monetization

gaming

In the past 18 months, Android has emerged as a major gaming platform. Yesterday at GDC in San Francisco, we unveiled that over 70% of all Android devices engage in at least one gaming app per month. On the Flurry platform alone we see over 525 million worldwide Android devices actively engaged in mobile gaming each month. But the perception is that iOS is the more mature ecosystem and that Android is still sorting itself out, especially around business models. As we have done with the iOS platform, today we unveil the retention matrix for Android, which we hope is a helpful reference and guide for developers.

Let’s take a look at the retention matrix for 1,382 of the top gaming apps for Android. On the X-axis is 30-day Static Retention, as defined as the percentage of new users who opened the app 30 days after install (this is not to be confused with Flurry’s Rolling Retention, which looks at the percentage of new users who open the app 30 days or later after the install). On the Y-axis is the average sessions per week for that genre. The genres plotted on the chart have been manually curated by Flurry and reflect the game mechanic of the apps.

Quadrant I houses those evergreen genres that are most likely to keep their users highly engaged for long periods of time, and are typically dominated by advertising. These easy-to-play, repeatable games have strong staying power, as evidenced by their high retention. According to Flurry data, good ol’ Solitaire is the game that never gets old, with the highest retention of any genre at 50%. Not surprisingly, Social Turn-Based games have the highest session frequency and high retention given their social nature. Successful apps in this genre have built in appointment mechanics, requirements that a player return to the game in a certain time period to gain a reward, which are critical to get those high frequency and return metrics. Genres in Quadrant I are particularly amenable to ads as they have the potential to generate a very high impression count over time given their high frequency and retention rates. It’s not uncommon to see advertising generate 90% of these games’ revenue.

At these retention levels, re-engagement marketing becomes compelling, as there is opportunity to recapture users who are likely to stick around for another long spell. Since we know the peak usage for the average game only lasts two months, re-engagement is very important for these titles. Evergreen genres are also great games to use as a platform for promoting your other, higher ARPU (average revenue per user) titles.

Games in quadrant II have high frequency but lower retention rates. In other words, they’re used often but for limited time periods. This implies that those users who do stick around will be highly engaged. To maximize revenue in genres like management/simulation, slots, and strategy, tip the balance towards IAPs, as these players are willing to pay good money for content and capabilities. But don’t ignore the significant percentage of players who won’t pay. Use rewarded video ads to get the non-payers to engage more with the game by letting them earn the currency they value.

In these genres, take advantage of this intense play with offers and new content early and often (and at times of key emotional investment). Content releases need to be complete, submitted, and ready near initial push of the game, as players won’t stick around for improvements.

Genres in Quadrant III have relatively low retention and frequency. These genres have the pickiest audiences, but those that stay…pay. Like Quadrant II, focus on IAP and use video ad opportunities to monetize those users who don’t pay but want to continue playing. Well-spaced interstitials that are not too disruptive are another good choice for these genres. Developers should look to maximize revenue early in the lifecycle of game.

For these low-retention genres, it can be difficult to find right kind of user. What are known to be the highest ARPDAU (average revenue per daily active user) categories (Card-Battle and Strategy) have low “loyalty” – this is in part due to heavy marketing despite only a niche audience being interested in the genre. It’s worth the broad marketing for some of these games because when they do find a good user it is highly valuable. That said, targeting by age, gender, and persona can improve efficiency.

Quadrant IV, like Quadrant I, houses genres with extremely high retention, but relatively lower frequency. Match 3/Bubble shooter and Endless Runner genres are on the border here. We say “relatively” lower frequency as these games are still played at least once a day on average. Monetization strategy tips slightly more towards advertising given the high number of impressions generated over time. Of course, if you’ve got a hit like Candy Crush, the game can monetize quite nicely through IAP.

The first step in monetizing any app is acquiring new users. To understand whom to target, we next looked at the age and gender distribution of our game genres. On the X-axis is the average percentage of MAU that is female and the Y-axis is average age of MAU in the genre.

Not surprisingly, Android skews male and younger. Most Android genres appeal to males under 35, suggesting there’s an opportunity for an Android game that appeals to older males. Solitaire and Slots are the only genres that have a firm middle-aged audience, with Solitaire skewing more female.

Games that monetize through IAP, such as Card/Battle, Strategy, and Action/RPG titles, are more appealing to men. Genres more appealing to women- Solitaire, Brain/Quiz- are those that are more amenable to monetizing through advertising. Of course, there are always exceptions and hit titles generally figure out how to make both men and women pay.

