We already wrote here a few times about eCPM decay and some of our tips were also quoted in other places. In this post we are going to talk about another type of eCPM decay – the one that is rarely mentioned. I’m talking about the trend of eCPM going down as a function of how long the user was retained in the game as opposed to the decay that happens as a result of high frequency of ads during the day.

First Test – How eCPM behaves over the life of a user

In this test we looked for users who started playing the game in a certain month and than checked their eCPM in that same month versus the eCPM in the following month and the third month. We did this test across many games to make sure the results are not isolated to a single game. In this chart below you can see the average values, the maximal ones (the game with the highest rate in that month) and the minimal ones (the game with the lowest rate in that month) across all the games we tested. Note that this test was done only with US based users and only in the following ad formats: Offer walls, Rewarded Videos and Interstitials.

There is clearly a trend here. The eCPM is going down the longer the user is retained in the game. In fact, new users can have 2x or more the eCPM of loyal users. We can attempt to explain this finding of course. One assumption is that the same behavior pattern that impacts eCPM decay also comes into play here. Users tend to grow tired of advertising. However, here the situation is a bit different. Consider the case of a user who downloaded a new game this month but might also downloaded another game 3 months ago. It’s the same user so why is he responding better to ads in the new game he downloaded vs. the older game? The answer could be that the user gets tired of ads in a given context seperately. He might learn where the ads are placed and his brain is getting trained better to ignore them. It will be interesting to see what happens if we mix up the ad placements for loyal users to see if we can engage them with the ads again.

Second Test – Does it matter where the user came from?

Here, we tried to see if a user that came organically behaves differently compared to a user that came through paid UA or cross promotion. We compared only for US based users – here is what we found.

So it looks like the Cross-promo traffic had very high eCPMs in the first month. Paid installs that came from Facebook also appeared higher than Organic. However, the drastic difference in the eCPM of the usres in the 1st month almost vanished when looking at the the 2nd and 3rd month. Specifically, the cross-promo installs were lower compared to organic installs in the 3rd month. In general, the eCPMs converge to the same levels almost. It seems that the impact of the source of the user only lasts for the first month and after that month the user ‘forgets’ where he came from and users behave in a similar fashion. It’s possible that users who came from an ad into your game are more likely to respond to ads in your game. The fact that the impact only lasts for 1 month could potentially be explained by users response to ads is a temporary behavior and not a long lasting behavior pattern.

Third Test – Do we see the same trend across all ad formats?

We wanted to see if all ad formats behave the same way when it comes to this type of eCPM decay. Do users lose their interest in rewarded videos the same way as they do with interstitials? We compared 3 ad formats and this time we compared not just US traffic but we allowed international traffic. To make it easier to follow we indexed the results so they all fit in the same scale.

It’s easy to notice that the findings are consistent across all ad formats we tested. We didn’t check banners and native ads in this study. It’s possible we will do another post specifically focused on that.

Optimizing for the long retained users

One conclusion from this data is that there should be opportunities to better serve ads for loyal users so they monetize better. Here are some ideas to consider specifically for this segment:

• Serving ads through SSPs – these ads come with an upfront bid price and are less influenced by users’ ad engagement
• Closing fixed CPM deals for this segment
• Mixing it up – changing the placements for user who have been playing the game long enough

The impact on LTV calculations

These findings might also impact how companies think about LTV prediction. Many LTV models assume that eCPMs and ARPDAU are not influenced by the amount of time the user played the game. If your existing model is predicting LTV based on the 1st month’S eCPM the actual result might be worse than the predicted LTV.

What about Apps

While the reseacrch was focused on games only we expect that to find the same patterns in Apps. At least that is true for the formats we checked: Rewarded videos, Offer walls and Interstitials.

One of the areas in which a company can drastically increase ad revenues with a relatively low effort is through the optimization of opt-in ratios to rewarded videos. Here we will show how to track and optimize opt-in rates with SOOMLA TRACEBACK.

What is opt in rate and why does it matter

Rewarded video is a unique format in the sense that it’s not forced on users. The game is offering some reward in return for watching a video and the user can accept the offer or not. These offers can be made with a pop up message, a button on different screen in the app or sometimes by replacing a call to action that would normally be prompting the users to pay. Regardless of the offer method, the users can accept or decline. The number of users who decide to take the offer is often called engaged users and the dividing them by the total number of active users is considered the opt-in rate.
One thing that is clear is that users who don’t engage with the rewarded video don’t contribute any revenue so by increasing the opt-in rate we are making the pool of monetizing users bigger. It is also known that users monetize best in their first impression and so getting more users to opt-in means you are getting a lot more of those valuable 1st impressions. Our experience has shown that increasing the opt-in ratio by x% often translates to a similar increase in the total ad-revenue.

Measuring the opt in with SOOMLA TRACEBACK

One of the easiest ways to measure the opt in rate is to use the TRACEBACK platform. You can see your overall opt in rates and number of engaged users but you can also look at specific segments and breakdowns across these dimensions:

• Countries
• Platform/OS
• Versions of your app
• Traffic sources
• Date ranges

Looking at specific segments allows you to find improvement opportunities. The way to spot these is simple – a low opt in rate means there is a room to grow it.

What is a good opt in rate

Depending on your game of course and how well you are doing with IAP monetization you can reach as high as 80% opt in rate according to this study by Unity Ads. However, apps that focus more on IAP would be smart to first convert the users into payers and only then try to push them harder to videos. From this reason we should look at the opt in rate on a cohorted basis and set different goals depending on the lifetime of the users. These are good benchmarks:

• 1st month – 20%
• 2nd month – 50%
• 3rd month – 60%

Optimizing opt in rates

Once you have identified a segment that falls below the target opt in rate, you can use TRACEBACK to optimizie it. One way to do it is to track the opt in rate in different versions of your app. Whenever you launch a new version you can immediatly compare the opt in ratio to the one of the previous version and check if you are moving towards the goal or away from it.

