Announcement

The waterfall screen shows the ecpm decay for your mobile app

We recently added a new screen to TRACEBACK dashboard called “Waterfall”. You can access it once you are in the analytics mode viewing one of your apps. This post will go through the basics of this screen and how to use it. Here is what we will cover:

  • The goal of the screen
  • How to set up a view that is productive
  • Comparison Table section
  • eCPM decay chart
  • Ad Network Comparison section

Goal of the screen

This screen is the first time publishers get a chance to look at their eCPM decay. It expands the horizons of what data publishers can access and opens up a world of possibilities. It is an important screen since ad-networks’ yield should be compared Apples to Apples. Publishers often develop a sentiment towards one ad-network since they see higher eCPM coming from that network. In reality this could simply be a result of waterfall position and not related to the ad-network. This screen fixes that!

There are many other implications. Publishers can realize that they need to change their ad-network mix, they might change the way they integrate ads into the game as a result and they will also have more productive discussions with their ad-network partners.

Setting up the view

This screen is highly flexible which means users can easily set up views that are not productive for them. Our recommendation is to set up as follows:

  • Single country at a time
  • Single ad-type at a time
  • Select a date range that brings the total #1 impressions to at least 100,000 impressions
  • Focus on 2-3 ad-networks that generate volume and remove
  • Ignore eCPM rates when impression volume is lower than 10,000

These guidelines will allow you to make the most out of this screen.

Comparison Table section

The first section of the screen is a table with full details about what is hapening in every impression number. Publishers can look at each ad-network and see the number of impressions, the total revenue generated by that impression number and the eCPM all broken down by the impression number.

img_3557

eCPM Decay chart

The middle section gives the averaged eCPM that the publisher receives for every impression number. The darker bars are the Actual eCPM – the average across all selected ad-networks for the segment filtered by the user. The optimal eCPM represented by the brighter bars is the maximal eCPM given by one of the ad-networks. Note that today the Optimal eCPM bars ignores the fill rate of the ad-networks and is therefore better than the achievable eCPM.

In the example below you can see quite a big difference between the average and optimal eCPM. This is likely due to a low fill rate by AdMob.

NOTE: It is wise to ignore eCPM rates when the impression volumes are lower than 10,000. Here this happens on the 5th impression for the Optimal eCPM and on the 9th impression for the average eCPM.

img_3558

Ad Network Comparison section

The last section includes a chart and a table and it visualizes very easily what ad-networks got to serve what impression. The stacked bar chart represents impression volume and each color is a different ad network. The table below the chart shows the eCPM paid by each ad-network for each impression count.

In the example below, you can see that Chartboost and Admob split the first impression 50%-50% but Admob part is declining quickly and almost disappears by the 5th impression. The table below the chart shows that AdMob eCPM is much higher compared to Chartboost for this segment – which bags the question – why is AdMob not serving more ads. This could be a result of low fill rate by AdMob or bad choices made by the mediation.

img_3559

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App Monetization

deltadna_publishers_are_unsure

A few days ago DeltaDNA released a survey about in-game advertising. The company collected responses from mobile game publishers about 11 questions and compiled a 25 page report showing the results and suggesting analysis for some of them. One of the most interesting findings is related to the confidence of game publishers in their in-game ads strategy.

In-game advertising is confusing many publishers

The report asks game makers to estimate how certain they are that they are taking the optimum appraoch about their in-game monetization. The results presented in the table below shows that most publishers are actually uncertain of their in-game ad strategy.

Chart visualizing the answer to the question - how certain are you that you are taking the optimum approach towards in-game advertising.

Image Credit: DeltaDNA

Publishers targeting casual players are less confident

The report later goes on and finds which publishers are more certain and which ones are less certain. It compares between different target audiences and finds that publishers who target casual players tends to be less confident about their in-game ads when compared to publishers who target mid-core and hard-core players. One way to explain this is that companies who target mid-core and hard-core players tend to have more resources and access to data.

Small publishers are unsure of in-game ads strategy

The report analyzes the confidence levels in small publishers vs. bigger publishers and finds that it is the smaller ones who are less confident. Small publishers usually don’t have access to premium advertising measurement tools such as SOOMLA Traceback.