There’s a player for every Android game. And for every game there’s the perfect mix of IAP and advertising to make it a successful business. If the game has the potential to generate high levels of impressions- either through high frequency, retention, or both- consider some form of advertising. If the game appeals to the few, the proud, the payers- focus on IAPs. Whatever the monetization strategy, target your acquisition efforts to find the right player.

Benchmarking the Half-Life and Decay of Mobile Apps

Anonymous — Wed, 01 Apr 2015 06:24:13 +0000

Benchmarking the Half-Life and Decay of Mobile Apps

By: Mary Ellen Gordon, PhD

Anonymous Tue, 03/31/2015 - 23:24

2014-02-20

Tags

Developers devote a lot of attention to the time immediately after an app is launched. How quickly is it growing? Will it go viral? How is it ranking in app stores? While that launch period is critical, managing apps well throughout their entire lifecycle means also paying special attention to what happens after an app peaks. Does it decline precipitously or manage to hold much of its audience for a long time?

We explore the post peak phase of the lifecycle of mobile apps using the concept of a half-life, which for purposes of this analysis we define as the point at which an app’s monthly users (MAU) have declined to 50% of their lifetime peak.

We start by looking at the overall rate of decay for 26,176 apps that peaked in the first half of 2011, 2012, and 2013. (That allows enough time for decay even for apps peaking in 2013 and also controls for the time of year in which the app peaked.) The chart below shows what proportion of apps were at what percentage of their peak MAU in each of the first ten months after they peaked. Apps in the gray band fell below 25% of their peak MAU by the month indicated, apps in the orange band had fallen below 50% but were still above 25%, apps in the green band were below 75% but above 50%, and apps in the blue band were above 75% of their peak MAU in the month shown in the horizontal axis.

As shown in the chart, decay is fairly rapid in the first few months post peak. A quarter of apps fall below half of their peak MAU the month after peaking (see the top of the orange band in the chart). That percentage grows to 43% in the second month post peak. In the third month post peak, just over half (52%) of apps fall below half of their peak MAU. The fourth month post peak is a critical benchmark: From this point on, MAU decay continues, but at a much slower rate (note the flattening of the color bands after the 4th month).

The overall pattern of app user decay has been remarkably stable over the past three years, but there is variation based on app category, size, and operating system. The table below shows median app half-life for apps within each grouping. This corresponds to the month in which the top of the orange band and the bottom of the green band intersect at the 50% line in the previous chart.

The biggest influence on the rate of app decay is category. Apps in utilitarian categories such as news and health decay at a much slower rate than apps in the more trend-driven games and social categories.

On average, apps that peak at 10,000 MAU or more enjoy a half-life that is two months longer than apps that don’t reach that level.

iOS apps typically reach their half-life a month later than Android apps.

Many developers give a lot of thought to how to grow their apps, but we suspect fewer devote the same attention to managing their app’s decline. In some ways, this is analogous to people looking after their health as they age. The right habits can prolong longevity. In the case of apps, that means paying close attention to user retention and re-engagement – particularly as MAU growth begins to plateau and decline.

Those first few months post peak are key since the rate of decay levels off after about four months. More than half (56%) of apps that manage to hold more than half of their peak users for the first four months after they peak are still holding on to more than half of them ten months post peak. That could have a sizeable revenue impact. For example, consider an app that peaks at 100,000 monthly average users and has average revenue per user per month of $2.50. Holding at least half of those users through month four means the app has a 56% chance of holding them for another six months and that translates into revenue of $750,000 (100,000 users* half of them remaining *$2.50 per user per month * 6 months).

Just as actuarial tables help insurance companies anticipate costs, understanding typical app decay patterns can help developers and those who fund them anticipate app revenue streams. For example, assuming similar development costs and average revenue per user per month, a game app would need to peak with many more active users than a news app to have the same profit potential since its life can be expected to be a lot shorter. And it pays to invest in growing MAU for any app to the highest peak possible since that is also likely to mean the app will enjoy a longer half-life.

