studies how age, gender and engagement vary across key game types. Understand this, and a game developer can design a more engaging game that appeals to the right audience. In short, they can build a better business. In this study, we included more than 200 of the most successful free iOS games, with a total audience of more than 465 million month active users. For a better comparison, we organized these games by their game type (aka game mechanic) instead of traditional, less granular genres. Let’s start by looking at how different kinds of games appeal to gamers by age and gender.
The chart above plots game types by age and gender. From left to right, we show what percent of the game audience is female, with the far right equaling 100% female. The opposite is true for males. For example, 0% female equals 100% male. From bottom to top, we show the average age of the game type’s user base, between 20 to 50 years old. Putting it together, games in the upper right quadrant are preferred by older females. Games in the lower right are preferred by younger females. Games in the lower left are preferred by younger men, and so on.
Young Men and Their Competition
Inspecting the chart, the tightest cluster appears in the lower left; specifically, game types such as Shooters, Racing, and Action RPG skew younger and more male. Card-battle and Strategy games also skew toward younger males. The only genre that skews toward males over 35 is Casino/Poker, with pure poker games skewing even more male than the game type as a whole. This appears to leave a big gap in the market for developers who can create games that appeal to middle-aged and older men.
While men tend to gravitate toward competitive games, women gravitate toward games that are less competitive and tend to be played in a more enduring way. These include Management/Simulation games where players can build out an environment, Social Turn-Based games in which they can play over time with friends, and Match3/Bubble Shooters and Brain/Quiz games, to which users can frequently return when they have a few spare minutes. Slots and Solitaire are both solo-play game types that skew toward females who are over 40, suggesting that they serve as long-term time-fillers.
From a marketing perspective, mobile game publishers can also leverage this knowledge to design targeted campaigns appropriate for the kind of audience to which a game appeals. Flurry’s ad network, AppCircle, allows publishers to target specific demographics for efficient spending.
From Courting to Betrothed
We find that mobile gamers tend to prefer playing a few kinds of games and demonstrate highly predictable play patterns. In other words, they form relationships with their games. Savvy publishers understand these dynamics and use them to inform acquisition strategy, gameplay design, and both in-app purchase and ad-based monetization tactics.
In the chart below, we map game types by usage and retention. On the y-axis we show the number of times per week consumers play different game types. On the x-axis we show how long different games retain their user (i.e., Flurry defines Rolling Retention as the percentage of users that return to the game 30 days after first use, or any day after that). To see how usage and gender work together, we’ve also colored-coded game types by whether they are more male, female or neutral in appeal. Let’s take a look.
The chart above reveals that different strategies should be employed for different kinds of games. It also shows, loosely, that women are more committed while men are more fickle. Sound familiar in life? Hmmm. For the gaming industry, the universal take away is that to optimize engagement, retention, and monetization, developers must tailor their mechanics and messaging to match their ideal target audience. Let’s take a tour of the quadrants.
“Players” try a lot of different games, play for only a short time and tend to be found in highly competitive games (e.g., “Player vs. Player style games). While fickle, they tend to have a high willingness-to-pay in order to progress faster in a game or increase their ability to compete at a high level versus other players. Attracting the right users through targeted acquisition can pay off, as those that stay will pay. Notably, Card-Battle games have very low retention but off-the-charts monetization, extracting enormous revenue from the small number of users that stick. One implication is that these games need to be highly polished at launch with updates ready to go, as gamers will discard games quickly and move on if the game fails to resonate. From a design standpoint, these games should offer immediate opportunities for users to advance by purchasing upgrades and boosts. For the users that might not spend, a lucrative option is to offer in-game currency for watching video ads.
“Going Steady” game types are found in the lower right quadrant of the chart. Usage is less frequent but retention is very high. While these gamers don’t play as often, they are loyal. This group of games tends to be easy-to-learn and easy-to-return-to even after a lapse in playing. They lend themselves to quick play while in a “wait state” (e.g., waiting in line, taking a bus or perhaps checking out of the meeting or class they’re in). Since these are not particularly immersive or competitive games, they are less likely suited to in-app purchase. However, they can generate significant ad impressions over time, and can be designed to show banner or interstitial ads without being overly disruptive to the experience. For games with larger audiences, publishers are utilizing mediation platforms that enable the use of multiple ad networks in one system, ensuring maximized fill and ad-revenue for each space.