Example – Optimizations results in higher opt in rate in later versions 

Here is an export from our dashboard into Excel showing the opt-in rates in different version of the app.

While comparing opt in rates between different versions is easy to do and comes as a built in feature of the platform, a better approach would be to use TRACEBACK alongside an a/b testing tool. This allows customers to compare between different versions and configurations simulatnously in a randomized testing environment. TRACEBACK will present the opt in rate for each testing group in the dashboard so you can easily compare and pick the winning configuration.

Last Thursday, the mobile ad-tech space was surprised to hear the news about Oracle buying mobile viewability company MOAT for 850 million dollars ($850M).  This reflects a very high multiple on revenue and a very nice return for the investors that put in an aggregated amount of $67.5M – most of it in 2016. If you have been mostly involved in mobile apps – you might have not heard about mobile viewabilty at all and are wondering how can such an unknown aspect of mobile advertising can be worth so much money to someone. To understand this – we need to dig a bit deeper.

Over 95% of the impressions are ads for installing other apps

In a recent blog post we announced a new and super exciting feature that was recently added to the Traceback platform. At SOOMLA, we are all about giving visibility to the publishers to know more about their in-app ads so knowing who is advertising in your app is a big part of that.

One of the things we quickly realized when starting to look at this data is that most of the advertising activity is driven by demand coming from other apps. These are typically CPI campaigns that only pay when the user installed the advertised app. The surprising part is that the number of ads coming from brand advertisers is very low. In the last 2 years we have seen a growing number of indications that the brands are coming to mobile.

In this link you can find a report from eMarketer breaking down mobile ad spend by category. In the image to the right you can see that while retial ad spend might have a mix of brand and app install campaigns, the following categories are dominated by brand ads:

• Financial services (Capital One, Geiko) – $3.49B
• Automative – $3.43B
• CPG (Procter and Gamble) – $2.33B

With at least 10 billion dollars ($10B) being spent on brand campaigns, we would have expected more of these ads to show up in mobile apps. We are not alone in our expectation of course, in 2014 Eric Seufert wrote:

“If the largest brand advertisers shift just 10% of their overall budgets to mobile, they’ll match or exceed the money spent by the app economy’s behemoths – and they’ll be competing for the same ad inventory.”

Well, according to the eMarketer report, almost 50% of the digital ad spend of these brands have shifted to mobile and yet these ads are no where to be seen. Eric was not wrong to expect a change but we all missed one important thing – the ‘other’ mobile industry.

Mobile web and mobile apps – two separate ecosystems

From a user perspective, mobile is a single experience. Opening the URL www.weather.com or opening the weather app will result in a very similar experience. The technological aspects are very different between mobile web and mobile app and each one has a separate eco-system when it comes to mobile advertising with very little overlap in between. The mobile web ecosystem was pretty much inherited from the desktop space. Each desktop advertising player in the ecosystem gradually started adding mobile web support so eventually the mobile web and desktop web ecosystems operate in a very similar way. The mobile app ecosystem evolved mainly from the need of gaming apps to acquire massive amounts of users. This resulted in an install focused industry with a great focus on attribution – a concept that mobile web companies haven’t even heard about.

What is viewability and how it evolved

In 2013, early reports started coming in showing that when brands are paying for users to watch their ads, they are often not getting what they paid for – nearly half of ads are not seen according to Comscore report from 2013. In 2014, a report from Google claimed it’s actually 56% of ads that are not viewable. This made brands worried about buying display ad inventory online and gave a big push to a category called viewability measurement. Here are some of the problems causing low viewability rate:

• Ads can be shown In a window that is in the background and hidden by another window
• Users sometime scroll away from the part of the page where the ad was shown
• Time on screen is to short or video playback was stopped early
• Traffic generated by bots rather than humans

MOAT has put it together nicely in this web image:

The need for a solution forced the digital advertising industry to act together and set official guidelines. The Internet Advertising Bureau (IAB) together with the Media Rating Council (MRC) has published a first set of guidelines in June 30th 2014 which later evolved into version 2.0 of the guildelines in August 18th 2015.

Viewability in mobile web

As mentioned before, the mobile web ecosystem is a replica of the desktop web ecosystem in terms of advertising at least. Viewability measurement quickly became an issue in this industry as well and in June 2016, MRC published their mobile guidelines for viewability measurement. These guidelines are for mobile apps but we will touch on that later on. The main guidelines in the publication are these:

• Client side measurement
• Measurement of offline activity for apps
• Filtering non-human ad views
• Differentiate viewing from pre-rendering and pre-fetching
• Detect when ads are out of focus
• Pixel requirement: at least 50% of pixels were on screen
• Time requirement: 1 continuous second of a post-rendered ad that meets the pixel requirement

The MRC is also accrediting viewability measurement companies who meet the criteria based on an annual audit.

Who can measure mobile viewability

The top 3 providers for desktop viewability measurement have all been accredited for mobile viewability measurement. Here they are:

• MOAT (recently sold to Oracle for $850M)
• Integral Ad Science (IAS)
• Double Verify (DV)

These companies cater for both advertisers as well as publishers.
Brand advertisers are willing to pay for viewability measurement to know that their ads are getting viewed. They would typically deduct the non-viewable ads from the media campaign when calculating delivery and payout. However, most brands would also consider publishers with low viewability scores as non-safe and illegitimate media. It is common for brands to have a viewability threshold of 90% or 95% for publishers. This means that publishers with a low score will simply not get any brand ads.