Android Speaks Spanish, But Brands Don’t Speak Android

Anonymous — Wed, 01 Apr 2015 06:04:15 +0000

Android Speaks Spanish, But Brands Don’t Speak Android

By: Mary Ellen Gordon, PhD

Anonymous Tue, 03/31/2015 - 23:04

2013-10-03

Tags

According to the U.S. Census Bureau, more than 12% of people in the U.S. speak Spanish at home, and nearly fifty million Americans are of Hispanic origin. The collective purchasing power of Hispanics is expected to reach 1.5 trillion dollars by 2015. Brands are clearly aware of the importance of this demographic group, and there are advertising agencies dedicated towardhelping brands communicate with Hispanic consumers. Nonetheless results of some recentresearch suggest that brands are not doing enough to create positive app experiences for Spanish speaking consumers in spite of the fact that Hispanics in general are enthusiastic users of smartphones.

Last week’s Advertising Research Foundation forum on the intersection between mobile and culture inspired us to investigate what the overall U.S. appscape looks like from the perspective of Spanish-speaking device users. Flurry is able to do that because we can detect what language a user’s device is set to each time they start an app session, and we record data from over a billion app sessions each day in the U.S.

We started by defining Spanish Interest apps as those for which there is at least one U.S.-based user with their device’s language set to Spanish for every twenty set to English. We used this as an indication that a non-trivial proportion of the app’s users speak Spanish, indicating some level of interest in the app among Spanish speakers. As shown below, 15% of apps with fifty or more daily users in the U.S. were in this category.

The overall percentage of Spanish Interest apps is in line with what you might expect given the prevalence of Spanish speakers in the U.S., but what’s surprising is how those apps are distributed across mobile operating systems. As shown below, nearly half of Android apps are Spanish Interest apps compared to less than 5% of iOS apps. In other words, 47% of Android apps have one or more user with their device set to Spanish for every twenty who have it set to English. That is only true of 4% of iOS apps.

It’s important to note that this may not be so much a reflection of app availability as it is a reflection of device ownership patterns since the apps a user can run are determined by their device’s operating system.

The apps that have the greatest number of U.S. users with devices set to Spanish as compared to English are mainly Spanish language apps (some originating from the U.S. and some from countries for which Spanish is the dominant language). Other than that, Spanish Interest apps span the full range of app categories but are disproportionately likely to be in the Game, Live Wallpaper, Personalization, and Photography categories in Google Play. As shown below, Spanish Interest apps are also over-represented in some iOS categories, but because the base rate on iOS is so low (4%) the absolute percentage of Spanish Interest apps is still relatively low even in categories in which they are over-represented.

By definition, the audience for Spanish Interest apps includes Spanish speakers, but we wanted to find out what else we could learn about the audience for those apps. We did that by identifying which Flurry Personas (psychographic segments) are over- and under-represented in Spanish Interest apps. We found that Pet Owners, Sports Fans, and Value Shoppers are among the over-represented Personas and Auto Enthusiasts, Home and Garden Pros, and Real Estate Followers are among the under-represented. A more complete list is provided below.

Many brands have taken a while to embrace mobile at all, and to the extent that they have they have tended to start with iOS. Some haven’t ventured any further. In some cases that is due to concerns about fragmentation or for brand safety in the more open Android ecosystem, but it is also due to the demographics of the user bases for iOS and Android. A considerable body of evidence suggests that, on average, iOS users are more affluent than Android users and they tend to spend more money in a variety of product categories.

Combine that with the tendency of Spanish Speakers to use Android devices, and it’s easy to understand why few brands have created Spanish language apps. The fact that some of thePersonas most coveted by advertisers are under-represented in the apps in which Spanish speakers are most over-represented reinforces this point.

Given the size of the Spanish speaking population, the shift of consumer attention toward mobile, and the advertising budgets following them, this situation needs to change. Our results show that if marketers want to reach Spanish speakers on mobile they are going to need to do so using Android. That creates an opportunity – or really a necessity – for Hispanic-focused advertising agencies to lead in creating high-quality brand promotion on Android. While Android users in the U.S. may be less affluent than iOS users, collectively they still have massive purchasing power, and world-wide, Android is even more dominant than it is in the U.S. Given the size of the Android audience world-wide, teaching brands to speak Android could pay big dividends well beyond those that can be generated from U.S. Spanish speakers.