 

If you want to gain more confidence and start being data driven about your in-game advertising. Check out SOOMLA Traceback – Ad LTV as a Service.

Learn More

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Fun Stuff

top 15 mobile advertising jokes and memes header image featuring conspiracy keanu

During Casual Connect Tel-Aviv that took place last week I had a funny conversation with some colleagues about ad mediation. I explained a game developer that the ad-mediation don’t know in advance how much an ad-network will pay for the impression since the payout is determined only later based on the performance. His reaction looked very much like Conspiracy Keanu Meme Character and inspired this post.

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App Monetization, Resource

top_10_resources_for_understanding_customer_lifetime_value

Customer Lifetime Value is one of the most important metrics to track for every service oriented business including free mobile apps, websites, free to play games, SAAS, music subscriptions, phone subscriptions and many others. There  is still, however, a lot of uncertainty around LTV calculations, how to use it and where to find the data to feed the formules. This post is trying to sort through the life time value maze.

Customer Lifetime Value Resource List:

1) Who Should Track LTV and Who Shouldn’t

Published: May 25, 2016

There are many resources showing how to calculate ltv but it’s really not for everyone. Some apps and websites should use other models.

 

img_33462) 6 Customer Lifetime Value Calculators

Published: Apr 26, 2016

6 calculators showing how to calculate customer lifetime value. Full explanation and links to additional resources such as spreadsheets and excel files. This is a great resource for anyone who is a beginner in LTV. Bookmark this link for future use.

3) Calculating LTV for Your First UA Campaign

Published: Jun 21, 2016

How do you calculate Life Time Value (LTV)? There are a number of formulas circulating but how do you know which is the right version for you? Here’s some tips and pointers that are specifically targeted for publishers are measuring LTV for UA campaigns.

4) 7 Analytics Platforms with Built In LTV Reporting

Published: Jun 30, 2016

Some analytics platforms offer LTV reports and also prediction of the lifetime value. This research brings together 7 platforms to consider and provides details about their LTV models and the pros and cons of each one.

5) Calculating LTV with Google Analytics

Published: Jun 8, 2016

Google Analytics for mobile apps doesn’t show LTV. These slides explains how to retrieve retention numbers and find the DAU in the Google Analytics screens. The slides show how to feed the data into an online calculator to get the LTV prediction.

5 surprising facts about customer lifetime value of mobile games6) 5 Surprising Facts about Customer Lifetime Value in Mobile Games

Published: Aug 7, 2016

LTV – life time value is a key metric many mobile game publishers follow. Here are 5 things that even some of the experts didn’t know.

 

7) Calculating LTV for a Mobile Game – Methosd for Different Stages

Published: May 9, 2016

Calculating LTV for a game in design phases is different compared to the soft launch phase which is again different from the lauch phase. This post describes the calcualtion in different phases and suggests additional resources such as CLV calculators.

8) Flurry Analytics – Calculating LTV (Slides)

Published: Jun 6, 2016

App developers who use Flurry analytics have hard time getting their LTV – user lifetime value. This presentation shows how to do it easily with a free online calculator. The first section is showing how to retrieve retention and DAU data from flurry dashboard and the second part explains how to use the calculator to ge the results.

 

9) Apple Subscription Model – LTV Formula

Published: Jul 5, 2016

Calculating LTV for apps that use the apple subscription model. This post includes a formula as well as a calculator.

 

10) Easy to use Customer Lifetime Value Spreadsheet 

Published: Sep 11, 2016

Our best LTV model brings together simplicity and accuracy. Input only 6 parameters and get d60, d90, d180 and d365 LTV results.

 

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Fun Stuff

Top 10 Donald Trump Games header image - the man himself over a mountain background

In a previous post we discussed the shift towards IP based games and mentioned that there are some opportunities for smaller studios to leverage some free IP if they stay alert and move quickly. Politics is one area that provides free IP and especially this is true in an election year. Donald Trump, however, brought this to new levels. Never have a candidate generate so many scandals and gained such a big portion of the media time. For game studios, Trump is the gift that keeps giving. At least until the elections. We collected the 10 most popular Donald Trump games from the app stores and are presenting them here.

