App Monetization

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.

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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 dependent 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 competitive 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 benchmarks post.

Outputs/ 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

We also wrote up a full post on the comparison between ad networks here or download the full comparison spreadsheet below for free.

FREE AD NETWORK COMPARISON SPREADSHEET

 

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App Monetization

network of ad providers participate through multiple hops to create a never ending fill rate of ads in your mobile app

If you have been placing ads in your apps, you have already heard about fill rates and how combining ad-networks through a mediation platform can help you optimize for fill rate.

Fill Rate – the percentage of the publisher ad requests that get a positive response (fill) by the ad network

The math is simple, if you have fillrate below 100%, you are leaving money on the table. Ad-networks have a finite number of advertisers so they will eventually run out of ads to show and so combining a few networks makes sense.

Well…. things change quickly in the mobile ecosystem.

Ad-networks outsource demand

In reality, the situation is a bit different. Most ad-networks have a finite number of direct advertisers but they also make their supply available to other networks. In other words, the ad-network will respond to ad request with their own ads as long as they have them but when they run out they will simply ask other ad-networks if they have ads. If other ad-networks have ads, they will show ads from those networks. This means that the fillrate will be high even for a single ad-network. However, these has some noticable downsides.

eCPM drops when outsourcing demand

What happens when ad-networks are taking ads from other networks? Simple – they are adding more middle mens and more mouths to feed. Let’s say that today you are getting 50% of what the advertiser pays – this is a common situation even if the quoted rev-share is 70% due to ad-network deductions – the standard rev-share only applies when there is one ad-network in the middle. If the ad-network is outsourcing their demand, it means that there are multiple parties that take a share in the middle. If there are 3 hops for example, the publisher will end up getting 12.5% (50% x 50% x 50%) from what the advertiser originally paid.

Adapting to the new situation

It’s pretty hard to tell when an ad-network runs out and starts out-sourcing. However, what you can do is enforce minimal eCPM floors on all networks that you are integrating through your mediation platform. If you enforce an eCPM floor of $1 for rewarded video in US for example, you will start noticing that your fill rate is not 100% anymore on every ad-network. Play around a bit with the threshold to find the right minimum. This will allow you to limit ad-networks to direct relationships. You will also have an indication when they run out – you will see the fill rate gows down. If that happens, this means you need to integrate more ad partners to increase demand. However, you will be integrating them as direct sources and not through a middle man.

Actively tracking your eCPM decay allows you to spot the drops in eCPM that indicates the ad-network is introducing another hop. Check out SOOMLA Traceback to get a grip on your eCPM decay

Learn More

 

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App Monetization, Game Design

Getting your users to day 365 retention is the equivalent of LTV heaven illustrated as a tropical island in calm waters.

I recently attended Pocket Gamer Connects event in Helsinki. It was super productive for us so first of all I should think the Pocket Gamer guys who set this up and the amazing gaming industry in Helsinki. Big shoot out to you all.

One of the panels I enjoyed on the conference introduced Saara from Next Games, Eric from Dodreams and Jari from Traplight. It was called LEARN HOW TO DRIVE PLAYER ENGAGEMENT FROM THE BEST IN FINNISH MOBILE GAMING. One of points raised by Eric was that it’s a great feeling to see players come back after 6 months or 1 year. In fact, it’s not just a great feeling, it also means great LTV. If you followed our 5 things you didn’t know about LTV post you already know that two thirds of the LTV is after day 30. However, games that can keep users coming back at day 365 often find that it’s much more. Losing some users between day 0 and day 30 is natural but if you can keep most of d30 users coming back month over month you will see that most of your LTV comes from those long retained users.

Specific Example:

One of the games I analyzed had 52.9%, 29%, 18% for d1,d7,d30 retention. These numbers are very good to say the least but he still lost 82% of his users. The interesting stuff is what happens after, users keeps coming back and the D365 LTV is almost 2x the D180 LTV. You can run the numbers yourself here.

Here are a few tips on how to get users to Day365:

Tip 1 – show the users something fresh every time

Updates are super important if you want to retain your long term users. Games gets boring fast but if you keep pushing update you can keep users engaged. If your updates follow a consistent schedule you are likely to have users that expect the updates and even complain when updates are delayed. A good example for that is Color Switch – this popular game has very high retention rates. One of the reasons for that is that every time you open color switch there is some new game mode waiting for you. The experience never gets old.

Tip 2 – give your users influence

Some games have ways for the users to create levels and challenge others. This is a great way to retain users and make them passionate about your game. Others don’t have native ways to do it but can still give the most loyal users ways to influence by creating special forums for them and making sure they know their opinions matter.

Tip 3 – make your game endless or close to it

Think about how many levels candy crush has – 2,620. You can play this game forever and yet they are adding new levels. The reason is that if a user ever reaches the last level he will leave for sure. Random games may not need to make new levels all the time but they need to make sure the experience doesn’t become repatitive and that there is enough content to create new variations.