Spoiler Alert: Don Draper Is Dead, And Mobile, Big Data, And RTB Killed Him

Anonymous — Wed, 01 Apr 2015 06:02:16 +0000

Spoiler Alert: Don Draper Is Dead, And Mobile, Big Data, And RTB Killed Him

By: Mary Ellen Gordon, PhD

Anonymous Tue, 03/31/2015 - 23:02

2013-09-17

Tags

Many of us at Flurry love Mad Men, but we believe that Don Draper’s advertising industry is ancient history. Don would probably mistake smartphones for cigarette cases and tablets for coasters. More importantly, sophisticated buyers and sellers in today’s advertising market are making decisions in real time based on masses of data rather than months in advance based on charm and corporate hospitality. Advertising buying is being disrupted by efficiency gains from real time bidding (RTB) and effectiveness improvements achieved by using big data to inform mobile advertising transactions.

Recently Flurry launched an RTB Marketplace that enables advertisers to bid for the attention of smartphone and tablet users one at a time. We are betting big on the trend toward programmatic buying for two reasons. First, it enables precision targeting that was unimaginable in the pre-digital age and is still uncommon. Buying ad exposures one at a time enables advertisers to reach precisely-defined audiences wherever they are and whenever they use their devices. That level of precision would always have been useful, but it is especially important now that consumer interests, preferences, and lifestyles have become so varied.

Second, RTB brings a new level of efficiency to ad buying. The whole nature of an auction means that an advertiser who is willing to pay the most to reach a certain type of person will earn the opportunity to do so. The price advertisers are willing to pay provides a clear signal of the relative value they place on a customer or potential customer.

In this post we share initial results from our Marketplace to illustrate the power of combining the price signals provided through RTB auctions with the individualized targeting capabilities made possible by big data.

The chart below illustrates three important results related to the value advertisers place on different types of mobile users and the available supply of those people’s in-app attention. The items being plotted are Flurry Personas. These are groups of devices whose owners access particular types of apps more frequently than people using other devices do.

The size of the bubble associated with each Persona represents supply, or the relative number of auctions for the right to serve an impression to a device in the Persona. Of the Personas shown here the greatest number of available impressions were for Casual and Social Gamers and the least were for Fashionistas and Food and Dining enthusiasts.

The x-axis, clearance rate, shows the percentage of auctions that had a winning bid, resulting in an advertiser displaying an ad on a device. As can be seen by looking at the right bound of the x-axis, less than half of the auctions had a winning bidder. This is a normal and expected result in RTB auctions. Reasons for auctions not clearing include price floors being set too high, bidding technologies used by advertisers’ representatives responding too slowly, some publishers being able to sell their ad inventory for higher prices elsewhere, and advertisers being uninterested in the inventory some publishers offer.

The y-axis shows the average effective cost per thousand impressions (eCPM). Even though these impressions are sold on an individual basis that is still the common pricing metric.

Examining supply, price, and clearance rate together reveals a lot about the state of play in the mobile advertising market. First, the fact that Fashionistas and Food and Dining Lovers are in the upper right corner implies that those Personas are of greatest interest to advertisers. It seems logical that those would be desirable psychographics, but the limited supply of ad inventory for those Personas also helps explain why prices and clearance rates are high. It means there are opportunities to generate mobile advertising revenue by publishing apps and content that attract Fashionistas and Food and Dining Lovers.

At the other extreme, Music Lovers and Hipsters have relatively low clearance rates and relatively low average eCPMs. While the supply of impressions for these groups within our Marketplace is not particularly large (as shown by the medium-sized bubbles), we hypothesize that people in these Personas are fairly easy to reach outside of our Marketplace because music fans spend a lot of time in music apps and many apps attract mobile-savvy Hipsters. It also makes sense that advertisers compete less aggressively for Music Lovers considering how inexpensive music is now compared to the pre-Napster era.

The overall diagonal pattern formed by the personas shows that the market is working efficiently, as expected. How do we know that? If a Persona had a high clearance rate but a low average eCPM we would expect advertisers to bid up the price to secure inventory. The fact that there are no Personas in the lower right corner shows that is exactly what has happened.

A position in the upper left corner of the chart means that auctions to advertise to that Persona have a high average eCPM given their rate of clearance. Here, we would expect publishers to drop their floor prices to achieve higher clearance rates. The fact that there are no Personas in the extreme upper left corner suggests that is also happening. There are some Personas with positions approaching that upper left corner: News and Magazine Readers are the most extreme example. We see two possible explanations for why those publishers didn’t drop their floors in search of higher clearance rates. One is that some of those publishers are able to sell impressions that don’t sell through our Marketplace direct or to use them themselves (i.e., to promote their own properties). The other is a policy of keeping prices above a certain level to maintain a premium image even if it means sacrificing short-term revenue opportunities.