From a commercial perspective, we don’t have full data on these Donald Trump games but at least the first three should be doing well based on the number of ratings they received. My bet is that the Trump vs. Hillary slots game is the best one in terms of monetization. Slots normally do well in terms of monetization but this one specifically was made by Super Lucky based on their existing Obama Slots game which indicates previous success with this IP recipe.

Donald Trump games – counting 10

Icon Name Publisher Game Links User Ratings
Punch the Trump game Punch The Trump Brutal Studios Android     iOS    Google Play: 135,672
Turmp vs. Hillary Slots game Trump vs. Hillary Slots Super Lucky Android     iOS    Google Play: 27,189
Another Donald Trump game Trump Dump Day Dream Android     iOS     Google Play: 14,505
Campaign Clicker featuring Donald Trump and Hillary Clinton Campaign Clicker Spring Loaded Android     iOS     Google Play: 9,839
Runner game where Donald is the running character Trump on the Run Squad Social iOS    iTunes: 1,980
Candidate Crunch by Game alliance feating the 2 candidates: Donald and Hillary Candidate Crunch Game Alliance Android     iOS      Google Play: 1,959
This Donald Trump game focuses on his days as a Tycoon - it's a variation of a clicker game where you endlessly collect money Trump Tycoon FME Fun Free Games Android     iOS     Google Play: 1,068
Trump wall game icon Trump's Wall The BLU Market Android     iOS     Google Play: 943
Icon of the game Gravity Trump Gravity Trump Hoodclips Android     iOS     Google Play: 546
Trumpy wall is using pixel art like Crossy Road and other copycats of that game Trumpy Wall Harambe Corp Andrdoid Google Play: 257

If you are interested in additional top 10 game lists from SOOMLA you should check out:

 

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Analytics, App Monetization

Header image showing how complex it is to a/b test your ad based app monetization

A/B testing has been an integral part of marketer toolbox for a good reason – it takes a great deal of the guess work away from marketing. In online and mobile companies it also became a popular tool for product managers. Every time a new version is released, why not a/b test against the existing version and make sure nothing got broken. In mobile app monetization, however, this tool is not available.

Why ad based app monetization is so hard to A/B test

The core requirement for A/B testing is to be able split your users into two groups, give each group a different experience and measure the performance of each one so you can compare it later. There are a number of tools who can facilitate the split for you including Google Staged Rollout. If you are measuring IAP monetization it’s easy enough to associate purchases to the users who made them and then sum the revenue in Group A and Group B. In ad monetization however, it’s impossible to associate ad revenue to individual users. The ad partners mostly don’t report the revenue in this level of granularity.

Method 1 – interval testing

One alternative that companies have been using is interval testing. In this method, the app publisher will have one version of the app already published and will roll out a version with the new feature to all the devices. To make sure all the users received the new version publishers will normally use force update method that gives the user no choice. The impact of the new feature will be measured by comparing the results over two different time intervals. For example, Week1 might have contained version 1 and week 2 might contain version 2 so a publisher can compare version 1 vs. version 2 by comparing the results in different date ranges.

Pros

  • Very simple to implement – no engineering effort

Cons

  • Highly inaacurate and subject to seasonality
  • Force update method has a negative impact on retention

Method 2 – using placements or different app keys

This is a pretty clever workaround for the problem. Most ad providers has a concept of placements. In some cases, they are called zones or areas but all 3 have the same use – they are planned so you can identify different areas in your app where ads are shown for reporting and optimization purposes. The way to use this for A/B testing is to create a zone A and Zone B and then report Zone B for users that received the new feature while reporting Zone A for the control group. If you are already using the zones feature for it’s original purpose, you might already have zone 1, 2, 3, 4 and 5 so you would create 1a, 1b, 2a, 2b, ….

Of course, if you are using multiple ad-networks you would need to repeat this set up for every ad-network and after the test period aggregate the results back to conclude your A/B test.

A variation of this method is to create a new app in your ad-network configuration screen. This means you will have 2 app keys and can implement one app key in group A and the other app key in group B.