“Committed” comprise of consumers who play games for the long-haul. As such, game makers should think of it like a marriage. Think about appoint mechanics like setting a date (hey, even married people need to keep it fresh). These games should be designed with deep content and not try to sell too hard to their users too quickly. From a monetization perspective, commitment-oriented games have great potential for in-app purchases since users of those games are likely to value such purchases and amortize them over long periods of gameplay. And while only the largest titles have achieved this to-date, this group of games are great candidates for in-app product placement. Additionally, with high impressions counts, it is worth publishers’ investment to implement monetization platforms that make ad spaces available to real-time bidded ad exchanges, ensuring they reach the brands and advertisers that value their audiences.
“Infatuated” consumers have fallen hard and fast for their games, but the candle that burns twice as bright burns half as long. They have crushes, and show binge behavior. During the “crush” window, the developer needs to work hard to extract as much revenue as possible. As such, developers must provide vast amounts of content to the users, consistently, in a short-window. Matching monetization to game type, the competitive nature of Strategy games, and Slots users’ incessant desire for in-game currency, make a solid in-app purchase strategy paramount. Sales, events, and purchase opportunities timed with key moments of emotional investment can drive significant profits for publishers.
There’s a Pebble on the Beach for Everyone
In gaming, there are a vast number of game types that attract distinct audiences. And these different consumer segments display very different usage patterns, which have direct implications on monetization strategies. As in life, where the richest relationships are borne from knowing oneself and his or her partner, game companies must also understand both. Only then can you get the most out of the relationship.
Just as a company might look to metrics such as their Net Promoter Score or individuals might look to their Klout Score to judge their social media influence, app developers want benchmarks to evaluate how their apps are doing relative to other apps.
To provide benchmarks, we studied apps by their retention and size of user base. We also compared these two dimensions to see how they relate to one another. For example, do apps with more users have stronger retention than those with fewer users due to network effects? Do apps with smaller audiences see higher retention because they focus more on the interests of a particular segment?
Apps By Number of Users
We started our investigation by identifying the apps that Flurry tracks that had at least 1,000 active users at the start of November 2012. That eliminated apps that were being tested or were no longer being supported. We then split apps into three equal-sized groups based on their total number of active users. To be in the top third of apps, an app needed to have 32,000 active users. To be in the top two-thirds, it needed to have 8,000.
Apps By Retention
We followed a similar process to categorize apps based on retention. For this analysis, retention was defined as the percent of people who first used an app during November 2012, who also used it again at least once more than 30 days after their first use. To be in the top third for retention, an app needed to have at least 37% of those who started using the app in November do so again more than 30 days later. To be in the top two-thirds, 22% of new users in November needed to use the app again more than 30 days later.
Combining User Numbers with Retention
Having classified apps into three groups based on both active users and retention, we then compared how the two metrics relate to one another. The proportion of apps that fall into each of the nine categories that result from considering retention and active users jointly is shown in the table below. If active users and rolling retention were completely independent, then approximately 11% of apps would be in each of the nine categories. As shown in the table, the mid level categories for each metric follow that general pattern, but the categories in the corners of the table don’t. The differences between what the distribution across the nine categories is, and what it would be if the two dimensions were completely independent, is statistically significant.
Fifteen percent of apps are in the enviable position of being the top third for active users and also in the top third for rolling retention. We refer to those as Superstar apps since they perform well on both dimensions. These apps are best positioned to generate revenue regardless of their monetization model. Another 17% of apps are at the opposite extreme: they are in the bottom third for both user numbers and retention. We refer to that category as a Black Hole. Apps in this “cell” could be relatively new apps that are still trying to establish a user base, old declining apps or apps that are of poor quality.
Possibly the most interesting apps are in the bottom right and top left corners of the table. We refer to the 6% of apps in the bottom right category as Red Dwarfs because they have a relatively small user base yet are doing well on retention. Those are likely to be successful long tail apps. In the opposite corner from that are 6% of apps we refer to as Shooting Stars since they have a lot of users, but may fade away quickly due to poor retention.
Time Spent by Retention and Active Users
Unsurprisingly, the average number of minutes per month users spend in high retention apps is greater than in low retention apps. This can be seen going from left to right in each row of the table. For example, Superstar apps have almost twice the average number of minutes per user than Shooting Star apps, 98 minutes versus 50 minutes. This correlation between average time per user and retention is statistically significant.