On the other side, publishers also have an incentive to have their sites and apps measured. The basic reason is that if you don’t allow measurement you will not receive brand ads. On top of that, publishers who sell directly to brands or ad-networks who represent them want to know the metrics and data points about their media so they can use it in their pitching.

Mobile apps have been a slow adopter of viewability

When you check how many app publishers have a viewability measurement SDK installed you discover that most of them don’t. We can look at the 200 most downloaded apps (top free chart) on both iOS (Top chart link) and Android (Top chart link). These are typically the apps who will have advertising in them as they attract a lot of users.

• On iOS – only 5 out of the top 200 have viewability SDK – 2.5%
• On Android – only 21 out of the top 200 have viewability SDK – 10.5%

Our assumption is that the more you advance towards the long tail the less likely apps will have a viewability SDK since the long tail apps are still focusing on more basic aspects.

SDK fatigue slowing down adoption and enticing provider collaboration

One of the problems slowing down the adoption of viewability measurement in the app ecosystem is the need to integrate an SDK. To make things even worse, an app would need to integrate the SDKs of all 3 providers (MOAT, IAS and DV) to enjoy the full benefits. A recent open source initiative is aiming to solve at least part of the problem. It started out of IAS but was later handed over to the IAB to manage the project. The 3 providers have agreed to collaborate and support the single open source SDK that will make things a bit easier on app-publishers.
At the same time, some of the ad-networks have increased their interest level in viewability measurement as a way to be more attractive to brand campaigns. Some of them are working on bundling the viewability SDKs inside their SDKs so that their entire inventory will become available to brand campaigns. That however, brings another risk. Most apps user multiple ad-networks so if many ad-networks follow the same path an app could carry a number of viewability SDKs at the same time.

Viewability might not be enough

It’s clear that brands would not advertise in mobile apps without having viewability measurement. If you haven’t heard about the P&G $2.8B Ultimatum to the media industry – you can read about it here. However, it’s not clear if having viewability measurement is sufficient to make brands start advertising in mobile apps. The condition might be necessary but not sufficient. In other words, there could be other road blocks for brands to advertise in mobile apps. Here are some other reasons why brands could be staying away from mobile apps:

• 50% of mobile app traffic is in games and brands have shied away from games historically
• Brands often have issues with incentivized advertising and rewarded video falls into the category
• The lack of audience data and tools for advertisers to target specific segments
• Today brands are separated from mobile apps by multiple hops which takes a cut and reduces the eCPMs for the publisher so his mediation provider might not allow them to show

Oracle might be the last one laughing

So if you think of the projections for mobile viewaiblity. MOAT is already positioned as the one of the top 3 and some say the leader of mobile viewability in a market that is expected to double itself in 3 years. Considering that mobile viewability hasn’t even made it into mobile apps – the market can double in size again when Apps realize that they can get the brands competing for their inventory as well. So if MOAT can still grow 4x in 3 years, maybe the price Oracle paid is not that high after all.

For a while now the business of advertising apps have zoned in to one standard business model – cost per install (or CPI). The CPI business model has pros and cons like any other business model but it proves less fraudulent than clicks, less risky for the advertiser than CPM and more uniform compared to more advanced CPA models. With that, measuring installs and deciding which sources they came from became a highly significant part of any marketing campaign. For the advertisers, it’s not only about fair business models but also about measuring marketing effectiveness. Attribution of installs to a sources, is a key ingredient in understanding which marketing activities and campaigns are the profitable ones. For the ad-networks, being able to receive the post-backs and post-install conversion data is an important feedback loop that allows them to optimize their campaigns and maximizing yield as well as returns for the advertisers.

3rd party attribution vs. in-house

There are a few companies that tried building attribution solutions in-house. Sure, the technology is quite complex but app companies are technologically capable. Especially the gaming companies tend to prefer in-house infrastructure when it comes to analytics. The ones that did figure out how to build their own attribution solution soon realized that there is not much use for it. The most prevalent business model between app advertisers and ad-networks is CPI. Since advertisers pay the ad-networks based on installs – both sides needs to agree about the number of installs so when the advertisers build their own attribution, they soon realize that many ad-networks are not willing to trust it’s measurements. Instead, they request that a 3rd party partner will be in charge of keeping the score.

Facebook’s attempt to become a player

In 2015, in light of the growing market, mobile advertising giant, Facebook also made an attempt to become a player in this market and own a bigger piece of the end-to-end marketing solution. The market, however, rejected the move. Publisher demanded that a 3rd party will be in charge of the measurement.

In the picture (credit: Venture Beat): the battle of the attribution providers panel at Casual Connect where the only agreed upon topic was the Facebook move legitimacy (or lack of).

How did this market get so big?

The 4 big attribution providers track about 18 billion dollars in ad spend worldwide. This is based on various points of information showing that Kochava measured 3 billion dollars in 2015 and Appsflyer measured 6 billion dollars in 2016. We can estimate based on the pricing of the providers that the attribution fees reached about 1% of that – 180 million dollars in 2016. The market is already big and is expected to double it’s size by 2020 reaching over $360M. Some of the driving forces behind this growth are the ad-networks themselves. Each attribution provider supports hundreds of networks and each ad-network has a lot of people in the field, meeting customers on a daily basis. Thousands of people going to every show, attending every conference and talking about CPI campaigns. Once ad-networks agreed that 3rd party attribution is a must-have, it was only a matter of time before the customer accepted that.

Attribution methods and approaches

Since the early days of attribution, there have been people saying that the models are not accurate. We live in a world where each user is being exposed to dozens of advertisements every day, often across different mediums. By the time a conversion, an app install, has occurred, the user is likely to have seen an ad for that app more than once and sometimes even as much as 10 times. Despite that, attribution models always give credit to a single source, single campaign and single ad-group. The credit is always given to the last click if such a click happened recently and if there is no click, it will be given to the last impression if it happened recently. It’s always the last one. Critics say rightfully that this is an inaccurate way to do it and there are better models. However, this is one of the areas where the delicate balance between ad-networks and advertisers mandates a simple model that is easily decide and so last-click attribution, while not perfect, is the best we have and will stay the attribution model going forward.