Pros

  • More accurate compared to other methods

Cons

  • The effort for implementing a single test is very high and requires engineering effort
  • Will be hard to foster a culture of testing and being data driven

Method 3 – counting Impressions

This method requires some engineering effort to set up – every time an impression is served the publisher reports an event to his own servers. In addition, the publishers sets up a daily routine that queries the reporting API of each ad-network and extracts the eCPM per country. This information is than merged in the publisher database so that for every user the impression count for every ad-network is multiplied by the daily average eCPM of that ad-network in that country. The result is the (highly inaccurate estimation of the) ad revenue of that user in that day. Once you have this system in place, you can implement A/B tests, split the users to testing groups and than get the average revenue per user in each group.

Pros

  • After the initial set up there is no engineering effort per test

Cons

  • Settting this system up is complex and requires a big engineering effort
  • Highly inaacurate – it uses average eCPM while eCPM variance is very high
  • Can lead to wrong decisions

Method 4 – leveraging true eCPM

This method leverages multiple data sources to triangulate the eCPM of every single impression. It requires significant engineering effort or a 3rd party tool like SOOMLA TRACEBACK. Once the integration of the data to the company database is completed, publishers can implement a/b tests and can get the results directly to their own BI or view them through the dashboard of the 3rd party tool. Implementing A/B tests becomes easy and a testing and optimization culture can be established.

Pros

  • The most accurate method
  • Low effort for testing allows for establishing a testing culture
  • Improvement in revenue can be in millions of dollars

Cons

  • The 3rd party tool can be expensive but there is usually very quick ROI

 

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Industry News, Marketing

Apple search ads - what's the master plan - header image

About a week ago, Apple launched it’s Search Ads platform into existence and there are many app publishers who are trying different tactics to use this medium to their advantage. You can read about some of these here:

Once the dust settles, you might want to ask yourslef what is Apple’s master plan for this? Why is Apple doing it? Here are my thoughts on this subject.

Search Ads aren’t enough for Apple

Apple’s revenue is well over $200B. When they go after a new revenue stream they ask themselves – is this channel going to be meaningful for us. If the channel has the potential of being at least 5% of their total revenue or in other words $10B/year they might consider it. If it’s lower, it might not be worth the efforts and risks. For example, the app-store revenue for Apple were $6B in 2015 but it’s growing fast and are likely to reach $15B in a few years. App-store search ads can’t deliver these revenue volumes on their own. There is simply not enough supply.

Apple is looking at the mobile ad spend forcasts

Some of you might have seen this report by eMarketer forcasting $195B in mobile ad spending by 2019.

Emarketer's forecast showing global ad spend on mobile will reach $195B by 2019

[Image credit: www.emarketer.com]

Everything we have seen until now tells us that this forecast is going to come true. Apple is 75% percent ahead of Google when it comes to App Store revenues but it knows that it’s far behind when it comes to advertising. The Apple ad-network – iAd was shut down as of June 30th 2016 after failing to become a major monetization channel for apps but Apple didn’t give up hope. Their mistake was that they didn’t understand the importance of consistent demand. They were fucosing on bringing big brand campaigns which is important in order to drive eCPMs up and stay competative but without consistent demand, publishers remove your SDK in favor of other SDKs. Apple learned from this mistake.

When Google launched their search ads product Apple were watching and they realized that Google did a sloppy job this time. Unlike Adwords, the Google Play search ads are not available as a market to the public but are instead offered as a managed service through Google account managers. Apple saw this opportunity to create a more appealing open product that allows anyone to set up their own campaigns. They created high demand for it’s search ads and more importantly this time the demand will be consistent.

Apple’s next move is for the supply side

The revenue potential of search ads alone is limited as we mentioned before. The demand is huge but the supply is the problem. Following the moves of other tech giants can give you a hint as to what Apple’s next move might be:

  • Google generated consistent demand with Adwords and then launched Adsense to improve supply
  • Facebook generated consistent demand with Feed Ads and then launched Facebook Audience Network (FAN) to enhance supply.

It’s likely that Apple will try to do the same once they aggregate enough demand for their ad products. They have tons of data about their users so they can offer the same levels of demographic targeting offered by facebook in addition to leveraging search data to indicate interests of users. Since they are the platform owner, developers will trust their SDK and give them a shot again. This time the demand will be consistent and allow developers keep them as part of the monetization mix. It might take a year or even 2 years but eventually this has to be Apple’s plan.

