Marketing, Research

Singular ROI Index symbol with a banner saying best ad networks over a blue background

About a week ago Singular released a very interesting study ranking different traffic sources or user acquisition channels according to how much return on ad spend they bring for companies using them. Return on ad spend (ROAS) or marketing ROI has been a critical KPI for marketers in the mobile ecosystems. It allows decision makers to compare marketing activities not only by the amount of received installs but also by how much dollars were received from users who arrived through the channel and compare cost vs. return on each channel sperately.

The Singular ROI index

The study can be downloaded via this link – Singular ROI Index. It ranks the top 20 ad-networks in terms of ROAS for Android and the top 20 for iOS. It also draws some interesting insights about the differences between these two ecosystems. It finds that despite higher CPIs on iOS the ROI is 1.3x when compared to the ROI for the same app on Android running via the same ad-network. This is partially due to higher average payout on iOS.

What about Ad Revenue

The report is lacking in one aspect – it only accounts for In App Purchase revenues for ROAS calculation. A more complete view on ROAS today would consider 3 elements for each channel:

  • Cost for that media channel
  • IAP revenue made by users who came through the channel
  • Ad revenue generated by users who came through that channel

Factoring in the ad-revenue generated from in-app ads in the ROAS calculation is becoming more and more important as the change in the mobile monetization landscape continues. This means that ad-networks who bring users who don’t convert to payers but do convert into ad-whales are under indexed in Singular’s report and networks who brings users who convert to payers but don’t contribute any ad-revenue are over indexed in the report.

Your own ROAS should also consider ad revenue

If you are using Singular for calculating your own ROAS, your decisions may be subject to the same measurement errors. Fortunately, there are already solutions for attributing ad revenue and completing your ROAS picture such as SOOMLA TRACEBACK consider using them and connecting them to Singular.

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App Monetization, Industry Forecasts

Latest report from App Annie supports the claim that the market is choosing View to Play as the business model of the future for the mobile ecosystem

Last November, while most of us were already preparing for the holidays, AppAnnie released a very interesting report that might have gone unnoticed by some of you. One of the Key Learnings is that Free-to-Play is giving way to View-to-Play. In other words, the fastest growing business model in the next 5 years in mobile apps will be in-app advertising and not in-app purchases.

In app advertising is growing at 24% CAGR and expected to surpass $110B by 2020 while Freemium is trailing behind

About App Annie and the report

The App Market data company needs no introduction from us and has become the source of data for most of the industry with regards to app store data. The company has over 600 employees in over 13, many of which are focused on researching data. From time to time, App Annie generates industry reports and forecasts and shares those through it’s blog and other content channels.
Company website – https://www.appannie.com/
Report Download Page – http://go.appannie.com/report-app-annie-app-monetization-2016-dg

What is View to Play?

If you haven’t heard the term View to Play, it’s probably because it’s new. When the app store just emerged, apps were sold and not given away for free. With the introduction of In-App Purchases, developer quickly started offering free apps to attract more users and find different ways to monetize them. This led to a new breed of game companies that specializes in conversion optimization, analytics, segmentation and performance marketing – the term Games as a Services was coined to reflect these new practices as well as Free to Play gaming. View to Play is similar in approach but instead of pushing users towards in-app purchases, the optimizations are focused around ad based monetization models – hence, “View to Play”. Users who want to advance in the game are often offered rewards and incentives for watching ads and a new breed of companies emerges with a toolset that includes special analytics capabilities around ad revenue measurement.

What is Driving the Change

In a recent article we covered how CPI is increasing and companies needs to adapt quickly. Well, some have already started and the App Annie report hints that more companies will be adopting the view to play model in the future.

These companies are realizing something that others have not. The CPI increase is highly correlated with the expected increase in ads LTV. They are both been driven by the same forces – the total mobile advertising spend is increasing twice as fast as the user growth. IAP revenues are actually increasing slower than the user growth and are becoming more and more concentrated in top grossing apps.

The cost per install is increasing over time as well as the average ad based revenue per user while In-App Purchase models are declining

This means that companies who transition quickly to view to play will be far better prepared for the future increase in CPIs. That is, as long as they can also adapt their measurement and optimization practices with a platform such as SOOMLA Traceback.

