Author

Raised in the Kibbutz and reborn in the city, Yaniv is a certified entre-parent-neur. When he’s not busy doing SEO, content marketing, administration, QA, fund raising, customer support… [stop to breathe], you can find Yaniv snowboarding down the slopes of France and hiking with his kids. Yaniv holds a B.Sc. in Computer Science and Management from Tel Aviv University. He is also an avid blogger and a speaker at industry events. Before SOOMLA, Yaniv co-founded EyeView and INTENTClick.

Industry Forecasts, Industry News

Zynga buys harpan llc. Pays $42M for a 1 person company

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.

Emarketer report projects a growth in mobile ad spending reaching $195 by 2019
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.

App Annie projects growth in ad revenues generated by mobile games. From $21B in 2015 to over $50B in 2020The 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

 

 

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Announcement

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:

Screen shot of the new advertiser screen available through SOOMLA TRACEBACK platform - each row contains an advertiser that places ads in the app of the customer

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.

 

If you wnat to start measuring your ad monetization and know who is advertising in your app you should check out SOOMLA TRACEBACK – Monetization Measurement Platform.

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Analytics, Marketing

blog header image with postbacks for ad whales written as the title and signs that say where, what, why, who, when and how

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.

 

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App Monetization

IMG_4102One 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.

 

If you want to start measuring your monetization and find ad whales you should check out SOOMLA Traceback – Ad LTV as a Service.

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App Monetization

Kate Uptown is starring the Machine Zone (MZ) ads for their Game of War which has been advertised heavily in the last 24 monthsDemand 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

Illustrative chart showing the risk levels for publishers and advertisers based on the selected business model: CPM, CPC or CPI

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.

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App Monetization, Infographic

1BD277C0-AE65-4D4E-A593-28EED01006E2-5333-00000A48117CA031_tmp

This is the 3rd post in the series. The ad viewer of January is a user who made his app publisher very happy by generating the most amount of ad revenue for him when compared to other users. Our unique tech allows our customers to associate ad revenue to the user level and measure ad ltv. To check out previous months’ over achievers, follow this link for December and this one for November.

January Ad Viewer of the Month

This user alone generated 53.39 dollars for his app publisher in 20 activity days during January. What’s also interested is that he only recently started using the app – in mid December. The user contributed a bit over $20 in Dec. which makes his ARPU to date or his LTV to date $74. We are sure it will get even higher as he generates $2.66/day on average during January.

Attribue Ad Viewer of November
Country  United States
Device  iPad
Ad Types  Interstitial
Impresions  416
Active days 20
Revenue $53.39
eCPM $128.36
ARPDAU $2.66

IMG_4044

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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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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