 

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App Monetization, Developer interviews

scoompa

I asked Victor Dekalov from Scoompa to give a short interview. To those of you who don’t know Scoompa, it’s a mobile-apps start up founded by Google veterans. They have a diverse portfolio of popular Android apps that have been downloaded over 200M times.

Q: Tell us a bit about yourself and the company you work for

I’ve been managing monetization and analytics with web & mobile publishers for the past 5 years. I was lucky enough to experience the evolvement of these landscapes first hand. I started by monetizing with premium direct campaigns and evolved with the industry through the rise of programmatic ad channels, to full stack programmatic mediation and content discovery in mobile.

Today I’m leading revenue & analytics at Scoompa – our apps are used by 15-20M monthly active users

Google and Facebook are enough

I asked Victor to describe his mix of ad-types, networks and mediation providers

Victor’s answer:

Up until now our approach was not to over complicate things.

We have simple flows inside the apps that trigger interstitials on natural breaks in the app usage (finished creating doc, etc). We also have banner ads on most of our screens, which in the past few months are being gradually replaced by native ad slots that fit in nicely in the apps’ UI.

We use our own in-house auto-pilot mediation system that optimizes the waterfalls, on a daily basis, separately for each placement and each country.

The vast majority of our ad traffic is split between FAN (Facebook audience network) and Google (Admob, adx) + few more we try from time to time. We consider different ad-networks from time to time but usually the opportunity cost is too high and we stick mostly with FAN and Admob.

Don’t over optimize focus on the big picture

Since ad-operations is a relatively new practice for a lot of mobile app developers. I thought it would be good to learn a bit from the expert. I first asked Victor to describe his day to day and was surprised to learn it’s not so much about optimizing the eCPM (or RPM as he calls it)

Q: What is included in your day to day as the one heading Ad-Operations

Answer:

Since most of our ad-ops are “managed” by our auto-pilot mediation platform, it frees me up to focus more on planning ads related product changes and analyzing their effect. This is key: as ad-ops people we tend to focus too much on optimizing our small piece of the puzzle like increasing RPMs (revenue per mile) at any cost. We forget that this is only the very “end of the funnel”.

You can get more impact by creating better ad interactions that perform better (lead to better conversion but also better UX and retention), working on cross-promotion and general growth, analyzing the usage of features inside the product to identify the best monetization opportunities.

Q: Can you give us an example and tips how to do it?

Answer:

One thing we noticed is that users mindfully CLICK on native ads. They do it intentionally. On the other hand banners, whose CTRs are so low that they might look like “margin of error” more than a KPI. A lot of my role is to find these native opportunities rather than just filling the screen with banners. We found that the best implementation of native ads for us is in a list of discoverable content items. This yielded a steady 2% CTR and 4-5$ RPM in US. Even when having several ads per screen.

Another thing we found is that interstitials, If implemented correctly and with the right partners, can bring higher RPMs than rewarded-videos.

So back to my 2 cents – over optimization leaves you focused on the tiny details. In the long run, being blind to the big picture will always loose you more money than what you can earn by lifting RPM by another 0.5%.

LTV is hard to calculate – power through the excel

A lot has been written about KPIs and the importance of LTV. I asked Victor if he shares the same views and he shared some interesting perspective about the challenges with LTV.

Q: What are the KPIs you are actively tracking and some benchmarks

Victor’s Answer:

ARPU/LTV – this is important since it’s the ultimate monetization goal. For a utility app you should shoot for 2-4 cents in top GEOs and 1 cent elsewhere. At least that’s what we are seeing.

Tip – Don’t give up on ARPU and LTV analysis. In most cases, it’s not easy. You need to work with several data points, they don’t always “speak the same language”, you need to spend hours pivoting and vLookuping in excel…… It’s worth it! You’ll find great insights that’ll contradict your instincts, and in the end you might double down on a BI tool that will help you streamline those analysis.

Impressions per session – we don’t try to optimize this one up but rather balance it – too much ad inventory devalues each impression. Too many ads reduce retention. Those 2 things are hard to come back from, and rather than obsessively optimizing for the upper limit I suggest to use common sense and practice restrain (of course, after you’ve analyzed your range). For us we balance for 1 interstitial per session and 4-6 banners.