RTB moves at lightning speed. A publisher can shop a single impression in the nanosecond before the winning ad appears. Compare that to the speculative, mass-market approach of the Upfronts, and it’s easy to see that advertising buying is likely to be completely disrupted by RTB.

The Persona-based targeting described in this post demonstrates the power of data to inform each bid. Advertisers no longer need to make buying decisions based on stereotypes about which types of people are interested in what type of products or content. They can define their target audience precisely, and aggregate that exact audience efficiently impression by impression. Mobile also contributes to this type of precision targeting since smartphones are highly personal devices loaded with apps that reveal much more about the person looking at the screen than standard demographics ever did. The long held promise of digital advertising is finally being realized on mobile.

All of this leads us to believe that advertisers or publishers who want to do things in the old way may be better off kicking back, pouring themselves a drink, and watching an episode of Mad Men instead of entering the fray in the mobile advertising space where data-fueled RTB is sure to win.

Upper Middle Class, Females Key to Bridging Mobile Ad Spending Gap

Anonymous — Wed, 11 Mar 2015 23:01:21 +0000

Upper Middle Class, Females Key to Bridging Mobile Ad Spending Gap

By Peter Farago, VP Marketing

Anonymous Wed, 03/11/2015 - 16:01

2012-02-20

Tags

Smartphones and tablets continue to break new consumer technology adoption records. From earlier research, Flurry found that iOS and Android smart devices have experienced twice the uptake rate compared to that of Internet adoption, and four times the rate compared to that of PC adoption. Following this unprecedented adoption, advertising dollars are beginning to flow into mobile. A recent IAB study reported that 63% of top brand marketers have increased their mobile advertising spending over the last two years, and that 72% plan to increase advertising spending over the next two years.

Focused on mobile advertising, this report has two parts. First, Flurry compares the allocation of advertising spending across various media versus the actual time consumers spend across those same media. Next, through detailed demographic breakdowns, we share which audience segments are best responding to mobile advertising. Let’s start by understanding trends in media usage versus ad budget allocation.

In the above chart, Flurry aggregated publicly available data from VSS and Mary Meeker (KPCB), then layered in its own analysis to reflect the growth in app usage we observe. With our adjustment, we resized the totals for U.S. advertising spending by media and consumer time spent using each media. From left to right, represented by the green columns, is the proportion of advertising spending across each major media. TV and Print command the greatest advertising spends in the U.S. with 43% and 29% of the total, respectively. Web, Radio and Mobile channels round out the balance of media spending with 16%, 11% and 1%, respectively. Adjacent to the ad spending columns is the amount of time consumers spend by media type, represented by blue columns. TV leads consumption with 40%, followed by Mobile and the Web with 23% and 22%, respectively. Radio and Print complete the picture with 9% and 6% of usage, respectively.

Comparing where usage and spending vary most, one notes severe over-spending in print advertising and even more severe under-spending in mobile. Web usage also shows sizable under-investment, relative to platform usage, though not as dramatically as seen on mobile. In short, despite the fact that mobile advertising is growing, the platform is far from getting rational levels of spending compared to other media.

We believe the main reason for this disparity is that the mobile app platform has emerged so rapidly over such a short period of time. With the iOS and Android app economy only three-and-half years old, Madison Avenue and brands have yet to adjust to an unprecedented adoption of apps by consumers. Further, the mobile advertising ecosystem remains nascent, without sophisticated platform tools. Concepts of audience measurement and segmentation on mobile are still forming, and mobile lacks the kinds of systems that agencies take for granted on the web. For instance, mobile inventory is difficult to buy in volume, ad networks have yet to be integrated into Demand-Side Platforms (DSPs) and common standards for ad serving, tracking and settlement are yet to be defined. Just consider that large publisher properties like Facebook have yet to monetize their mobile properties, with many still needing to hire media sales organizations to position themselves to do so. As the mobile platform matures, and these problems are addressed, mobile advertising is poised to take off in earnest.