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App Monetization, Infographic

Ad viewer of December is the user who generated the most amount of ad revenue for his app publisher

We are continuing the series of Ad Viewer of the month that we started last month. This type of analysis is one of the things that sets SOOMLA apart. We are using the Traceback technology to provide publishers with reports that get as granular as a single user. The Ad Viewer of December is a single user who made the most amount of revenue for the publisher of the apps he was using. Here is the link for last month’s report – Ad Viewer of November

December Ad Viewer of the Month

The amount of ad revenue generated by this user is mind blowing – $52.92 generated for the app publishers. He registered 19 active days in the month of December and made an average of $2.78 in each one of them. Unlike the Ad Viewer of November, this user also received a lot of in-game rewards for his revenue contribution. His favorite ad-types were Offer Wall and Rewarded Video that surely gave him incentives for his ad interactions.

Attribue Ad Viewer of November
Country  United States
Device iPhone
Ad Types  Offer Wall, Rewarded Video
Impresions  398
Active days 19
Revenue $52.92
eCPM $132.98
ARPDAU $2.78

Infographic featuring the ad viewer of December and different attributes about him. How much revenue was generated and at what eCPM

NOTE ABOUT SHARING – Feel free to share this infographic and embed it in your blog. If you do this, we will appreciate a link to http://soom.la.

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Analytics, Marketing

Reality can prove very different than the statistics that represent it

There is a simple idea at the core of most mobile marketing campaigns these days – if you spend $x on some marketing activity and received $y in return you want y to be grater than x. This is often referred to as ROAS or campaign ROI. We have trained mobile marketers to break down their activities to small units: ad groups, ad sets, ad creatives, audiences, … and find the ones that show ROAS. Doubling down on the positive ROAS units while shutting down the negetive ROAS units is the leading campaign optimization strategy today.

Here is the problem – it only works under certain conditions.

There is a famous saying by Mark Twain – “There are lies, damned lies and Statistics”. It comes to warn people about using statistics in a wrong way. One such way is using statistics when small numbers are involved. Another way in which statistics are deceiving is called Multiplicity or Multiple comparisons. Let’s see how those come into play when calculating returns.

Beware of the small numbers

Most companies base their ROAS calculations only on revenues from In-App Purchases. This is a result of 2 things:

  • Up until recently, ad based monetization and ad spend were mutually exclusive
  • Until SOOMLA TRACEBACK there was no way to attribute ad monetization

The problem with In-App Purchases revenue is that it’s highly concentrated. Studies have shown that purchases are less than 2% of the users and among those 2%, the top 10% generate half of the revenue. Let’s say that you spent $5,000 to acquire 1,000 users and you are trying to figure out the return. Most likely you have 20 purchases but there are 2 whale users who generated $1,500 each (this is aligned with the studies – yes). Now, suppose you had 2 ad-groups in that campaign and you are trying to figure out which one was better. Here are the options:

  • Group A had both whales
  • Group A had one whale and B had one whale
  • Group B had both whales

Since we are talking about 2 users here – the scenario that actually happened would be completely random. Even if one ad-group is better than the other it is still very likely for that group to outperform the other group when we are talking about only 2 users who can flip the outcome completely. The danger here is that our UA teams would double down on the ad-group that yielded the 2 whales without understanding that it’s not better than the other. If we look at sample sizes here n=1000 is normally considered a good sample size. Has the monetization been less concentrated a sample size of 1,000 should have been enough to make decisions. However, for the purpose of acquiring whales the actual sample size is n=2 in this case. We should try to get at least n=500 before we start making decisions on media buying. The problem of course is that attracting 500 whales could be a very expensive test – more than $100,000 based on the numbers in the example above.
On the other hand, companies who monetize with ads enjoy the fact that more users participate in generating revenue and can make decisions based on smaller sample sizes and smaller test budgets.

Multiplicity – the bias of multiple shots

Another bias we normally see in mobile marketing is Multiplicity. The easiest way to explain this is with the game of basketball. Let’s imagine you are through from the 3-pt line and you have 50% chance to score. What happense if you try twice, the chances of scoring at least once becomes 75%. With 3 shots, it’s 87.5% and so forth. The more times you try the better your chances to score.This is what happens when you try to hard to find positive ROAS in a campaigns that has a lot of parameter. You compare ad-groups – that’s 1 shot, you compare ad creatives – that’s a 2nd shot, you compare audiences – that’s a 3rd shot and so forth. The more you try to find a segments with positive ROAS by slicing and dicing the more likely you are to find a false positive one.

