App Monetization

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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Analytics, App Monetization

Podcast - The role of analytics and data in ad based monetizationThis is a recent interview I gave at Cranberry Radio. I’m talking about the following topics:

  • The role of unbiased measurement companies on the advertiser side and the publisher side
  • Insights that SOOMLA have seen by measuring ad-revenue to an unprecedented granularity level
  • Insights from our latest study – Rewarded Video Ads Retention Impact in Match 3 Games

You can listen to the podcast here.

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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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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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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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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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App Monetization, Resource, Startup Tips

img_3323

Many app developers are looking to project their app revenue as they are starting out. This is obviously an important question as publishers needs to justify the development effort in their mobile apps. The answer is dependant on many parameters and quite complex to answer. However, we identified the key drivers and created the following calculator for you. Below the calculator you can find explanation of all the fields.

 

FREE REPORT – VIDEO ADS RETENTION IMPACT

 

Mobile App Revenue Calculator

Calculator Explanation and Fields

The calculator has 4 parts:

  • Retention Inputs
  • Monetization Inputs
  • Traffic Inputs
  • Output / Results

Retention Inputs

Here you will need to input your D1, D7, D14 and D30 retention figures. If you are unclear about how to do this we highly recommend checking these guides for getting your retention in Flurry and GA. Even if you are using another analytics platform it will give you a sense of what you are looking for. In short d1 retention is how many users played your game 1 day after the first day they played. If you had 1,000 users that downloaded your game and played it in one specific day and the next day 350 of them came back to play again your D1 retention is 35%. One popular benchmark in the mobile games industry is 40%,20%,10% for D1, D7 and D30 retention – this is considered good retention and not all games get there.

Monetization Inputs

In order to calculate your app revenue, we need 2 types of monetization inputs: IAP and Ads. For In-App Purchase (IAP), the two input fields are:

  • Conversion rate to payers
  • Average Revenue Per Paying User (ARPPU)

The ARPPU is impacted greatly based on what type of app you have. Some apps and games simply sell remove ads for $1 and then the ARPPU will be $1. Strategy game apps on the other hand allow the user to build armies and castles by buying virtual currency and create a competative state that can lead users to spend hundreds of dollars in the app.

On the Ad side of things, you will need to enter the following fields:

  • Opt-in ratio to rewarded video
  • Number of rewarded videos you expect your user to see in a typical day
  • Number of interstitials you expect users to watch in a typical day
  • Number of banners per day

Note that most apps don’t have all 3 at the same time. If your app doesn’t have one of these ad types, simply put 0 (zero). The opt-in ratio to rewarded video is a tricky one if you haven’t started out yet. Consider these values:

  • 10% if you are hiding it inside the virtual goods store
  • 20% if you are prompting users in a specific situations as they run out of a resource
  • 40-60% if you are going to measure and optimize on this parameter and have multiple prompts on a daily basis

If your app have interstitial videos as opposed to rewarded videos, just treat them as regular interstitials.

Traffic Inputs

Another critical set of data for calculating the app revenue is how many users you will have. You will need to enter the expected number of downloads per day and the traffic mix between Tier 1 (US, UK, CA, AU, DE, FR, NO, FI, SE) and Tier 2 countries. If you are unsure about the values for these fields or other fields in the calculator you can also check out our game benchmakrs post.

Ouputs/ Results

Here you will find the estimated daily app revenue alongside other results:

  • User life days – this is how many days the average user will play in your game over his entire life
  • DAU – how many users open your app on a daily basis
  • Tier1 ARPDAU – the daily revenue per active users for tier 1 countries
  • Tier2 ARPDAU – the daily app revenue per user in tier 2 countries
  • The ratio of ad revenue out of the total

 

 

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SOOMLA - An In-app Purchase Store and Virtual Goods Economy Solution for Mobile Game Developers of Free to Play Games