Average time per user per month is also positively correlated with the number of active users. This can be seen by looking from the bottom to the top of each column in the table. For example, users spend more than 50% more time in Superstar apps than in Red Dwarfs. Once again, this correlation is statistically significant; however the correlation between time per user and retention is stronger than that between time per user and active users.
Retention, Retention, Retention
These results imply that developers need to make retention their top focus. Developers can impact retention by shaping and modifying the app experience. It’s within their control. Furthermore, the association between retention and time spent implies that retention drives revenue. More repeat usage means more opportunities to generate revenue from in-app purchase and advertising. Finally, the more useful and compelling an app, the better it retains users, making acquisition efforts more efficient. Acquiring aggressively before an app retains well can be a costly mistake. On the flip side, an app that retains well can generate powerful word of mouth, which is the ultimate (and free) promotional machine. The more a developer masters retention, the better their chances of turning their Red Dwarfs into Superstars.
Suppose you’re an app developer who wants to ensure that your app is optimized to function well on 80% of the individual connected devices currently in use (e.g., my iPad, your Windows phone). How many different device models (e.g., Kindle Fire HD 8.9" Wi-Fi, Galaxy S III) do you think you need to support? 156. Maybe you’re okay with having your app optimized for only 60% of active devices. That still means that you need to support 37 different devices. Even getting to 50% means supporting 18 devices, as shown below. If you’re a large or particularly thorough app developer, reaching 90% of active devices will require supporting 331 different models.
The dominance of iOS and Android platforms has obscured the proliferation of connected device models. During January, Flurry detected 2,130 different device models with active users (defined as having app sessions during January), including 500 different device models with at least 175,000 active users.
20% of Device Models Is Still a Big Number
Using the 80/20 rule, the market for devices might even seem concentrated: just over 7% of device models account for 80% of active users. Still, the large total number of device models in use poses challenges for developers.
It’s obvious that different apps are required for different platforms. Developers can choose to serve only a portion of the app market by developing apps for only a subset of operating systems (and consequently a subset of device models). Even having made that choice, though, adaptations may be required to accommodate different versions of the same platform (e.g., iOS 6.x versus iOS 5.x, forked versions of Android, etc.), smartphones versus tablets and the increasingly wide variety of screen sizes and aspect ratios in which those devices are now available.
Developing apps on the device models that represent the majority of devices currently in active use has become an expensive and time-consuming process. Not optimizing or testing apps on devices being used by even a minority of people exposes developers to negative user experiences and potentially to buying expensive devices to troubleshoot problems as they arise.
Is the Market for App Development Ripe for Consolidation?
This fragmentation has the potential to change the app ecosystem by making it harder for small developers to compete since they are unlikely to have the resources to support the growing list of device models currently in use. They may also be disadvantaged in economies of scale in promotion (including word of mouth) if their apps are not available or do not work well on most device models. Scale is likely to be increasingly important when it comes to app development and that may lead to consolidation within the app development industry.
Developer surveys, such as Vision Mobile’s, consistently show that the revenue distribution for app developers is highly skewed: only a minority of developers make more than $500 per app per month. The increasing need for scale to ensure full functionality on the full range of connected device models in use may help explain why. The growing challenge of discoverability in an increasingly crowded app market is also likely to be part of the explanation.
So what is a small developer to do? One strategy is to focus on the device models used by the greatest number of people. Surveys consistently show that developer commitment to iOS is disproportionately strong relative to the market share for iOS devices. Our results suggest this trend is probably a consequence of developers seeking efficiency (the most users for the least work) because device models running on the iOS platform average 14 times the number of active users than device models running on other platforms. This is shown in the chart below in which the average number of active users for device models running on different operating systems are indexed to Android (where Android = 1).
It’s difficult to fully disentangle platform from manufacturer and comparing devices made by Apple to devices made by the three other device manufacturers with the greatest average number of active users per device model tells a similar story. This is shown in the chart below – this time indexed so the average number of active devices per Samsung device model = 1. As shown in the chart, on average Apple device models have more than seven times as many active users as Samsung device models and more than four times as many as Amazon device models.
App Sessions Are More Concentrated than Active Devices
Of course, some people use their devices more than others and many developers prefer to target heavier app users. So what about app sessions? They are somewhat more concentrated than active devices. As shown below, for developers to ensure they were optimized on the devices responsible for 50% of app sessions conducted during January, they would have needed to support only eight different device models and to cover 80% of sessions they would have needed to support 72 different device models. That’s still a lot of device models, but it’s less than half the device models required to reach 80% of active devices.