For the second part of our analysis, we measure which audience segments respond best to mobile advertising, leveraging data from our own ad network, AppCircle, as well as publicly available data. Taking a sample of 60,000 daily active users on iOS, from among a total group of 6 million for whom we have demographic data, we calculated the effective cost per mille (“thousand” in Latin), or eCPM, earned by publishers. Using eCPM allows us to consider both branding (e.g., CPM) and performance (e.g., CPC and CPA) advertising campaigns in our calculations to get an accurate read on which mobile audiences monetize best. In the following charts, we display eCPMs by age and gender, household income and educational level attained. The higher the eCPM earned by audience attribute, the more valuable the audience is to both advertisers, who pay top dollar to reach this audience, and publishers, who earn the most revenue for selling access to this audience. Let’s start with audience breakdown by age and gender.

The chart above shows the value of mobile application segments by age and gender. Males are shown in green and females are shown in blue. The value above each respective column is the eCPM earned by that segment. For example, 25 – 34 year old females fetch the highest eCPMs at around $13, driven by underlying high click-through and conversions rates. In fact, females are the more desirable target audience across most age breaks, tied with men in the 18 – 24 year old age range, and exceeding them at 25 and older.

Breaking out eCPMs by household income shows that income ranges from $60,000 to $100,000 are the most valuable, with $100,000 to $150,000 also performing very well. For mobile advertising, there appears a strong correlation between affluence and eCPMs. This squares withearlier analysis from Flurry that found households with iOS and Android smartphones are, on average, 50% more affluent ($44,000 average U.S. household income vs. $66,000 average U.S. smartphone household income). Smart device owners are, on average, more affluent and more educated.

Similar to household income, we find that those who attained higher levels of education are more valuable segments in terms of eCPM generation. Those with a bachelor degree fetch the highest eCPMs, close to $8.00. The second most valuable segment are those even more educated, having earned a master degree or higher.

As a total snapshot, our analysis shows that females and males, between the ages of 25 and 34 years old, who have higher levels of disposable income and a bachelors degree or higher, more strongly interact with mobile ads. Leading sociologists William Thompson and Joseph Hickey define this class as “the rich” or “upper middle class,” comprised of highly educated salaried professionals whose work is largely self-directed. Typical professions for this class include lawyers, physicians, dentists, engineers, accountants, professors, architects, economists and political scientists.

What bodes best for the outlook of mobile advertising is the quality and quantity of the audience that not only uses smartphones and tablets, but also interacts with ads on these devices. Based on our analysis, revealing that the most sought after segments already interact most with mobile ads, a key ingredient required to realize the promise of mobile advertising is the introduction of mobile ad platforms that can segment publisher audiences and enable targeting by advertisers to reach segments of their choice. Like online, which is infinitely more measurable than Print, Radio and TV, mobile advertising is poised to grow radically with the introduction of scalable, data-driven solutions that put advertisers and publishers in control of their own destiny. Actionable data and well-built platforms are the keys to unlocking Madison Avenue spending.

The Awesome Potential of iPhone In-App Purchases

Anonymous — Wed, 11 Mar 2015 18:31:16 +0000

The Awesome Potential of iPhone In-App Purchases

By Peter Farago, VP Marketing

Anonymous Wed, 03/11/2015 - 11:31

2009-06-11

Tags

The ability for developers to offer in-app-purchases within paid iPhone apps, as part of iPhone OS 3.0, creates exciting new revenue opportunities. At the same time, the option to sell virtual goods, additional game levels, subscriptions and other forms of micro-transactions, creates more complexity around how to best monetize a given application. Developers who can quickly and effectively measure and optimize the impact of these new pricing options will emerge as winners in the next phase of the iPhone economy.

To date, selling an iPhone application required a few simple decisions: developers could either give an app away for free or charge for it. The two most common business models to emerge were free-to-paid and ad-supported, with some companies opting to ship only paid version of their applications. The decision to offer a free version seems correlated to whether a developer has recognizable brands. For example, EA Mobile, which boasts The Sims 3, Tetris and Scrabble tends to release more paid-only versions. By contrast, companies with more original, less-recognizable titles like Digital Chocolate, which makes Crazy Penguin Catapult, Brick Breaker Revolution and Tower Bloxx frequently go to market with free trials of their games to entice consumers to try-and-buy. Overall, much of the learning in the market has centered on what price to charge, when to drop price and whether ad-supported apps earn more revenue than paid apps.