The evolution of click fraud detection and IAP measurement

Being a key ingredient for measuring marketing returns or ROAS (return on ad spend). The attribution providers got pulled into providing more analytical services. The attribution dashboard became the go to screen for understanding install volumes from different sources. As the industry became more aware of the issues around traffic quality, attribution providers got pulled in to provide additional reports.
One direction in which attribution providers evolved is the area of post-install metrics such as retention tracking and conversion to payers via in-app purchases. Publishers also started using post install metrics reported by the attribution providers to set goals for user acquisition channels. It’s common these days to see an IO (Insertion Order) with traffic quality criteria. Beyond measuring the in-app purchases, attribution providers also developed the ability to distinguish real purchases from fake ones.
The other direction in which attribution developers were asked to evolve is install fraud detection and prevention. While generating fraudulent installs is much harder than clicks, publishers have a lot of motivation to do so and methods were developed to manufacture installs. All big 4 attribution providers today have developed mechanisms to detect such activity and remove it from the reports. By doing so, they are providing more value to the advertiser who only pays for real installs.

The top 4 mobile attribution providers

Appsflyer is the most VC backed provider out of the bunch. They hold an impressive share of the top 200 companies and have the biggest penetration in far east countries. They are based in Hertzelia, Israel with offices all around the world. Their unique pricing model allows app companies to start for free which makes them highly attractive for smaller companies. Appsflyer started in the Microsoft Accelerator and is still rumored to be using Microsoft cloud infrastructure – Azure.

The Berlin based provider has made itself a name in quite a short time. They are an official measurement provider for Facebook and pride themselves as being the leader among the top 200 apps. Adjust is leveraging their own private cloud infrastructure rather than a hosted one which also allows them to be the best at protecting user privacy.

Tune is a popular attribution choice. The company had two products going by separate brand: Has Offers and Mobile App Tracking. They are the only one of the four that is not focused completely on attribution and are also not an official Facebook measurement partner. To overcome this problem, Tune provides a solution for Facebook attribution leveraging deep linking.

Kochava is the only bootstrapped provider out of the top 4. They made themselves a name by attracting top tier media companies such as ABC, CBS and Disney as well as the mobile gaming giant – MZ. The company recently launched a new product called Kochava Collective to help their customers reach relevant audiences and also started offering a free version of their platform under the name – Free App Analytics

Mid market solutions with an attribution feature

The 4 top providers mentioned above are highly focused on attribution and cater to customers who often build their in-house analytics and use the API offered by these providers to pull the data into their own BI or data warehouse system. However, there is a group of companies below the top tier that are relying on 3rd party analytical tools rather than a tool that is built in-house. These companies are preferring a full-solution approach that includes both analytics as well as attribution. This opportunity is considered the mid market of the mobile attribution space and there are companies who are starting to cater for it. Here are 2 providers in this category:

• Tenjin – provides analytics solution with built in attribution
• Apsalar – provides analytics, attribution and audience building under the same roof

The ability of companies to simply add attribution as a feature on top of something else is a result of the maturity of the market. The connections with the ad-networks became standardized and there are now also tools to attribute Facebook traffic with no need to get their official blessing. This is the reason why we also see companies like Singular adding attribution into their marketing and cost aggregation platform.

It’s important to note however, that these solutions are often lacking in terms of how many ad-networks accept them as an authority to counting installs. One of the key features of the attribution providers is the ability to serve as an unbiased 3rd party that is accepted by both sides when it comes to discrepancies in install counts that impact how much is paid by the advertiser to the ad-network.

Free attribution – what are the options and what’s the catch

Publishers might be surprised sometimes by the fees for the attribution service. Take Appsflyer for example – they recently tripled their price from $0.01 per install to $0.03 per install. Furthermore, if a certain app has a low volume of installs, the price is even higher. Tracking 100,000 installs for example will cost $4,000 per month. Quite a high fee for apps that are just starting out. This is part of the reason why many publishers are looking for alternative free solutions.

Option 1 – Branch Metrics deep linking

Branch offers a free deep linking solution. This means that every time an ad is shown a savvy marketer can provide a dedicated click url with additional parameters and those parameters will magically find their way to the app. Unlike other deep linking solutions, Branch takes the extra step to report these parameters back to their own dashboard and present it for the marketer to monitor the performance of each channel.

What’s the catch:

• Ad-networks that get paid based on CPI might not agree to trust this solution
• Advanced features like: postbacks and fraud protection are not available
• Not scalable – every new ad requires generating additional links and passing them through to ad-networks

Option 2 – Facebook and Google attribution

For apps that only buy media on Facebook and Google, this might be a good enough solution. Both companies have an SDK that allow the app to report post install events. The impressions, clicks, installs and post-install conversions will be shown in a dashboard along side the cost. This means that marketers can calculate ROI on each campaign very easily and take action right away without leaving the dashboard.

What’s the catch: Facebook and Google are not neutral. They actually have an interest to attribute installs to themselves and given Facebook’s history with reporting errors one might be worried about putting all his faith in them. In addition, installs might be reported twice as there is no 3rd party overseeing both platforms.

Option 3 – Free App Analytics by Kochava

This is an interesting option for app publishers. Free App Analytics offers the same features as the main Kochava solution and is using the same infrastructure and reporting interfaces with one major difference – it’s completely free.