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Industry Forecasts, Marketing

IMG_3787

If you have been marketing your app long enough you must have noticed a CPI increase. Getting users to install your app used to cost a lot less than it costs today. This change can be noticed globally and across different platforms.

The reason behind CPI increase

One of the drivers of the CPI increase we are seeing is the brand budgets starting to pour into mobile. when the internet just emerged, users adopted it first and a few years later bigger budgets started to follow. Facebook story also shows a lot of resemblece – the social network first had 1 billion users and 3 years later it was making $25B in advertising. Mobile advertising also follows the trend of the money following the eyeballs. Recent report from eMarketer projects mobile advertising to reach $195B by 2019 with most of the increase coming from brand budgets.

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Ad spend per user is growing

Here is another way to think about it – if you devide the projected ad spend growth by the projected user growth you can see that the average ad spend per user has been increasing but will continue increasing even more.

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So apps who want to get users face 2 options:

  • Try to relay on organic discovery
  • Increase their LTV in order to afford higher CPIs

Relaying on organic discovery however has proven more and more difficult due to the app stores being overly crowded. Apps today have to invest in marketing to gain momentum. So that leaves us with only one option – increasing LTV.

Adapting to change in CPI prices

In order to increase LTVs app companies must adapt to the change quickly and make the brand budgets work to their advantage. In other words, your company needs to make sure some of this new money finds it’s way over to you. The most effective way to increase LTVs is to introduce a view-to-play model in your app and targeting the 98% of the users that don’t pay. This puts your app in a position to enjoy the projected increase in ad-spend per user and not suffer from it. From a unit economics perspective, monetizing a larger portion of your user base allows you to increase ARPDAU and LTV. Combined with an adequate ad revenue measurement tool, you would be able to increase CPI bids with confidence, remain compatitive in the market and keep growing your app.

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App Monetization, Video

Blog header image - what's inside the advertising black box - video snaps from casual connect panel
Last Casual Connect in Tel-Aviv introduced many interesting lectures and panels. However, this is the one when ad-networks secrets got revealed. These are the top 9 moments of the panel presented in an easy video navigation tool.

 

Panel Participants

Lior Shiff – Co-founder and ex-CEO, Product Madness

Guy Tomer – Co-founder and CMO, TabTale

Niko Vouri – Co-founder and COO, Rocket Games

Yaniv Nizan – Co-founder and CEO, SOOMLA

Noam Neuman – VP Mobile Strategy at Matomy

Fernando Pernica – Mobile Monetization at Ad-Colony

Minute 5:29 – The Secret Guage

Lior asks Fernando whether there is a way for ad-networks to dynamically manipulate rev-share rates for publishers and create periods where they are more competative. Can you gues the answer?

Minute 12:06 – What Surprised Yaniv

Lior asks Yaniv what surprised him the most when lifting the hood of the black box. Not all app users are made equal apparently.

Minute 14:30 – When Ad Networks get Naughty

Guy tells the story about an ad-network that didn’t play by the rules and showed inappropriate ads to kids user audience.

Minute 32:11 – Brands – Friend or Foe

When a big change comes along you can either get defensive or find the opportunities that change creates. While the entrance of brands to mobile ads makes buying users harder it creates new monetization opportunities that translates back into the ability to place more competitive CPI bids.

Minute 33:09 – Is there an Unbiased Mediation?

Why is the ownership of mediaiton by ad-networks a problem? Bias and lack of transparency come into play here.

Minute 35:54 – Ad Networks’ Transparency

Guy explains that regardless of their various attempts to get more data from the ad-networks they still couldn’t get granular data and even aggregated data is sometimes tough.

Minute 37:07 – Lack of Transparency is a Double Edged Sword

Fernando explains how mediation is a black box for the ad-networks and how the lack of transparency goes both ways.

Minute 39:53 – Are There Ad Whales?

Lior is asking Yaniv and Guy whether or not Ad Whales exist. Guy explains that he can’t track it today but Yaniv is answering with precision: “We have seen $124 generated by a single user”.

Minute 45:43 – How Would You Leverage Ad LTV Data

Yaniv is asking Niko what would he do differently if he had the power to know. Niko explains how granular ad revenue data can impact their user acquisition decisions.

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App Monetization

How can one single user generates $74 in ad revenue - here are some answers

I received the following questions yesterday and wanted to share the answers so more users can learn.