In addition to having more active devices per device model than other platforms, iOS device models average more app sessions per active device than device models running on other platforms. This is shown below, again using an index for which app sessions per active Android device are set to one. This further clarifies why developer support for iOS is disproportionate to iOS’ share of the installed device base. Developers can reach more active devices by developing for a smaller number of device models on iOS and they can also capture the attention of very active users. People who have iOS devices tend to have more app sessions, creating more opportunities for in app purchases, advertising revenue and paid app purchases.
Viewed at the manufacturer level, Apple device models average more sessions per device than device models made by the other manufacturers previously shown. This is shown below, again indexed so that average sessions per Samsung device = 1.
The App Development Company
With competition in the device market heating up, manufacturers seem likely to fill and expand product lines with an increasing number of devices intended to differentiate themselves and address the preferences of specific types of users. That implies that it will only become more difficult for developers to optimize, test and support their apps for use on all device models. And yet doing exactly that is likely to be increasingly important for app developers given the market for apps is also becoming more crowded and more competitive, making negative user experiences more damaging. Promoting apps and leveraging that investment in promotion across as many potential users as possible will also become all the more critical. Putting all of this together, we expect a future in which app developers are less frequently individuals with a creative idea and a laptop and more frequently, companies designed to develop, produce and distribute apps at scale.
Flurry recently revealed that China’s installed base of smartphones and tablets surpassed that of the United States. Further, two thirds of all app sessions now occur outside the United States. With the app market becoming increasingly international, developers need to better understand how app consumer behavior varies across different countries to remain competitive.
This report focuses on how the top 30 heaviest app using countries vary in terms of app usage. As developers build apps for the largest international markets, they need to consider deviating from what has worked in the United States, the former number one market. Can developers simply localize for different markets, or are there meaningful cultural differences in app usage to consider? How different is behavior in China and India, the world’s two most populous countries?
For this study, Flurry grouped countries according to their similarity in app category usage using cluster analysis. Cluster analysis is a statistical technique that creates groupings based on associations; in this case, among the proportions of app users who use different categories of apps. This technique controlled for differences in populations, device penetration rates and app store taxonomies. We ran this analysis for the top 20,000 apps in the 30 heaviest app using countries as of January 2013. For purposes of this report, we focus on app categories used by at least 5% of app users in at least one country cluster. We also excluded social networking, since use of those apps tends to be more country-specific.
Membership in the resulting country clusters are discussed next, followed by a description of some of the differences in app engagement across country clusters.
App Usage Around the Globe
The cluster analysis process produced six country groupings shown in the map below and the country list that follows.
As shown in the map above, the first group of countries in blue is made up of countries that tended to be early adopters of mobile technologies.
The second category, in purple, is comprised of the most hyper-connected parts of Asia: South Korea, Hong Kong and Taiwan.
China and Japan had app usage patterns that were unique to them, making each country its own cluster.
Most of the countries in green are neighbors in South East Asia; however, app usage patterns across the Pacific in Mexico also put it in that same category.
The final category, in yellow, includes many large countries, such as Brazil, Russia and India as well as smaller but influential countries such as Switzerland and Israel. Besides sharing similarities in app usage, these countries tend to have lagged behind the Mobile Pioneer and Connected Asia countries in adopting mobile technologies.
Countries shown in gray were not included in the analysis because they are not among the 30 heaviest app using countries.
Interest In Gaming Is Global. Genre Preferences Are Local
The chart below shows the proportion of app users who used apps within each of the gaming categories shown, as defined in Google Play, during January 2013.
Overall, games are the most-used types of apps in each country cluster, with the biggest Android game category being Arcade and Action games for all country clusters. While Android game categories follow a similar rank ordering across country clusters, there is clear variation between clusters. For example, compared to app users in Japan, almost twice the proportion of app users in the Equatorial Pacific country cluster use Android Arcade and Action games. And while countries in the Mobile Pioneers’ cluster are among the most enthusiastic users of Casual Games and Brain and Puzzle Games, they are less enthusiastic users of Arcade and Action games compared to those in most other country clusters.
The chart below shows similar data for iOS apps within each of the gaming categories as defined by the Apple App Store. Please note that these classifications have changed over time and that games are assigned to categories by developers; however those things are common to all countries and therefore should not, on their own, result in differences between countries.