RPM/eCPM – did I do something to change performance of the ad format? Is there an industry/seasonal trend? The variance is pretty high between ad formats and GEOs. For USA: interstitials should average 6$, natives should be 2-3$ (for very large native ads can be same as interstitial)

Note that RPMs can change industry wide by 50% from one period to another. It can happen in a matter of days, and can influence just one ad-source and not the other.

Finally, I recommend finding your top 5 GEOs and optimizing them. Even when you have a very diverse WW user base, often 5 countries can account for over 50% of ad revenues. Think about it when you’re spending all your energy on localizing for Africa.

Calculate ROI on new SDKs and factor implementation effort as well

With new tools and ad-network SDKs coming to the eco-system all the time, I had to get Victor’s views on how to find the right partners.

Here are some of his thoughts about tools

I mostly use excel and SQL (workbench) to combine ads data with analytics data from Flurry, Google Analytics, Fabric, etc. Don’t spring for expensive and time consuming BI tools, if you can’t estimate how much ROI you’ll have from such a project.

From previous experience, and if you have the time resource, I extremely recommend creating pipelines to streamline all this data into a unified database (redshift, bigQuery, etc) and using a data querying and visualization tool to give you easy day-to-day access to all your main KPIs. You don’t have to go for the big and expensive solutions (like Tableau), there are great and lean (and MUCH cheaper) solutions out there that will do for 90% of use cases. I recommend redash.io

And about adding more ad-networks

We always want to add more networks to our stack and increase average yield per impression, but we always ask ourselves these questions : can this action improve our LTV by more than X %? Will it be more profitable than investing the same development time in new apps and features? So far the answer is usually “NO”.

Cross promoting is key

Q: What do you think about user acquisition for ad supported apps?

Victor’s Answer

Buying mobile users, for a utility app, hoping to be ROI positive by monetizing with ads, is a dead end. Unless you truly believe that you have a home-run app that just needs some advertising to get discovered and become viral. If you plan on generating an average LTV that’ll be higher than the cost of user acquisition (actual CPI + overhead cost), know that for 99% of use cases it’s just plain impossible

A great publishing business is one that has diverse properties. It can utilize cross-promotion to expose masses of users to new releases, instead of relying on paid advertising or chance.

Q: Any other tips you want to share?

A: Read!

Our landscape is full of people eager to share their experience and results. You don’t always have to reinvent the wheel or try-fail every single idea. On the other hand – be sceptic. Not every blog post is relevant to your business and not every piece of data is objectively true 😉

 

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Industry News, Marketing

img_3345

In the last WWDC, Apple announced that they will be launching Search Ads into the App Store in one of the biggest changes since the App Store was first launched. Later in Jun/16 they also revealed additional details about how the new feature will work. The release date was recently announced as October 5th – today. If you haven’t done anything about it yet, now will be a great time to start moving so you don’t get surprised and might actually benefit from the chaos that will break lose following the launch.

Bidding on brand searches for your own apps

One of the standard practices from search ads in Google will also work well here. Buying the name of your own app as well as small variations and mistakes allows you to to defand against Conquesting.

Conquesting – getting the top search result when the user was clearly searching for a different app than yours


I can imagine that its frustrating to pay for something you used to get for free. However, not doing this will be much more painful as your competitor will conquest your brand searches. In addition, Apple explained that they will optimize the algorithms based on user interaction and relevancy so even if you place a low bid you are likely to win as your app is the most relevant one.

Conquesting your competitor brand searches

The flip side of this is that a few of your competitors will be less prepared and you can catch them off guard to conquest their brand searches. This will give you highly targeted users and can work especially well in genres where apps offer similar services or gameplay. For example: casino games, card and board games, dating apps.

A mile wide and a cent deep

One of the interesting choices that Apple made is not to enforce any minimums on the bidding. That’s right – you can bid as low as $0.01 per click. This calls for a wide net strategy. If you set up enough keywords at $0.01 there will be a period of time where demand is still picking up and it will allow you to get some really cheap installs.

 

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SOOMLA TRACEBACK - Monetization Measurement Platform allowing mobile apps to attribute ad revenue and optimize eCPM levels