Being an indie game developer I’m trying to understand all the low level stuff that happens around user acquisition but I often lack experience to close the gap. After reading the latest blog post
(http://blog.soom.la/2017/01/ad-viewer-of-the-month-generates-74-at-134-ecpm-for-publisher.html,
I have a few simple questions that I hope you can answer. Regarding the user that generated $74 in ad revenue at $134 eCPM.

The reason of such a high eCPM

Does the advertiser know the spending potential of the user (whale) and his playing patterns and such wants to acquire it in a game where spending can be in tens of thousands of dollars?

First, let’s start with some basic terminology so that we will be aligned:

ADVERTISERS – App publishers who want to spend money and get users.
PUBLISHERS – App publishers who want to get money and are willing to put ads inside their games

Now, let’s talk about the reasons for high eCPM. In most games, the advertising transaction model is based on performance – CPC or CPI. Advertisers are willing to pay high CPI and CPC when they believe that users will likely to do two things:

  • be loyal/engaged/retained
  • spend money in their apps

Most likely in this case, the ad-network was able to convince some advertisers that a segment of users that includes this one is worthy of these high CPC or CPI but this by itself is not enough.

In order for high CPC or CPI to translate into high eCPM and revenue for the publisher, users needs to take actions – they need to engage with the ads, click on them and most likely also go and install the apps that were advertised to them.

In CPC model – eCPM is CPC bid x CTR
In CPI model – eCPM is CPI bid x CVR x CTR

There is an interesting report from Comscore that identifies a group of users they call “Heavy app downloaders” – you can read more about these users in this link – http://blog.soom.la/2016/12/reward-abusers-and-heavy-app-downloaders.html. It’s highly posible that the user who generated $74 in a single month was a “Heavy App Downloader”.

Was it a game where spending can be in tens of thousands of dollars?

Yes, as a general rule all big mobile game advertisers are apps where you can spend thousands of dollars.

Was this a result of retargeting?

Was the advertiser was trying to re-target the player which has churned? from the game, but did invest a big amount of money before churning?

It is possible that this user have been part of one or more retargeting campaigns. Obviously companies who have already experienced good results with this user would try to get him back. However, I doubt that this is enough. It takes more than one advertiser to generate that sort of revenue for the publisher and retargeting campaigns are only a small piece of the advertising ecosystem.

Are the advertisers wasting money on this user?

Regarding the 555 impressions delivered, if all these impressions are advertising a single game, I guess at some point the advertiser will stop Targeting ads to that player because it is a simple waste of money (he can not acquire the player). Is that correct?

Actually not correct.
Most ad-networks charge the advertiser based on a performance model CPI and CPC. This means that if the publisher earned $74 the specific user should have not only watched the ads but also clicked and installed some of the apps that were advertised to him. Most likely the publisher will want to keep getting this money so he will continue to serve ads to that users. The ad-network will keep targeting more ad campaigns to him due to the high performance and revenue he is generating for them.

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App Monetization

Ad viewer of the month header image featuring the user who made the most amoutn of ad revenue in the month of November

To demonstrate the pure awesomeness of TRACEBACK technology and it’s superiority over the alternatives we decided to start doing an “Ad Viewer of the Month” celebration. The idea is simple – we scan through all the users tracked through SOOMLA TRACEBACK and find the users who made his publisher the most amount of ad revenue. Unlike with In-App Purchases, these users are not taking this money out of their pockets but rather generate revenue for the app publishers by watching large amounts of ads and engaging with them.

November Ad Viewer of the Month

The user who did the most amount of ad revenue in November did no less than $74.76 for his app publisher. He used the app every single day in November – totaling 30 active days. More importantly, his eCPM and ARPDAU number are way off the charts and much higher than the averages for that app. Here is his score card

Attribue Ad Viewer of November
Country  United States
Device iPad
Ad Type Interstitial
Impresions 555
Active days 30
Revenue $74.76
eCPM $134.70
ARPDAU $2.49

Infographic showing details about the ad viewer for the month of november, this single user generated $74 for his publisher at $134 eCPM and $2.49 ARPDAU

NOTE ABOUT SHARING – Feel free to share this infographic and embed it in your blog. If you do this, we will appreciate a link to http://soom.la.