Once again, note that the main Games category attracts a large proportion of people who use any iOS apps, and that the Equatorial Pacific has the greatest proportion of users and Japan has the least though the differences are not as great for iOS as they are for Android. It’s interesting to note that while Japan tends to lag the other country clusters in the proportion of device users engaging with most game app categories, the country that gave us karaoke leads in the proportion of app users who use iOS Music Games.
Interest In Productivity and Utility Apps Varies
While Japanese app users are disproportionately unlikely to play most types of games (with the exception of music, as noted above), they are disproportionately likely to use productivity and utility apps. Chinese app users are also disproportionately heavy users of these more functional types of apps.
Use of More Lifestyle-Oriented Apps Maps To Offline Behavior
Hobbies often associated with Japan came through in app usage for music games, and also in use of lifestyle-oriented apps in terms of Japanese enthusiasm for photography. Japanese device owners are more likely than device owners in other country clusters to engage with photography apps on both iOS and Android devices. Entertainment categories within both app stores are fairly broad so it’s not entirely clear why, but those from China and the Lumbering Giant country clusters are disproportionately heavy users of Entertainment apps on both of the major mobile operating systems.
Mapping the Future of Apps
While this analysis only scratches the surface of variation in usage of 20,000 apps across more than 800 million devices being used in 30 different countries, it shows systematic variation across country clusters even at a high level. This has important implications considering the great potential for growth of connected devices and app use in countries and country groupings such as China and the Lumbering Giants, given their large populations and relatively low current rate of device penetration. App usage patterns in those places don’t always mirror those in Mobile Pioneer countries, which up until now have been the source of a lot of app development. For example, productivity and utility apps are more popular in China and Japan than they are in the United States. Differences such as these suggest that app developers in Mobile Pioneer countries may need to give greater consideration to the usage patterns and preferences of those in other countries or else that we may see growing app developer communities in some of those other countries.
Just days into the Chinese New Year (Year of the “Snake” for anyone keeping track), China has passed the U.S. to become the world’s top country for active Android and iOS smartphones and tablets. This historic milestone takes place a year after Flurry first reported that China had become the world’s fastest growing smart device market. Since then, it took China’s rapidly growing middle class just twelve months to close the gap on the U.S.
For this report, Flurry uses its entire data set, tracking more than 2.4 billion anonymous, aggregated application sessions per day across more than 275,000 applications around the world. Flurry estimates that it reliably measures activity across more than 90% of the world’s smart devices.
Reviewing the chart shows that China and the U.S. had roughly the same active smart device installed base in January 2013, 222 million in the U.S. versus 221 million in China. We use a model to project the final February 2013 installed base for each country based on historical growth trends as well as the number detected devices per country through the first half of February. Flurry estimates that by the end of February 2013, China will have 246 million devices compared to 230 million in the U.S.
We also conclude that the U.S. will not take back the lead from China, given the vast difference in population per country. China has over 1.3 billion people while the U.S. has just over 310 million. Considering that the U.S. has the world's 3rd largest population, the only other country that could feasibly overtake China sometime in the future is India, with a population of just over 1.2 billion. However, with only 19 million active smart devices in India, China will not likely see competition from India for many years. Below, we show the top 12 countries by active iOS and Android installed base through the end of January 2013.
The chart shows that the U.S. and China each have more than five times the active installed base than that of the U.K., the world’s third largest market. Additionally, both China and the U.S. continue to see rapid device adoption. Year-over-year, compared to January 2012, the U.S. added 55 million new devices. However, in that same time, China added a staggering 150 million new devices. With its growth rate, China would have passed the U.S. earlier, except for the U.S.’s massive holiday season, which enabled the U.S. to hold off China for an additional two months.
The final chart in our analysis shows growth in the number of active smart devices per country, between January 2012 and January 2013. While China no longer leads the world in growth, it still commanded an impressive 209% rate of growth on top of a base of 71 million devices from January 2012. For this chart, Flurry selected countries that had a minimum of half a million devices as of January 2012. Countries that grew faster than China over the last year were Colombia, Vietnam, Turkey, Ukraine and Egypt. While the four BRIC countries are not all among the top 12 countries in terms of percentage growth (specifically, Brazil and Russia are not top 12 "growers"), all four are among the top 12 when calculating the number of net active devices added per market (i.e., Brazil +11.5 million, Russia +12.0 million, India +12.4 million, China +149.5 million).