What’s the catch: Publishers most provide Kochava with a license to use the data in Kochava Collective. This means that advertisers will be able to apply more advanced targeting for these users but the ads might not necessarily appear in the app that is using the Free App Analytics solution. Might be a small price to pay for saving a few thousands of dollars a month.

In addition to these 3 options, we have also heard the names – Attriboost and DCMN as potential free attribution vendors.

How to compare between different providers

With so many options to choose from, it’s easy that some companies are finding it hard to choose. The first question to ask yourself is – do I have an in-house analytics solution. The answer to that will tell you if you are looking for a point solution for attribution or a full solution that includes both attribution, analytics and flexible visualization system that allows you to create dashboards focused on different aspects of the data.

If you want to take the scorecard approach for comparing providers that’s a good approach according to Saikala of SpaceApe. Here is her free template for comparing attribution providers with a spreadsheet. She is also giving her opinion and tips how to use the spreadsheet in this article.

Where is the attribution market headed next

One of the areas that is still unresolved is the single ROAS view. Advertisers are surprised that even after years of evolution in marketing measurement tools, there is no single provider that allows them to see a per source/campaign/ad-group view with the following components:

• Cost of the marketing activity
• Returns on the marketing generated via in app purchases
• Returns on the marketing generated via in app advertising

This is the very fundamental view that allows marketers to evaluate the business merit of each marketing activity. However, today it is still very hard for marketers to get this view generated. Attribution providers realized this problem is an opportunity for them to provide a more complete solution and are now looking into two areas of expansion:
Aggregating cost data from all the different ad-networks and bringing them to the same view that summarizes the install counts and in app purchase revenues. While Singular was the first company to offer this type of aggregation, attribution providers identified the value for the marketer is far greater if he can get both elements from them rather than using an additional provider.
Assigning ad revenue to users and attributing them back to marketing activities to complete the ROAS picture. This area is gaining a lot of momentum as apps are expected to increasingly rely on ad-revenue according to industry forecasts. The first company to provide a solution in this field is SOOMLA but attribution providers are now working to add these capabilities by partnering or building in-house.

Last month, App Annie released it’s annual index of the top App Publishers in the mobile eco system. This is a tradition that App Annie has been going at for 3 years now. The winners not just get recognition but are also getting a very nice present. You can find the full list for 2016 in the image below as well as in the table where I also added some revenue numbers based on the companies financial reports.

App Annie used IAP revenue as the criteria for the list

There are obviously many ways to compare between companies and App Annie had to select one. Based on my analysis, App Annie is using revenues as the index in this list. This means that a publisher with 4.5B total downloads to date is not in the list while Miniclip is closing the list with a reported downloads number of 500M across all titles. That’s a fair decision, revenue is more important than downloads in the eyes of most people.

The other choice that App Annie made is a bit odd in my opinion. App Annie is ignoring ad revenue and is only measuring IAP revenue for the Index. It seems that Supercell and MZ are high on the list without any advertising revenues while companies who relay only on ad monetization are not on the list at all. Sure, the industry has been focused on In-App Purchases as the main monetization model but even App Annie reports that this is all changing.

App publishers who monetize with ads like Facebook were ignored

The one app publisher that is clearly missing from the first spot is Facebook. They are obviously the #1 app publisher in the world. In 2016, the company made $22.5B from mobile advertising revenues out of their total $27.6B. One might say that Facebook is a tech giant that shouldn’t be on the list but if you look at the company who currently holds the first spot on the list – it is Tencent. Tencent has almost twice as many employees and a similar size user base on it’s messaging and social networking apps as Facebook.

Here are a few companies that should have made the list based on publicly available information:

Another company that would probably make the list but we couldn’t find public revenue number is Outfit7. The company was recently acquired for $1B which suggests an annuarl revenue of at least $250M based in industry benchmarks such as the acquisition of King.com with a multiplier of 3.3x ($2B reported in 2015 and valuation of $6.6B).

Google would also easily make this list with mobile apps such as YouTube, Chrome and the Search app. However, they are the operators of the Google Play App Marketplace and are also in the business of selling Android based phones such as the Nexus and the Pixel so they should be excluded from this race.

The top 52 publishers according to App Annie

Below you can find the table of App Annie’s top 52. I added the revenue stats for some of the public companies so you can see what are the thresholds for different spots in the list.

This table is also available as a google spreadsheet here

Here is AppAnnie’s original list in an infographic format.

As well as in a text format:

1. Tencent, China
2. Supercell, Finland
3. NetEase, China
4. MZ, United States
5. Activision Blizzard, United States
6. Mixi, Japan
7. Line, Japan
8. Bandai Namco, Japan
9. Netmarble, South Korea
10. Niantic, United States,
11. GungHoOnline Entertainment, Japan
12. Square Enix, Japan
13. Electronic Arts, United States
14. Sony, Japan
15. Elex Technology, China
16. COLOPL, Japan
17. GAMEVIL, Sourt Korea
18. Ceasers Entertainment, United States
19. CyberAgent, Japan
20. DeNA, Japan
21. Zynga, United States
22. KONAMI, Japan
23. Chrchill Downs, United States
24. InterActiveCorp (IAC), United States
25. Spotify, Sweden
26. SEGA SAMMY, Japan
27. IGG, China
28. Perfect World, China
29. Kabam, United States
30. NEXON, Japan
31. Time Warner, United States
32. Playrix, Russia
33. Happy Elements, China
34. Snail Games, China
35. Netflix, United States
36. Glu, United States
37. Baidu, China
38. Scientific Games, United States
39. GREE, Japan
40. International Game Technology, United States
41. Scopely, United States
42. gumi inc, Japan
43. Marvelous, Japan
44. Microsoft, United States
45. Klab, Japan
46. Aristocrat, Australia
47. G-bits, China
48. Vivendi, France
49. Kunlun, China
50. Long Tech Network, China
51. Ateam, Japan
52. Miniclip, Switzerland

Some of you might have heard about the recent acquisition of Harpan LLC. by Zynga. Forbes have reported it last week and so have Busienss Insider and PC Mag. Reading these articles, you can’t help but notice the surprise of the reporters as they write about the amount of money spent to buy a 2-person company with 4 versions of the same game that was not even invented by them. Is Zynga out of their mind? Actually – it’s quite the opposite.

