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.
Apple and Google have ushered in a new era of mobile computing whose consumer adoption is rivaled only by the PC revolution of the 1980s and the Internet boom of the 1990s. Since 2007, more than 440 million iOS and Android devices have been activated, with 1 million additional devices across both platforms now activated each day.
On top of this massive and rapidly expanding platform, a software battle is raging. With very low barriers to entry, and friction-free digital distribution, companies have been feverishly building, shipping and updating applications, intent on capturing and monetizing consumer audiences. To illustrate this growth, let’s look at the number of available apps in the App Store vs. the Android Market.
This chart is comprised of publicly available data. Where data wasn’t available for the same month in both markets, we estimated the number of available apps based on interpolation (e.g., approximating a point between two existing data points), or by looking at the growth rate leading up to a specific month. The number of apps is growing significantly in both markets. And while the App Store has attracted more apps to date, the Android Market is closing the gap. Now, let’s turn our attention to total app downloads.
The chart above sums Android Market and App Store downloads per month. Starting on the left, with January 2010, we show downloads per month every three months, until we reach October 2011. In October 2011, we estimate over 2.6 billion apps were downloaded. The number of apps now downloaded is four times greater than this time last year, in October 2010. With the holiday season under two months away, the 3 billion-mark download per month mark surely will be shattered this December. Month-over-month, app downloads have been growing at an astounding rate 11.4%. With app downloads growing swiftly, even faster than the number of apps being made available, let’s now look at app retention.
This chart shows the percent of consumers that continue using an app, since their first use, over 12 months. At the far left, marked as month “0,” 100% of a consumer cohort begins using an app. After three months, 24% of them continue using. After 6 months, this percent shrinks to 14%, and, by 12 months, only 4% are left. For this analysis, we compiled data from 25 apps downloaded a cumulative 550 million times.
With app downloads increasing month-over-month and app usage not only climbing, but also surpassing web usage, we know that consumers are both discovering and using apps more than ever. And while the industry often talks about discovery as a problem, we think the real problem is traffic acquisition. To understand this, we turn to the web.
Online, website marketers don’t stop marketing after they get a consumer to visit the site only for the first time. They can get in front of the consumer in various ways again, and spur a return visit by having the consumer click on a link. Typically, online, a visit starts from an organic search result, but search doesn’t exist for apps the same way, and consumers seem to browse more, especially given touch interfaces. The closest thing to search in the app world is a consumer browsing the top ranking lists, which represents “popularity” in a similar way to top ranking organic search results. However, in the app world, top rank lists are more like “paid search” since heavy advertising is what typically launches an app to rank high, at least for a while.
Further, always trying to rank high, as a tactic, is not only untargeted and expensive, but also suffers from diminishing returns. First, the bar required to make the top 25 keeps rising, as the installed base of consumers grows and more apps compete for a fixed number of top spots. Regarding diminishing returns, an app can only appeal to first-time-users each time it ranks. It’s a pure first-time acquisition tool. App users don’t re-launch apps when seeing them in the top rankings. They need to go to their app icon and launch from there. So as an app’s installed based grows over months, even years, the relative number of incremental users that can be added from ranking in the charts continues becomes relatively smaller. In other words, over time, an app is better off targeting its much larger installed base of users to increase usage. This is the equivalent of traffic acquisition.
The key challenge is that developers lack the tools to bring traffic back to their app, post-download. And, therefore, the industry has a traffic acquisition problem, not a discovery problem. Only when compelling ways of connecting with existing app users are established, that allow the easy re-launch of an app, can app makers address retention through marketing, and fully control their own traffic acquisition.