Already, there are several iPhone apps well suited to micro-transactions. To stay relevant, well ranked, and retain consumers, developers have been adding extra content and features via updates. Pocket God by Bolt Creative* is an example of an app that has strong micro-transaction potential. They have already successfully trained users to expect regularly released content updates that keep the gaming experience fresh (personally, I like the spear used to fight off the Tyrannosaurus Rex). As a result, Pocket God has been ranked among the top paid apps for several weeks. However, they have been collecting a mere $0.99 for the initial download of the app and then giving away a steady stream of additional content after the sale. While their current strategy has earned them users, they should weigh this approach against maximizing revenue through micro-transactions. A risk to keep in mind is that users who have been receiving content updates for free may resent paying for updates going forward. This could be mitigated with a combination of free updates and optional in-app purchases.

To further this example, listed below are some ways Bolt Creative could consider applying micro-transactions:

Experienced publishers and developers will tell you that testing and measuring is the best way to focus on the right parts of your business, especially when it comes to your product and how you price it. As it relates to micro-transactions in the App Store, think about the content you are offering and whether it’s well suited to micro-transactions. Then test launch different kinds and amounts of content, at different price points, from within different points of your app and at different intervals. Compare how these perform using your analytics service of choice and tweak your approach. With this kind of testing, learning and tuning, you’ll be reaping the rewards in the next era of the impressive iPhone economy.

* Bolt Creative is not a Flurry customer and the business model options explored in this blog post are for illustrative purposes only.

Free iPhone Apps: How Much Should Developers Give Away?

Anonymous — Wed, 11 Mar 2015 18:27:36 +0000

Free iPhone Apps: How Much Should Developers Give Away?

By Peter Farago, VP Marketing

Anonymous Wed, 03/11/2015 - 11:27

2009-04-30

Tags

An age-old question in any demo or trial program is how much of the product to give away in order to maximize sales. With Free and Paid sections, Apple has designed the iPhone App Store to easily facilitate this classic go-to-market strategy, and we’ve found that iPhone App free trial strategies are effective.

In the world of mobile applications, the question of how much to give away is actually a relatively new challenge since carriers, for the most part, did not support free trials on their decks. Previously, mobile developers dealt with the occasional issue of defining trial length only when they won a coveted embed (aka pre-load) deal on an OEM’s handset. With so many new developers throwing their hat into the iPhone App Store ring, there is little collective experience around this topic. Additionally, mobile application analytics solutions, like Flurry Analytics, did not exist. With Flurry Analytics, the guess work can be completely removed and developers can measure with precision the optimal up-sell point in their trial application.

Before we review mobile developers’ choices around “free” iPhone App store offerings, we want to point out that Apple requires the amount of a free game or app to be a full, stand-alone experience. For example, developers cannot “gray out” menu items that would appear in the paid application if the consumer were to purchase it. Additionally, since it’s well documented that few ad supported applications generate meaningful revenue, exceptions notwithstanding, we’ll suspend this topic for this blog post.

The decision of how much of a free game or application experience to give away begins with understanding the mechanics of the basic equation: Revenue = (Number of Free Players) x (Free to Paid Conversion %) x (Price per Unit). If a developer gives away too much of an experience, it inadvertently satisfies the consumer so that upgrading to the paid version does not seem necessary. However, by giving away a lot of value, the application may become more popular as a result, garnering strong community review scores and increasing the free version installed base. Having a large installed base means that a smaller percentage conversion is needed to make good money. Generally, we recommend a balance with a focus on optimizing conversion.

In other words, we recommend giving enough of the game or application away so the consumer understands the value, but still understands that if they buy the full version, there is a lot more value to be had. Overall, don’t be afraid to cut off a consumer if he isn’t willing to pay. We subscribe to the philosophy that developers should be focused on finding consumers who are willing to pay, not trying to completely satisfy free-rider consumers.

Below is an example of how one game developer tracked user progression in its free trial game. While this free version shipped with ten levels, analytics showed that most users failed to progress beyond level five of the free game. With the up-sell message shown at the end of the demo, describing additional features the consumer would get when purchasing the full version, many consumers did not see this. By cutting the free version in half, holding the same conversion rate, we estimate this developer could increase its sales by approximately 40%.

Traditionally, the process of deciding how much of a game or application to give away has been more art than science. While the length of a demo experience should vary from app to app, leveraging analytics data allows developers to test, track and tune their free trial strategy to drive maximum conversion rates and revenue.

iPhone App Store Marketing: Give it Away to Get Paid

Anonymous — Wed, 11 Mar 2015 18:25:29 +0000

iPhone App Store Marketing: Give it Away to Get Paid

By Peter Farago, VP Marketing

Anonymous Wed, 03/11/2015 - 11:25

2009-04-10

Tags

Among your strongest marketing plays in the App Store is to offer a free trial of your game or application. Not only is the App Store designed for this, but also it’s the best way to reduce consumer risk in trying your application, with the goal of eventually getting that user to purchase the full version. Think: money. And from our data, it’s among the most effective moves you can make. Here’s a motivational example using customer data collected using Flurry’s mobile app analytics service in their iPhone apps.

