App Monetization

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.

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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.

Learn More

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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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App Monetization

Ad_Mediation_Setup_101

The use of ad mediation in mobile apps has become the mainstream in the last few years. One ad-network can’t have demand to give you good fill-rates and high CPMs for all your user segments and all the geographic locations where you have users. In addition, mediation allows you to add more ad-networks and increase demand for your ad inventory. As you might remember from “Introduction to Economics” – with fixed supply and increasing demand the price goes up. If you want to evaluate different mediation platforms check this post comparing 6 different mediation platforms.

Use placements/zones/areas for placements

Most mediation platforms has a feature called placements or zones or areas. This is designed so that if you have different areas where ads are presented in your app, you can give them names when implementing the SDK and later on refer to them from the mediation platform. This has a few benefits:

  • You will be able to see reporting for each placement separately
  • The mediation auto-pilot will optimize for each placement individually giving the optimization more precision
  • When setting up a manual waterfalls you can set specific one for each placement

Use placements for segments, testing, impression frequency and ad types

Advanced publishers often use the placements feature for other purposes to enjoy the same benefits mentioned above. Here are a few ideas:

Segments – Let’s say that you could create a segment of users who respond well to videos it would have made sense to show them only video ads. Alternatively, if you had a segment of users that don’t perform well for CPI campaigns – it would make more sense to create a waterfall for these users that gives higher pirority for brand oriented ad-networks such as Mediabrix and Hyper MX.

Testing – optimizing and trying different things is a standard practice in mobile these days. The only way to test different setups is to differentiate group A from group B by duplicating all your placements and marking them A and B repsectively.

Impression frequency – serving different placement every time an ad is presented to the same unique users can give you visibility into your eCPM decay and allow you to optimize your impression frequency.

Ad types – this one goes without saying. Differentiate your videos from interestitials.

Some of the advanced segments mentioned above requires the use of SOOMLA TRACEBACK which also allows to test different ad setups and monitor eCPM decay without jumping through hoops

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Don’t put all your eggs in the auto-pilot basket

Most mediation platforms have 3 modes:

  • Automatically allowing the mediation platform to select the best provider
  • Manually configuring the waterfall
  • Mixed – manual for the top countries and automatic for smaller ones

The automatic approach means that the mediation platform will check each of the ad networks every day to see which one provided the best eCPM and then prioritize the waterfall based on that number. The problem with the auto-pilot approach is that it is based on yesterday’s data. Moreover, in many cases the lower performing network will get switched to the top of the waterfall due to randomness in small numbers. The companies that make the most amount of ad revenue in the world often takes things into their own hands. You should at least experiment with this approach.

Disable the network that gave you the mediation

Most mediation platforms are owned by ad-networks. This creates an immediate conflict of interest. The mediation should be an unbiased, neutral 3rd party while the ad-networks compete with each other for more inventory. What is a publisher to do? Try to find a setting in your mediation platform that eliminates the owning ad-network from the auction. This way you are still enjoying a free mediation but you can be sure that it’s neutral. Important note – make sure you have a catch-all ad network or an SSP if you use this trick. Otherwise you will end up with a low fill rate.

Multiple CPM floors to force ad-network bidding

This is the most advanced trick in this post and requires you to do some prep work on the ad-networks. Basically every ad-network can apply a CPM floor. Some of them allow you to control this from their dashboard while others have the control on their side so you have to call and ask for it. What you want to do with this setting is to create 2 or 3 CPM floor levels under different App IDs or placements and then enter them into the mediation in different positions in the waterfall. Let’s see this simplified example with 2 ad-networks.

Example of a waterfall multiple CPM floors:

  • 1st priority – network A floor $20
  • 2nd priority – network B floor $20
  • 3rd priority – network A floor $15
  • 4th priority – network B floor $15
  • 5th priority – network A floor $10
  • 6th priority – network B floor $10

This means that if either ad-network has an ad that will pay more than $20 they will get to serve it. If none has it, the waterfall goes down to 3rd and 4th priority and checks if there is an ad that will pay $15 or more and so forrth. This method forces ad-networks into an auction that increases the price you get for your inventory.

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App Monetization

10-Ways-to-Improve-Optin

We already covered the emergence of rewarded video ads in mobile games and the importance of focusing on opt-in ratio to increase the number of first impressions you get.

Opt-In Ratio – the number of unique users who watch rewarded videos in a given day, divided by the number of total unique users in the same day.

List of ideas for improving opt-in ratio for rewarded video

Here you can find a few game design suggestions to improve the number of people opting in to videos. However, we don’t recommend implementing all of them. The way to use this list is to set up a system like SOOMLA TRACEBACK that allows you to measure opt-in on a segment and cohort level and then try different methods for different segments while monitoring the improvement through the system.

