App Monetization, Resource, Startup Tips

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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.

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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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.

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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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Marketing

imageAbout a week ago I received a question from a friend and I thought the answer could be useful for a few others as well. As a media buyer in one of the leading mobile game publishers he is working with different DSPs and from time to time he is using lists of IDs and publishers – both whitelists and blacklists. “How can I be sure that other mobile game publishers don’t get access to my lists once I give them to the DSP provider?” he asked.

His concern is very easy to understand. Often whitelists are used in retargeting campaigns and contain a list of the most valuable users that ever played the app. Blacklists on the other hand often contain a list of all the users who have the app installed so you can exclude them from your campaign. Either way, these are very sensitive lists – the damage of having them fall into the wrong hands could add up to millions of dollars.

The answer has 4 parts:

1 – Legal – contract with real teeth

The standard agreements obviously have some sections that say “vendor will use his best efforts to protect….” or something similar. This is pretty loose and might not do the trick for you. As a game publisher I would suggest a clear language that places blame on the DSP in case of leakage and names severe penalties. In combination with the other tips below this change will go a long way towards ensuring your data is pretected. Another aspect of this is to verify that the provider has assets you can sue against. The threat of a penalty will be less effective against a provider with nothing to lose.

2 – Prefer DSP platforms with no conflict of interest

Some DSPs are hybrid DSP companies that play a dual role. With some companies, they are simply a platform provider that don’t have a stake in the game but with others they are providing agency service on top of their platform and are measured by the success of their campaigns. When acting as an agency for your competitor, they are actually creating a conflict of interest with your company if you are only using their platform. I would recommend sticking to the pure platforms that don’t engage in agency services.

3 – Programatic data handling creates consitency

Some DSP providers have tools built into their platform to manage lists. These tools allow you to create a list, delete it or modify it. Other providers manage lists manually. There are 2 main differences:

  • Software tools are more consistent than manual handling – this means that leakage either never happens or happens all the time
  • When using software tools only a handful of people in the company has the access or knowledge to extract the data and they are usually not the same people who might have their incentives aligned with your competitors

4 – Set traps or at least pretend you did

You can easily add a few known IDs into your lists. If these device IDs will start showing irregular activity from competitor campaigns you would know that the list leaked. All you have to do is monitor these IDs in one of two ways:

  • By having the actual device and checking what ads you receive in a test app
  • By requesting bids from different exchanges for these device IDs

If you are also following the advice in #1, it might be enough to pretend you have such a system to make the DSP provider think twice before sharing any of your lists.

Combine these methods for maximal impact

Having the teeth in tip #1 is very effective if the provider know you will be monitoring as explained in #4. If the provider is handling lists manually as explained in #3 he will be hesitent to sign up for penalties explained in #1. These methods work much better if you use all of them and there is no reason why not doing so. Combine multiple layers of defense that re-enforce each other.

 

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Industry News

Header image - new breed of companies get 7 funding deals in 2 months

In a previous post we presented 3 case studies starring a new breed of game companies. Companies who are both publishers and advertisers at the same time. These companies have in-game advertising as a significant part of their monetization strategy but can still see high enough ARPDAU and LTV to be able to effectively do paid user acquisition campaigns.

This summer we have seen this new breed of companies catching fire with 7 of these companies getting additional funding or engaging in M&A activity for a total amount of $300M. The market is telling an interesting story through these acquisitions. Companies who can effectively trace-back their ad revenue and manage their monetization smartly can often reach monetization levels where they can show ROI on their marketing spend. Moreover, by demonstrating their ability to be on top of their moneitzation, these companies are able to convince investors that they will be able to scale fast with additional funding.

 

If you also want to stay on top of your ad based monetization and be able to show investors that you know how to smartly invest your UA budgets you should check out SOOMLA TRACEBACK.