 

If your company has good retention and is monetizing through ads it’s important to know the Advertising revenue per user. Check out SOOMLA Traceback – Ad LTV as a Service.

Learn More

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Analytics, App Monetization, Resource

ltv model

In previous blog posts I posted 6 different LTV calculators and received a lot of feedback about the LTV models. Turns out game publishers found them super useful for calculating the LTV of their game. It was great to hear the positive feedback which also led to a lot of conversations about how people are calculating their LTV. Here are some of the learnings I can share.

Specific LTV model is always better than generic one

All our LTV calculators can’t be nearly as accurate as the ones you can build in-house. If you have the money to hire a data sceintist or at least contract one to build a formula for you after you have gethered some data, you will end up with a more accurate model. The reason is simple, in predictive modeling, the more signals you have the more accurate the model will be. All our calculators use retention and arpdau because they need to be widely applicable. However, there are a lot more signals you can feed to a specific model: tutorial completion, level progress, soft currency engagement, challenges completed, … Factoring such signals would give you a better prediction model. Our generic calculators’ main purpose is to get you started, give you a framework to think about LTV prediction and help you do some basic modeling if you are on a budget.

Simplified spreadsheet modeling

Our original spreadsheet model was taking in 31 points of data. However, after talking with readers I learned that most of you only track 4 retention data points and 1 arpdau point. This is why I created a version that is simpler on the input side. Another feedback I received is that you want more outputs: Day 60, Day 90, Day 180 and Day 365 LTV. Here is the new calculator based on all that feedback.

Inputs:

  • Day1 retention
  • Day7 retention
  • Day14 retention
  • Day30 retention
  • ARPDAU

Outputs:

  • Day60 LTV
  • Day90 LTV
  • Day180 LTV
  • Day365 LTV

Method:

This spreadsheet is the same one from the retention modeling we presented in this post but with a few tweaks.

The actual spreadsheet

 

If you want to measure the ads LTV in addition to IAP LTV you should check out SOOMLA Traceback – Ad LTV as a Service.

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App Monetization

An ad-network employee looks at data from thousands of impressions to calculate the game developer eCPM and payout

If you have been placing ads in your apps, you are not alone. More and more apps are doing so and even the top grossing games have reached 50% ad penetration. Figuring out how your payout is calculated, however, proves to be a very difficult task. The ad-networks report an eCPM level that along side the impression volume makes your payout as shown below:

Basic formula (that is actually wrong):

Payout = Impression volume x eCPM

In reality, the situation is a bit different. eCPM is only calculated in retrospect as the average CPM across all impressions. So the basic formula is actually

The real formula:

Payout = sum(Price for impression1, Price for impression2, ….)

eCPM = (Payout / Impression volume)x1,000

Impressions can have drastically different prices

So what the formula above means is that eCPM is an averaged number. Now, you might remember from your statistics class that average can be misleading when variance is high. Don’t remember? Here is an article to brush up on this topic. Impression payout certainly falls into the high variance category. Let’s see how high the variance is.

eCPM Variance in CPI Networks

What is a CPI network? The following networks pay mostly based on rev-share of their CPI campaigns: Unity Ads, Vungle, Chartboost, Supersonic/Ironsource, Applovin, Adcolony, Tapjoy. In these networks, the CPI variance is especially high. This is a typical distribution of the actual prices:

Price Proportion of impressions that pay this price
$0 99%
$500-$1,000 0.2%
$1,000-$2,000 0.4%
$2,000-$3,000 0.2%
$3,000-$4,000 0.2%

If this seems a bit high it’s ok. We were also surprised but the simple reason is that eCPM is 1,000x than the price of the impression. $4,000 eCPM really means that there is an impresison that paid $4. This is simply the impression that led to the install. $4 is not a very high price for an install after all, there are much higher prices.

Variance in bid levels in RTB

If you are using an SDK from Mopub, Inneractive or Smaato, it means that each impression is getting a differnet price based on an auction. The bid levels here also vary drastically. In some cases you can see bids of over $100 when a user is being retargeted and in some cases the bid levels are at $0.05 since no one is bidding on that user except for a scavanger that bids the minimum on everything and catches the leftovers.

What about Facebook Audience Network and Admob

Facebook and Admob both operate an entire eco-system. Each of them have both CPI campaigns that are paying per installs or at least optimizing per installs along side retargeting campaigns and other forms of demand. The result here is also the same – big variance in eCPMs when you look at specific impressions and one single avarage eCPM reported for all the impressions.

Why should you care about variance in eCPM

If you are basing any business decision on ROI calculation that uses the avarage eCPM, you should care about eCPM variance. Trying different App Icons and App names, new features, traffic sources, monetization setups. These are all decisions where you try to measure the ROI. If your ROI calculations uses the average eCPM to estimate ad revenue as part of that calculaitons – it’s likely that you are making the wrong decision.

If you want to know the eCPM of every single impression and make better ROI calculations you should check out SOOMLA TRACEBACK – Ad LTV as a Service

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