In this new era of mobile computing, sparked by a confluence of powerful innovation across microprocessors, cloud storage and network speeds, Apple and Google have helped create the fastest adopted technology revolution in history, 10X faster than that of the PC Revolution and 3X that of the Internet Boom. And now, as the largest and fastest modernizing country in the world, Chinese consumers lead that revolution.
Today as those in relationships rush to stores to pick up Valentine cards and gifts for their significant others, single women looking for relationships may want to pick up their smartphones. Just in time for Valentine’s Day, Flurry explored user composition and behavior in a sample of smartphone dating apps. We found that in dating apps targeting both genders, there are typically almost twice as many active male users as active female users. For this analysis, we examined 20 top dating apps whose combined 17 million active users delivered more than 2.1 billion sessions in January 2013.
Women wishing to further stack the odds in their favor may wish to download an Android dating app. When we compared the user composition for a sample of dating apps available on both iOS and Android phones, we found that active users of Android dating apps skew even more male.
Young adults in search of a Valentine (or those in search of a young adult Valentine) also may want to download a dating app on an Android device. In looking at the sample of dating apps available on both iOS and Android, we found that adult users of Android dating apps are more likely to be under 25 than adult users of iOS dating apps.
The millions of people who use dating apps do so regularly. They typically open their dating apps eight times a week and use them for seventy-one seconds at a time. Users of dating apps for gay men are even more active. They typically use them twenty-two times a week for ninety-six seconds at a time.
The Super Bowl is one of the world’s top media events. This year’s contest, Super Bowl XLVII, was hosted in New Orleans and drew an average of 108.4 million viewers, the third largest audience in U.S. television history. According to Nielsen, previous Super Bowls captured the top two U.S. TV audiences, with last year’s event drawing 111.3 million viewers and the previous year’s attracting 110.0 million.
While the contest on the field pitted the San Francisco 49ers against the Baltimore Ravens, an equally fierce battle for consumer engagement was waged across multiple screens. As the world’s top brands paid up to $4 million to air 30 second television spots, consumers were more distracted than ever, accessing mobile apps and social media in droves. Twitter reported 24.1 million Super Bowl-related tweets, the most popular of which focused on Beyoncé, Destiny’s Child, the Superdome power outage and key game moments. Facebook reported similar increases in conversations around these topics.
Mobile Apps Make TV the Second Screen
In this report, Flurry finds that mobile appears to have become the first screen. The implication is that, from this day forward, as marketers advertise on television, they must ensure that the content is sufficiently compelling to pull the consumer away from her smartphone or tablet. While TV may continue to be widely regarded as the first screen, Flurry believes that brands need to reverse that logic in order to reach and engage their consumers.
For this study, Flurry measured U.S. app session starts, per second, over the course of this year’s Super Bowl, last year’s Super Bowl, and the equivalent time period on the Sunday before this year’s Super Bowl (to establish a baseline for an average Sunday) from 3 PM PST to 8 PM PST. Flurry Analytics is used by 275,000 apps, including many of the most-used apps, with aggregate daily usage sessions of 2.4 billion.
For this analysis, we estimated U.S. app session starts occurring on Super Bowl Sunday by sampling from our own data and extrapolating based on the proportion of the market that Flurry "sees." To be able to compare across last year's to this year's Super Bowl, we created an index where “100” represents a baseline for app usage. Let’s start by looking at how this year’s Super Bowl app activity compared to that of last year’s.
The chart above shows this year’s Super Bowl in blue compared to last year’s Super Bowl in grey. The spark lines show application session starts in the U.S. sampled from Flurry’s system, per second. The way to interpret the chart is that if the line is moving up, consumers are picking up their phones (or tablets). And if the line is moving down, consumers are putting down their phones (or tablets). In other words, when something on the TV cannot sufficiently hold the consumer’s attention, she often reaches for her connected device. The advantage for using mobile app usage as a signal is that we can accurately measure when consumers are interacting with the mobile apps. In this way, we can distinguish between active (consumer is using the "app") and passive use (app is just "on"). Using mobile app usage as a signal, the events to which consumers paid the most attention were the National Anthems, Halftime shows and close finishes.
A few structural differences to the length, shape and height of the curves are worth noting. First, last year’s Super Bowl was faster up through the first half, as we see that Madonna’s half time show started earlier compared to Beyoncé’s. Additionally, this year’s Super Bowl was further extended due to the 34-minute power outage in the Superdome just after the beginning of the 3rd quarter. Relative to last year’s Super Bowl, consumers began picking up their phones and tablets en masse during this period. Next, this year’s Super Bowl curve (blue) sits higher than last year’s curve (grey), which indicates that there was more relative app usage in the U.S. this year versus last year. Specifically, we measure a 19% increase in app usage between last year’s Super Bowl versus this year’s.