Zynga doubling down on ads

If you have been reading Zynga’s financial reports you might have noticed an interesting trend also highlighted in another post we published on this subject. Zynga has been depending more and more on ad revenues. In 2011 only 6.5% of their revenue came from advertising while in 2016 this number grew to 26.1% or $194M in absolute numbers. We will soon how this relates to the acquisition of Harpan.

The macro trends all point in the same direction

This shift in Zynga’s strategy might be a smart move in response to macro trends in the industry. There is a concensus in market forecasts provided by different intelligence companies. eMarketer projected that ad spending worldwide will increase from $101B in 2016 to $195B in 2019. So far their projection is coming true.

More recently AppAnnie projected that the amout of reveneu mobile game app companies are generating from in-game ads will increase from $21B in 2015 to over $50B in 2020.

The increase in ad spending is exceeding the growth in mobile users and creating inflation in two important KPIs of the industry:

• CPI -cost of install / bringing a new user
• ARPU and LTV from ads

This is also covered in some of our recents posts – AppAnnie: View to Play is here to stay and CPI Increase is here to stay

Harpan is part of a trend – acquisition of ad driven app companies

So if Zynga is indeed following the trends and made a strategic decision to base more of their business on advertising the acquisition suddenly makes a lot of sense. Sources in the industry suggests that Harpan was making almost all of it’s revenue from ads. These revenue streams are on the rise due to the mactro trends and the current worth of Harpan could double in 3 years due to increase in ad revenue monetization opportunities.
But Zynga is not alone in reading the market and pretending for the change: in 2016 we covered a peak of funding and M&A deals targeting companies with a strong focus on ad based monetization. You can add to that list the acquisition of Outfit7 for $1B and the acquisition of Ketchapp games by Ubisoft. I’ll not be surprised if we will see even more activity (funding and M&A) around ad-driven mobile game companies in 2017. Some companies to follow are:

• Tabtale
• Mobilityware
• Gram Games
• Voodoo
• FuturePlay

Today we are super excited to announce a new feature by SOOMLA. It allows app publishers who use in-app ads to see exactly what apps are being promoted to their users. After upgrading to the latest version of our SDK, the new screen should look like this:

We believe this type of information would be quite useful for publishers. Here are some of the potential use cases that we heard from the market:

Compliance to policy

Most ad-networks allow you to block certain advertisers. This is typically used by publishers to prevent competitors from poaching their users as well as to block inappropriate content. The ad-networks can comply or not comply with the requests and the publisher don’t have a way to know unless they have employees in each and every country to monitor what ads are presented.

Frequency monitoring

Showing the same ad to the same user 100 times is not a good user experience and most likely not the best monetization tactic. This tool allows you to spot such problems and address them with your ad partners.

Comparing ad-networks Apples to Apples

Many ad-networks run the same campaigns but the eCPM received by the publisher is not always the same. The new feature gives the publisher the opportunity to compare the eCPM of the same campaign across different networks.

The power of an API

The ability to know who is advertising your app is powerful on it’s own but it becomes even more powerful as publishers can access it via API and combine it with the granular revenue per user and the CPM per impression that is available through SOOMLA TRACEBACK.

Before we jump into the topic of postbacks for ad whales, lets first understand what are postbacks and why they are so important for any marketer of a mobile app company. Let’s say you have a dating app called TrueMatch and after you have had some organic growth you have recently partnered with a few marketing partners – mostly ad-networks who specialize in bringing installs. Let’s call one of them Tap4Buck. Tap4Buck places ads to promote your app TrueMatch on different websites and apps. As a result users click on them and get to your app-landing page. Some of them also decide to install your app and a smaller percentage even continues and converts to payers. Since Tap4Buck wants to give you the best results possible, they want to know which clicks ended up converting to installs and which ones converted to purchasing users. The problem is that the app store landing page breaks the flow of information so Tap4Buck can’t continue to track the user once they have installed the app. Postbacks solve this issue. If you are using an attribution provider (you should – it’s a must have these days), you can easily configure it to send postbacks to Tap4Buck and help them optimize your campaign for you.

What are ad whales and what are postbacks for ad whales

Now, let’s imagine that TrueMatch makes 50% of it’s ad-revenue from advertising. This means that sending postbacks for users who made purchases only tells Tap4Buck half the story. What about users who generate a lot of revenue from ad based monetization? Ad Whales are users who made at least $0.7 in ad revenue. This is the minimal amount of revenue a payer can make ($1 purchase minus 30% cut by Apple/Google). $0.7 threshold means that a conversion to ad-whale yields the same amount of money as a conversion to payer would yield. Postbacks for ad whales means that your attribution provider would send Tap4Buck an event every time a user that came through Tap4Buck has generated at least $0.7 in ad revenue and converted into an ad whale. This typically happens with 2%-5% of the users in games that are tuned towards ad based monetization but obviously changes from one game to another.

Who should care about postbacks for ad whales?

Companies who have any type of paid marketing activity would benefit from sending postbacks in general. The ones that also have an ad revenue component that amounts to at least 15% of their total revenue should be sending postbacks for ad whales. Ad whale postbacks also benefit the partners on both sides. For the marketing partner that sent the traffic to your app, better postbacks means more effective campaigns and happier customers. For the monetization partners, better postbacks means that the app will get more ad whales as a result of the optimization and therefore their volume of revenue would increase.