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Analytics, App Monetization

Ad revenue concentration hero image with chart and text

We are happy to report some interesting data points we recently looked at. The goal was to understand how concentrated ad revenue really is. Everybody knows already that the 80/20 law applies in IAP – at least 80% of the revenue is driven by the top 20% of purchasers. There is plenty of research showing how concentrated IAP revenue is. Ad revenue, however, is still a mystery for most publishers and very few companies actually have the data on how concentrated the revenue is. If you take the naive approach and believe all the users contribute revenue based on the average eCPM you might think that the ad revenue concentration chart will be flat. The reality however, is very different.

Comparing Concentration in Ad Revenue vs IAP Revenue

In the image below you can see a comparison of the revenue concentration between ad based monetization and IAP based monetization. These charts are based on data from 28 days of activity in a Match-3 game where most of the monetization comes from interstitials. The revenue model behind the ad monetization is CPC in this case.

On the left side, the IAP revenue is highly concentrated and 80% of the revenue is generated by the top 20% of the users. The top user generated more than $300 in revenues for the app.

On the right side, we see that the ad revenue is also highly concentrated. The top 20% are contributing more than 50% of the revenue here and the top user generates $2.5 while there are users who only contribute a few cents.

Comparison between concentration of ad revenue and IAP revenue

Ad Revenue Concentration with Reward Ads

One of the hot trends of 2015 and 2016 was the adoption of rewarded video ads by many game publishers. We wanted to look at the ad revenue concentration in rewarded video as well. The chart below does exactly that.

The data here is from a single day so obviously more concentrated than information aggregated over an entire month. The game here is an mid-core action game and the monetization is done with both rewarded videos and an offer wall.

The ad revenue concentration is much higher in this data set. The top 20% of the users are contributing 90% of the revenue and the top user is contributing more than $15.

Ad revenue concentration in rewarded video ads and offer walls

Who are the users contributing high amounts of ad revenue

Once you realize that the ad revenue is concentrated almost as much as iAP revenue, your next question is likely to be: “how are these users”. On a high level, these users are typically the users who download many apps as indicated by a Comscore Report highlighted in this article. But you can go a lot further than that. Using SOOMLA Traceback you can profile these “Ad Whales and target them in marketing activities.

 

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Analytics, App Monetization

Reward Abusers written in blue text next to a trophy and Heavy App Downloaders written in green text next to an app icon with two axes

As the market adopts TRACEBACK technology we are learning new things about how users interact with ads. This allows us to classify users into types. Let’s think about these two types of users who are highly relevant to rewarded video based monetization.

Reward Abusers – these are users who watch the videos to get the rewards but are not contributing any revenue in Neither IAP nor in Ad revenue.

Heavy App Downloaders – these are users who download and try multiple apps each month. Typically, these are the users who end up generating the most amount of video ad revenue for your app.

How these segments impact your business

Let’s say you are buying traffic from a new source. You probably ask yourself, how many installs I received but you should also ask the million dollar question – “what type of users am I getting?”

Why?

Consider 2 possible sources:
Incent Campaign – this campaign gives users an incentive in another app in return for downloading your app. By nature these users are after the rewards so this source might be heavy in Reward Abusers
FB Campaign – now consider a campaign targeting lookalikes of users who are existing Heavy App Downloaders. This campaign is likely to bring more Heavy App Downloaders. You can learn more about this specific technique – here

How can you segment your users

If you are already convinced that knowing the Reward Abusers from the Heavy App Downloaders can impact your business your next question should be how to spot them. Let’s think about what features are similar and which ones are different between thm.
App Engagement – both user types have high app engagement
Video Ad Engagement – Reward Abusers will watch as much videos as Heavy App Downloaders
Post Impression Performance – this is the feature that sets them apart – Reward Abusers will only watch the videos while Heavy App Downloaders will also click and install the apps presented to them

Reward Abusers Heavy App Downloaders
App Engagement High High
Video Ad Engagement High High
Post Impression Performance Low High

So understanding what the user does after he watches the video ad is the key here. Today, there are two solutions in the market:
Developing In-house – this requires your engineering team to figure out specific ways to track post impression events with each ad-network and to keep updating the code every time there is an update to the ad-network SDKs
SOOMLA TRACEBACK – our platform does all the work for you, it requires a simple integration but once implemented you will be able to segment your users reliably, track ROAS and do many other mind blowing optimizations to your ad-revenue. CLICK TO LEARN MORE

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