In particular this strategy can favor non-branded applications. For example, instead of simply purchasing a familiar and “safe” game like Tetris, Bejeweled or Pac-Man, a consumer can explore and try your innovative, unknown game for free to decide whether or not to purchase. Simply put, free trial has leveled the playing field for independent developers who previously struggled to get consumers to even give them a try. Additionally, it rewards more established companies who innovate and leverage capabilities of the iPhone hardware to wow consumers - things like touch, the accelerometer, contact list integration for invites, and more.

To further motivate you to seriously consider a comprehensive free trial strategy, we took a look beyond the very hot and sexy iPhone App Store, observing that all major new mobile store entrants will also offer a Free Apps section. Check out a very good side-by-side app store comparison courtesy of Gizmodo that compares the iPhone App Store, Android Market, BlackBerry App World, Windows Mobile Marketplace, Palm App Catalog and Nokia Ovi Store. What this tells us is that a free trial strategy is a must-have going forward in all viable launch strategies. One could argue that uber-brands are an exception to this rule since their consumers are probably willing to buy an application or game without first trying it. However, even if you’re the proud steward of such an uber-brand, remember that free trials drive discovery your title, helping increase the adoption of your paid version and ranking in the Paid App category. And don’t forget that all the previously disadvantaged Indies now have a shot to take away you consumer with their free trial.

In an upcoming blog entry, I’ll touch on how to track and tune your free-to-paid program from a product experience standpoint to maximize your conversion, and therefore revenue generated. Also, there are some Apple policy issues you need to be aware of when designing your free experience - nothing too crazy, but that can result in failing the Apple approval process. Stay tuned.

Cross-sell or Die: Get More Sales in the iPhone App Store

Anonymous — Wed, 11 Mar 2015 18:24:09 +0000

Cross-sell or Die: Get More Sales in the iPhone App Store

By Peter Farago, VP Marketing

Anonymous Wed, 03/11/2015 - 11:24

2009-03-30

Tags

The glut of applications in the App Store has made application discovery a top concern among companies releasing iPhone games and apps. Last week, 148 Apps reported that more 30,000games and applications are available in the store, already 5,000 more than the 25,000 announced by Apple when it previewed its iPhone OS 3.0 software on March 17.

With rampant competition, companies must leverage every customer contact point to increase sales. This is where cross-selling can help. Cross-selling targets a company’s existing consumers to sell them additional products. On the iPhone, the best opportunity is from within a downloaded application, usually with a link to other games or applications included on the menu screen.

While cross-selling theoretically has been around since the beginning of business, it has become far more effective since the advent of e-commerce on the Internet. In addition to allowing a consumer to quickly and easily complete a follow-on purchase, it can be tracked, measured and tuned for maximum impact.

Since cross-selling is such a classic marketing tool, not to mention easy to execute on the iPhone, we were surprised to observe several developers either not doing so, or treating it as a rushed after thought. So we took a look into our data set to ask: how well does cross-selling work on the iPhone?

The short answer is that it can be highly effective, and the following example demonstrates just how effective. After three weeks of strong sales in the App Store, sales began to decline for Company X’s first application. When the second application was released, it included a strong call-to-action to purchase the first application. As the graph below shows, strong sales of the second application, along with solid cross-sell conversion, reversed declining sales of the first application.

It is worth noting that these two applications benefitted from sharing a similar target audience to which both products appealed, and that the efficacy of cross-selling efforts can vary. However, whether your application can achieve a similar lift from cross-selling is something you won’t know until you test and measure it for yourself. As all markets mature - and the iPhone App Store has matured in record time - it is important to think strategically about growing your business by maximizing every precious consumer point of contact. Cross-selling remains among the most effective marketing tools.

Can Developers Still Make Money in the iPhone App Store?

Anonymous — Wed, 11 Mar 2015 18:21:02 +0000

Can Developers Still Make Money in the iPhone App Store?

By Simon Khalaf, CEO

Anonymous Wed, 03/11/2015 - 11:21

2009-03-20

Tags