When – 2017 is the year of change

If you have been following the industry trends you already know that ad revenues are becoming the dominant way to monetize apps. It’s already as big as In-App Purchase and is projected to grow faster in the next 4 years. In Mobile games alone, App Annie projects in-app advertising will amount to revenue streams of over fifty billion dollars ($50B) for the companies who will be placing these ads in their apps. The total mobile ad spend worldwide is projected to reach $195B by eMarketer. As ad based monetization is becoming so important, companies are looking for tools to optimize them and postbacks are a big part how the mobile marketing space has been operating.

Where – not all geographic areas are created equally

Most of the media buying today is concentrated in a few countries where people are willing to spend money on in-app items. These countries are often referred to as Tier 1 countries and are also where most of the postbacks are being fired today. At the same time, postbacks for ad whales bring a new opportunity to table. There are other countries with large population where people can’t afford to buy in-app items. These countries offer low rates for user acquisition due to lack of demand. Setting up postbacks for ad whales allow app publishers to find opportunities to acquire users in these countries with positive ROI. This means that as postbacks for ad whales became more popular through out 2017 we will see a shift in the postback geographical activity areas.

Why track conversion to ad whales and report it as postbacks?

There are 3 main reasons to track and post ad whale conversion:
Business goals alignment – many apps that have a big ratio of ad revenue today would make up a game progress goal such as “100 sessions completed” or “10 levels”. These goals would be defined as events and companies would track conversion to these goals and report postbacks to the ad-networks. However, these goals are not aligned with the business of the company. Conversion to payers and to ad whales is a far better goal and will bring better results in the long term.

ROAS not enough – Measuring and optimizing the return on ad spend is the best theoretical approach. However, in a real world situation it relays on predictive models that are often hard to implement. Media buyers often require a more day to day metric to optimize against. This is why most UA campaigns track the conversion to payers as one of the leading KPIs. Similarly, in apps that monetize mainly with ads, the easiest way for media buyers to optimize is against a goal of conversion to ad-whales.

Postbacks allows manual as well as automatic optimizations – reporting the conversion to ad whales as a postback to the traffic source allows them to have an optimization goal that is aligned with your business. In turn, it impacts what users you will be getting from this traffic source. In some channels such as search and social media there is a lot of algorithmic optimization taking place. These algorithms need a goal to optimize against so having them optimize for ad whales would be the best approach for an ad supported app. Similarly, in other channels there is a manual optimization process of eliminating bad sub-sources such as sites or segments – these manual optimizations also requires a goal and reporting ad-whale conversion as postbacks provides such a goal.

How to set up ad whale conversion as postbacks

There are 3 components for setting up ad whale postbacks in your app:

#1 – Tracing back ad revenue per user – in order to detect the ad whales and report them you will need a way to measure the ad revenue for each user separately. Your monetization partners typically report ad revenue per country and average CPM but not the ad revenue for specific users. The most accurate way to measure ad revenue today is SOOMLA TRACEBACK. It is the only platform that can identify the ad whales for you.

#2 – Connecting the data pipelines – your attribution platform is the one in charge of sending postbacks to your marketing partners. Once you have SOOMLA integrated in your app you can configure it to send the right postbacks to your attribution platform with just a few clicks.

#3 – Setting up postbacks in your attribution platform – this step is slightly different depending on the attribution partner. However, they all have a partner configuration screen where you can set up the ad-whale conversion from phase #2 as the trigger for the postback.

One of the things that were a part of mobile apps since the early days is the REMOVE ADS button. The idea is simple – ads generate low amounts of revenue per user and getting $0.99 or $1.99 from them is better from the app publisher stand point.

Not showing ads to payers is the standard practice

Even in games that don’t have a specific purchase option around removing ads it became a standard practice to not show ads to depositors. This is based on the same approach that ads yield low amounts of revenue while purchases yield higher amounts.

Rethink what you know – ad whales exist

In recent posts we covered the existence of ad whales. Individuals who generate large amounts of ad revenues for their app publishers. Here is a user who generated $74 in ad revenue in November, and this user generated $52 in December. While these levels of revenue per user are quite rare for ad monetization, they are also quite rare when it comes to in-app purchases.

How many users generate enough ad revenue to level with payers

If we consider how much revenue is generated by a payer – the minimum is $0.7. The lowest purchase by a user is $0.99 and given that Apple and Google take a cut of 30% the publisher gets 70 cents.
Based on the data SOOMLA Traceback is collecting we can check how many users go over the point. How many monetize with ads at least to the same level as payers. The result is that in some games that relay heavily on ads it’s more than 10% of the user base. This is higher than a normal conversion rate to payers. We can also check how many users went over $3.5 which is the publisher share of a more $4.99 purchase by a user. The result is that it’s over 2% in some games.

Rewarded videos offer incentives to users

Let’s start thinking about a different approach. Should we allow any type of advertising to people who paid? One area to consider is the type of advertising in question. Ads that may annoy a paying user could be a bad choice from a user experience perspective but what about incentivized formats such as offer walls and rewarded videos. These formats are loved by users so the question becomes more about optimizing the revenues.

Option 1 – reversed approach

Let’s imagine for a second a complete mirror image of the “no ads for payers” approach. What this means is that we set a threshold of $0.7 and the users who have made at least $0.7 in ad revenue are considered ad-whales. Once we classified someone as an ad-whale, we don’t allow him to make purchases in the game. That would be the reversed approach to the “no ads for payers” approach. If it sounds silly to you – it’s because it is silly. Blocking someone from paying in a game is just nonsense but so is the “no ads for payers” approach. Why block someone from making revenue for you through watching ads?