1 – Guide users to watch a video as part of the tutorial

The rational here is simple. D0 is the only day where you have 100% retention. Guiding users to watch a video in the tutorial allows you to get a lot of 1st impressions and also teaches users that rewarded videos are an integral part of the experience.

2 – Daily offer that require users to watch a video

Once users come back, a nice way to promote the videos to them is to offer some special benefit, bonus screens or game advancement in the form of a daily promotion.

3 – Surprise box in exchange for a video

Many users like to get a surprise box in the game. Making the surprise box the reward of watching a ad is a great way to incentivize more users to engage with the ads.

4 – Lives for video views

Many games has energy mechanic built into them. It could be lives, energy, fuel or any other resource that is consumed with every attempt of the user. Users who want longer sessions usually have to pay but in many cases rewarded video can be offered as an alternative to payment for the non-payers segment.

5 – Save Me / Revive

Games often offer an option to keep playing from the last point by watching a video ad

In many action or arcade games ther user is able to play until some tragic event kills his character. This is the perfect opportunity to offer him an opportunity to keep playing from the same point in exchange for watching a video ad.

6 – Virtual currency for video completion

Another popular place to introduce the value exchange is inside the store of the game. The user can buy coing with money or he can get some for free if he watches some video ads.

7 – Double virtual currency collection for a limited time

Future play offers a 2x profit doubler for a period of 4 hours in exchange for a video viewMany games have coin collection mechanics into their games. In game design these are known as ‘pools’. The option to double the effectiveness of a pool is very appealing to a player and creates an incentive to watch an ad. Futureplay is one company that did a nice job with this option in their game Farm Away.

8 – Remove other ads in return for video view

Another option for introducing rewarded video is to offer the user an interruption free session in return for watching a single ad. You can see examples of this option here.

9 – Accelerate delivery/building time in return for video

Some games have built in waiting periods. This is specifically true for games in the strategy, simulation and racing genres. In these games the user have to wait for some action to be completed and is offered to pay to accelerate. Another option would be to give him an option to accelerate the action by watching a video ad.

10 – Users that don’t opt-in for videos – show them other ads

The final tip wouldn’t directly improve the opt-in rate to rewarded video but it is important just the same. About 2% if your game users pay for In-App Purchases, A typical opt-in ration might be 25%-40% and games that have optimized their opt-in ratio might get to 80%. This still leaves at least 20% of your users that are not contributing any monetization. To these users, you can show interstitials, banners and native ads to round up the monetization.

If you want to improve your opt-in rate to rewarded videos try these ideas and monitor which one works using SOOMLA Traceback.

Learn More

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App Monetization, Resource

image

There is a lot to be learned from the success of others and this is why case studies can be really effective if they are done well. However, it’s not always easy to find good case studies that don’t have a strong bias and are not too sales oriented. Here are 3 case studies I think any game publisher should read especially as ads are becoming a critical component in the app monetization mix.

Adding rewarded video in IAP heavy games – Next games

Unity did a very good case study with Next Games. The games themselves are high production quality games and one of them is also based on strong IP – The Walking Dead is an award winning TV series by AMC. With this kind of investment you can imagine that the games monetize mainly with IAP. However, Next games made a strategic decision to have rewarded video designed carefully into the game. In Compass Point West – they would have a wagon coming into the screen and offering players an incentive to watch a video. The players loved the wagon and the result is that Next game are seeing an ARPDAU of $0.06 from ads alone without any negative impact on IAP revenue. From an LTV perspective this translates to a full $1 of Ad LTV. So if their IAP LTV is $3, The combined LTV would be $4. This is critical for companies who do paid marketing and trying to constantly get the CPI below the LTV.

Take away points:

  • Video ads that are thoughtfully designed into the game work better.
  • Ads can yield $0.06 ARPDAU and $1 Ad LTV without hurting IAP revenue.
  • For companies who relay on paid marketing an extra $1 in LTV is critical for being ROI positive

Ad supported clicker games can do effective user acquisition

The next case study is also from Unity but it covers a very different type of game. FuturePlay creates clicker games that are relaying mostly on ads. 70%-75% according to Jami’s statement in a Panel from Casual Connect Amsterdam. Here the challenge was different – creating enough monetization to rise above the neccessary bar for paid marketing. Futureplay integrated video ads into the core loop of the game and was able to get as much as 80% opt-in ratio. More over, the combined ARPDAU they reached was $0.15. So roughly 10 cents from ads and 5 cents from IAP on a daily basis. With good retention KPIs such as 40%, 20% and 10% for d1/d7/d30, these $0.15 translates into $2.23 in LTV according to this calculator. This LTV is high enough to allow FuturePlay to do paid user acquisition. So they are an advertiser and a publisher at the same time.