The chart above plots app usage during this year’s Super Bowl against the same time period from the Sunday before. This gives us a sense for how much application usage varies on a normal Sunday compared to Super Bowl Sunday. Overall, total app usage dropped in aggregate by only 5% from the Sunday before to Super Bowl Sunday, which suggests that the Super Bowl largely failed to curb consumer app usage when compared to normal behavior. The height and the shapes of the curves are very similar. More notable differences did appear from just before the Super Bowl started up until about half way into the second quarter of the game, where consumers appeared to be paying more attention to the Super Bowl (i.e., the blue line was modestly below the grey line for that period). We also note a spike in app usage during the Jeep halftime report during the sports analyst commentary, followed by a plummet in activity during Beyoncé’s performance. Next, during the outage, consumers began using their apps. After gameplay resumed, app usage was very similar to a normal Sunday except for the last minutes of this year’s close Super Bowl finish, as the 49ers mounted an exciting, narrowly-missed comeback.
Next we studied how app usage varied during different times during the Super Bowl: while the game was on, when ads were broadcasted, during halftime and during the power outage. We used app activity during the game as a baseline.
The overall finding was that app usage did not vary greatly between commercials and game play, with only a slight increase in app session starts during ads in this Super Bowl, and an even smaller decline in session starts during the last Super Bowl. In contrast, session starts dropped by nearly ten percent during this year’s halftime. That suggests that while Beyoncé was compelling enough to cause viewers to put down their phones, much of the game and many of the ads were not. The large increase in app session starts during the power outage provides additional evidence that TV cannot hold attention without compelling content. Consumers turned to their smaller screens in great numbers as soon as there was a lull in the action on TV.
Of course, there is variation within these averages. Groups particularly prone to starting app sessions during ads include: Photo & Video Enthusiasts, Real Estate followers, Small Business Owners, TV Lovers and Movie Lovers. For your convenience, you can find Flurry (psychographic) Personas listed here. Consumers less inclined to start app sessions during ads include iPad Users, Food Enthusiasts, Catalog Shoppers, Fashionistas and Home & Garden Enthusiasts. Those most inclined to take a break from their apps and watch the halftime show included Home & Garden Pros, Health & Fitness Enthusiasts, Fashionistas, Catalog Shoppers and Food Enthusiasts. Groups whose app use climbed most during the power outage – suggesting that they were paying closest attention to the game at other times – were Males, Seniors and Sports Fans.
Mobile Is Killing The TV Star
Ratings from Nielsen confirm that people continue to sit in front of TVs on Super Bowl Sunday. However, the fact that overall app usage declined by less than just 5% compared to same time period on the prior Sunday suggests that a large amount of consumers’ attention is spent in apps, even as they sit in front of the TV. This should cause advertisers to question the value of paying a premium for Super Bowl ads when the attention premium they command is eroding. That’s particularly true for some groups. For example, overall app usage by Moms, during the time the Super Bowl was on, dropped by less than two percent compared to the previous week. While Tide’s “Miracle Stain” ad was certainly entertaining, it appears that the “Mom” target market was not paying attention.
The price of a Super Bowl ad pays for a lot on mobile whether that’s in app advertising, sponsored content, in-app product placement or branded apps, and Flurry believes many marketers may benefit from reconsidering their media mixes in light of evidence in this report showing that unless exceptionally interesting things are happening on TV, a significant and increasing amount of consumer attention is spent using smartphones and tablets.
New Consumer Behavior. New Strategy.
Brands who continue to believe in the potential of TV during major events such as the Super Bowl must also now understand the multi-screening behavior of their target market, and take that into account in developing their campaigns. For example, marketers targeting Fashionistas would be well-served by scheduling ads to run during or near the half-time show, while running in-app ads during the game itself. The reverse strategy would apply to groups such as Sports Enthusiasts. These results also have implications for those who wish to run integrated campaigns across screens: those will only be effective if the TV portion is compelling enough to pull attention away from the screens in the hands of the audience.
With the holy grail of TV events disrupted, advertisers need to take note. The winner of the Screen Bowl is the smartphone. Mobile is here. Mobile is the new first screen.