Option 2 – balanced approach

A more reasonable approach to the problem is to simply allow users constent access to all methods of getting benefits. A user can get benefits by buying them, by watching video ads, or by taking on offers. Since the payout of a video view by a user is normally determined in retrospect, the publisher could apply a model where the rewards are dynamic based on the past payouts received for that user. If such a model is implemented, the publisher can guarentee that the price of getting the benefit is balanced across the different methods the user has for getting them. For example, if the eCPM of a user starts falling after a while, his rewards for watching videos will decrease and he will be more inclined to make purchases. If however, the eCPMs for a specific users are growing over time, the rewards he will get from watching videos will increase and he will have more motivation to keep watching them as opposed to buying something.

Ad measurement tools are becoming a must have

This type of innovative monetization strategies are becoming critical for the survival of game studios. We covered before the increase in CPI rates and how companies needs to adapt to stay relevant. Advanced segmentation and monetization measurement tools that can find the ad whales segment for you are becoming a must have in today’s mobile eco-system.

Demand diversity is a topic not many people discuss in the mobile game monetization forums. To understand it let’s think about the journey or a user through our app. The first time the user watches an ad, the mediation will check what is the ad-network that is on the top of the waterfall today and will have that network serve the ad. The network will normally try to serve the highest yielding campaign they have – why don’t we call the app in the ad Mobile Assault – it will help us refer to it later. In many cases, this user will see the same ad over and over again in the same day and more time in the next day. Having a user see an ad for 100 times these days is not uncommon. This is the demand diversity problem I’m referring to.

Why demand diversity is important

From a user perspective, seeing the same ad over and over is a poor user experience. The first time you are seeing an ad, it could be interesting, cool or even funny. If you have seen the new Clash Royal ads, they are quite amusing. However, nothing is interesting, cool or funny after you have watched it a dozen times already. At that point, it is just annoying.
From an ad effectiveness perspective, showing the same ad over and over is a bad choice as well. It leads to banner blindness so users stop noticing the ad. Most ads today are shown with the purpose of creating installs for tha advertised app and blindness leads to low click rates and conversion rates so less installs are generated.

The business models determine who takes the risk

One of axioms of online advertising is the chart below. Basically it says:

• In a CPM model the risk is on the advertiser side while the publisher has guarnteed income
• CPC is the middle ground
• In a CPA/CPI model the risk is on the publisher side while the advertiser has guarnteed outcome

The mobile advertising industry today is mostly driven by the CPI model which is a form of CPA meaning that the publishers assume most of the risk. They place ads in their apps hoping to get paid but their monetization is driven by whether or not the users ended up taking additional actions outside of their apps.
So now that we established who has the risk, we also know how is the one that gets heart from the situation. Users who watch the same ad over and over again become blind to it and the publishers’ monetization levels are getting hurt.

Risk and data are normally aligned

In most business situations, the party who is willing to takes the risks is the one with better tools to assess it and mitigate it. For example, in a CPM model, the advertiser assume the risk but they demand transparency about where their ads are being placed and have tools to measure the performance. In mobile app monetization however, the publishers are the one assuming the risks but they are doing so with complete lack of data or measurement tools. More specifically, the publishers are the ones that get hurt from the lack of demand diversity but they actually have no way to measure and manage it.

Mediation platforms are also left in the dark

The parties that are in the perfect position to be the police of demand diversity are the mediation platforms. Publishers are trusting the mediation companies to act as their agents and help them manage things of this sort using their ad-tech expertiese. The problem is that mediation companies are also in the dark about what ad is being shown to the user. They simply call the ad-network SDK as a black box that shows ads but they don’t get any information out.

Ad networks only see their own ads

The only type of company that has information about what ads are shown to the user are the ad-networks. The problem, however, is that each ad-network is only aware of what ads they show. Instead of collaborating and sharing this data between them and be part of the solution they are part of the problem since an ad-network that is not aware of what other ad-networks are showing is likely to show the same popular advertiser again to the user.

Choosing ad networks smartly

App companies often tend to choose ad-networks based on rumors of their projected CPMs or based on how well it worked for their friends. Often, one ad-network will seem better than another in the eyes of the publishers due to their presence in shows and their general brand perception. However, choosing 4 networks that are practically representing the same demand menas making the problem worse. It’s common to see a rewarded video stack that includes Supersonic/Ironsource as the mediation in addition to Vungle, Adcolony, UnityAds and Chartboost as the ad-networks. These networks are considered the best when it comes to rewarded videos for mobile games. The problem here is that thery are all bringing similar types of ads. The chances of a user seeing the same ad over and over again is much higher like that. A smarter strategy for selecting ad partners is to try and figure out how to diversify. SSPs can often bring more diversification through access to exchanges and there are also companies like Mediabrix who focus only on bringing brand advertising.

Diversifying through blacklists

Most ad-networks supports blacklists as a way for publishers to block certain advertisers from placing ads in their apps. This is mostly used for 2 things: 1) blocking competitors and 2) blocking inappropriate ad content. This feature however, can also be used to force ad-networks into skipping ads that are being shown too much. If you focus on the top 5 ads shown in your app and only allow one ad-network to serve them you will force the other ones to bring new ads and diversify the user experience.

Getting more visibility to what ads are being shown

While a solution to this problem might look far fetched at the moment, it’s actually feasible. The ad-networks are under a lot of pressure to be more transparent at the moment and this is one area where if each network gives up some transparency it can receive a lot in return. After all, ad-networks also loose from ad blindness. It will be a better world for everyone, publishers, advertisers, mediation platforms, ad-networks and users. However, someone needs to take the first step. Until then, feel free to contact SOOMLA if you want access to this kind of information. A side benefit of publishers gaining access to this info is that it will accelerate the path to full transparency by the ad-networks.