Take away points:

  • Clicker games are a good platform for high paying rewarded videos
  • By integrating rewarded videos well you can reach 80% opt-in ratio
  • Even if ads make the majority of your revenue you can still have LTV as high as $2 and acquire users through paid channels

To read the full case study – click here

Even games with almost no IAP can have ROI positive UA campaigns

The last case study is about Gram Games. The company had phanomenal success with their game 1010 and are pioneers in the new trend of companies who are both a publisher and an advertiser. The case study points out that Gram was able to acquire users at scale with a reported CPI of $1 in US and grow to 20M users world wide. The relatively low CPI is possible for casual titles with wide appeal and a familiar gameplay. To reach an LTV higher than $1 in US, Gram games are relaying mostly on ads as a monetization strategy. The gameplay they created retains users for a very long time and 14% of their users stay after the first month. In other words, their retention is 40% better than 40/20/10 which is considered a very good retention. This means that even with a $0.05 ARPDAU they can get to $1 LTV. While having LTV = CPI is not profitable on it’s own, games like 1010! typically get an organic boost when acquiring users through paid channels.

Take away points:

  • Games with excellent retention can get over $1 LTV from ads only.
  • If your game has wide appeal and super casual gameplay you can buy users at $1 CPI
  • Super casual games with mass appeal often get an organic boost on their media spend

 

If your company has over 15% ad revenue and is marketing the game through paid channels you need powerful ad traceback tools like SOOMLA TRACEBACK.

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Analytics, App Monetization

there are many things even the experts don't know about life time value - here are 5 of them. this image is a header image for the blogpost

You might have heard some industry experts talk about LTV (life time value) and how important it is. Here are 5 things even some of the experts don’t know about LTV.

1 – Life time value (LTV) is not just for marketing campaigns

You might have heard that you need to know your life time value to do marketing. This is correct but there are actually more reasons. The first reason for for calculating LTV is related to the early design phase. Before you even start making the game you should analyze the potential LTV based on benchmarks from similar games. This important for fundraising as well as for choosing the right games to build. The second reason is even more important. LTV is the one KPI that wraps both ARPDAU and retention and it is highly correlated with long term success. By actively tracking LTV your team will be focused on the right thing when making decisions about the game and monetization techniques.

2 – There is no real life time value – only predicted life time value

Knowing the real LTV requires waiting a very long time – technically you will have to wait a lifetime. You can assume some maximal lifetime – in games 180 days and 365 days are common values for the maximal lifetime. These time frames are just too long to make any meaningful decisions about marketing, product or monetization. Lets say you made a new feature and want to know if you should keep it or not – waiting 180 days for a decision is just impractical. Whenever someone is talking about life time value he means the predicted life time value. That’s the only parameter you can actually work with. To predict yours, you can use one of these 6 LTV calculators

3 – You can succeed with low LTV but not with declining LTV

There are successful games with LTVs as high as $20 or as low as $0.3. You can succeed with low lifetime value and many games have – this is especially true if you are able to constantly increase it. However, you can’t succeed if your LTV is declining – it means that something is fundamentally broken with your game.

4 – Most companies have both CPI > LTV and CPI < LTV

LTV has to be greater than CPI! There are a ton of articles that explain that If your get the basic formula right you are golden. In fact, there was even a conference with that name (http://ltvgtcpi.com). In real life however, you can’t be golden in all segments so the trick is more around finding your golden segments and expanding on them. If your app uses ads, you will need to trace ad LTV per segment using a traceback platform.

5 – In successful games most of the life time value is created after day 30

If you build a life time value spreadsheet and play around with the numbers you will soon see that typically the first 30 days contribute between 25% to 50% of the total life time value. Plugging in the known ratios of 40%,20%,10% for d1, d7 and d30 retention shows that the yield in days 31 to 180 is twice as much as your first 30 days. This means that you should invest time in giving your most loyal users reasons to play for a really long time. King has mastered that art well and Candy Crush has 1,880 levels in the game. I’m sure they are working on some new ones as we speak.

Plugging in 40%, 20% and 10% as the values for d1, d7 and d30 retention shows us that only one third of the LTV is generated in the first 7 days.

 

If your game uses ads and you want to track the LTV per cohort, segment and testing groups, you need a traceback platform. Check out SOOMLA Traceback – Ad LTV as a Service.

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