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Studio Press release Date Transaction Amount 
futureplay logo - the company is pioneering a new way to monetize they are calling view to playFutureplay  PR Link  Aug 25th 2016 $2.5M
huuge games logo. The company CEO commented in a panel at casual connect that the company is making 50% of their revenues from advertisingHuuuge Games  PR Link  Aug 4th 2016 $4.6M
Rocket games logo. The company was recently acquired by Penn Nat'l for $170MRocket Games  PR Link  Aug 3rd 2016 $170M
Scopely recently announced a funding round of $55M. Both their hit games utilize ads for monetization while investing heavily on paid UAScopely  PR Link  July 26th 2016 $55M
Flare games recently bought Kopla games following the success of their hit Nonstop KnightFlare games  PR Link  Aug 2nd 2016 Undisclosed
Tinyco was bought by SGN for an undisclosed amountTinyco  PR Link  July 75h 2016 Undisclosed
Firefly recently announced a funding round of $10MFirefly Games  PR Link  Aug 11th 2016 $10M
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Marketing, Open Source, Plugins

How we got 8500 apps to install our SDK. Tips for marketing your SDK.

As of last week, the number of live apps using the SOOMLA open source framework is 8,500. While the framework was not a commercial success and is no longer the main focus of SOOMLA it is still a big achievement and often people ask us – how did you do it. It is very common to hear these days terms like SDK Fatigue and here stories about how no-one likes to install new SDKs. SOOMLA’s framework miraculously made it’s way to a huge number of apps including apps by very big publishers: Disney, SEGA, Gumi, Kabam, Ketchapp, Playlab and Scopely. Here are some of the secrets behind it’s success.

Tip 1 – If you want to be in the rocket, get in before the launch

Many mobile games follow the pattern of a rocket. There is a ton of work done before the launch but once it’s launched it’s very hard to make changes and bring in new passengers. A company that just launched an app has so many things they need to do and everything is critical that your SDK will never get enough priority to get included.

Following the footsteps of giants like Unity and Cocos2d-x, SOOMLA realized that for the SDK to reach massive distribution it needs to be included in the first build. The way to do that is to solve a problem that saves the developers development time.

Tip 2 – Open source and app development shops

Another thing that worked to our advantage with the SOOMLA framework is that developers like open source but even more – app development shops likes open source. There is a surprising amount of apps that gets outsourced to 3rd party development shops. No one tracks how many exactly but there are pretty significant app publishers that outsource as a philosophy. Other publishers do it from time to time. For app development shops there are many advantages to using open source projects. It’s free and it saves them time and development effort which are the two most precious things in an outsourced software project. Once our framework made it’s way to the hearts of the app development shops it started getting included as a default in all the apps that were made by that shop.

Tip 3 – The Unity Asset Store + 13 Tips in one free eBook

In August-2014 we uploaded the framework as a plugin to the Unity. Since the plugins were downloaded close to 20,000 times and the number of apps that use the framework grew 2.5 times. Successfully publishing a plugin in the Unity Asset Store is an art on it’s own – if you are serious about it – download this free eBook – The Unity Asset Store COMPLETE Publisher’s Manual

free ebook - the unity asset store complete publisher's manual

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Analytics, Marketing

Kongregate's recent blog post suggests that you can double your traffic by tracing your ad revenue

I recently came across a fantastic post by Jeff Gurian. Those of you who don’t know Jeff, he is the Director of Marketing at Kongregate. In his post he brings up a super important point – you can double your traffic by Tracing the Ad LTV or “counting the ads” in the language of the article.

Doubling your traffic only takes a 25% increase in LTV

According to Kongregate’s experience with user acquisition, Jeff explains, the correlation between how much traffic you can get and the bids you place is not linear but rather a power function. “There is always a tipping point where your traffic will increase exponentially relative to the increase in your bid.” says Jeff.

The chart in the post does a good job in explaining this point:

chart illustrating the power curve of the impression volume you can get at different bid levels

Image from original article at Kongregate developer blog

In this example – acquiring traffic with bids of $12.5 as opposed to $10 will allow you to get twice the amount of traffic. In other words, a bid increase of 25% transatles to a volume increase of 100%.

Tracing Ad LTV allows more room in your CPI bids

Not all games have ads but the ones that have added in-game advertising are seeing between 10% to 80% of their revenue coming from ads. 25% is a typical scenario in many games and is also close to the ratio reported by public companies such as Glu and Zynga. The example given in the article (see image below) is showing that tracing Ad LTV can modify your ARPU / LTV analysis by 25%-30%. As we know, higher LTV means that we can afford to pay higher CPI which leads to twice as much traffic per the explanation above.

Illustration of LTV and ARPU calculations with and without tracing-back the ad revenue

Image from original article at Kongregate developer blog

Let SOOMLA do the work and get you the accurate Ad LTV

Many companies skip the Ad LTV since the process for calculating it is often complicated, time consuming and in many cases it is not accurate enough. Their claim is that none of this matters if you are miscounting your Ad LTV. Counting impressions can lead to significant errors in LTV calculations which means your ROI analysis can be off and end up losing money for the company.

Fortunately enough, SOOMLA has developed a solution that automates the Ad LTV calculation and we do that with much greater accuracy so now you can enjoy the benefits of Traceback and double your traffic without worrying about accuracy or extra development effort.

To save valuable resources and ensure you are getting the Ad LTV correct for every cohort you need a specialized system like SOOMLA TRACEBACK. The platform traces the ad revenue and sends it to your attribution partner or in-house BI.

Learn More

 

 

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Analytics, Marketing

BiggestMistake_in_Ad_LTV_Calculations

Recently I became aware of game publishers that implemented an in-house solution for Ad LTV tracing but were doing a huge mistake in how they think about ad revenue. We all know that any LTV calculation has 2 main factors:

  • Retention
  • Revenue
The Ad revenue is the factor that companies get wrong when they build in-house solutions for Ad LTV tracing. These solutions often assume that each impression pays the same level of CPM. This is a huge mistake that can lead to errors in orders of magnitude and ROI calculations that are way off.

If this is how your company calculates Ad LTV you should read the following examples carefully.

Example 1 – The Rewards Collector

  • User played during the first month and never came back after.
  • Watched 50 rewarded video ad impressions from Vungle – didn’t click or install any ads.
  • Average eCPM for this month from Vungle $15
Ad LTV Based on Impressions The True Ad LTV Error
$0.75 $0 $0.75

This type of error could lead the UA teams to a false positive ROI calculations. The UA team thinks the ad spend on this user is ROI positive while it’s actually a losing buy.

Example 2 – The Ad Whale

  • User played 5 days during 2 weeks
  • Watched 10 interstitial ads from AppNext, clicked on 2 and installed a Match-3 game and a Strategy game
  • Average eCPM reported by AppNext for those days – $5
  • CPI for that Match-3 game – $2, CPI for the Strategy game – $5
Ad LTV Based on Impressions The True Ad LTV Error
$0.05 $2 $1.95

Here the ROI calculation could be false negative. The UA team will stop buying these type of users since ther reported Ad LTV is $0.05 while it’s actually $1.95 and the buy was actually a good one.

Example 3 – The Retargeted User

  • User played 10 days during 1 month
  • Watched 20 video ads through Inneractive
  • Average CPM reported by Inneractive for those days – $5
  • This user was a whale in Game of War and was part of a retargeting campaign so specific CPM bids for that user were high – $80 x 4 ads, $90 x 2 ads, $100x 8 ads, $110 x 2 ads, $120 x 4 ads
Ad LTV Based on Impressions The True Ad LTV Error
$0.10 $2 $1.9
The ROI calculation in this example is also likely to be false negative. The UA team might think this was a bad user to bring to the game although his Ad LTV alone was $2.

 

If your company needs to calculate Ad LTV you should try to avoid these costly mistakes. Check out SOOMLA Traceback – Ad LTV as a Service.

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