Analytics, Announcement, App Monetization, Resource, Tech Resources

Optimizing Price Floors Boosted Revenue by 30% – Case Study with Tripledot

A case study with Tripledot on how they boosted revenue by 30% by optimizing price floors

We just released our latest case study with Tripledot – showing how by optimizing pricing floors they were able to boost revenue by 30%. We break down step by step what actions were taken by Tripledot as well as an overall look at the industry guidelines / limitations for setting up pricing floors.

You are welcome to download the report through this link.

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Analytics, Announcement, App Monetization, Resource, Tech Resources

Acquiring Ad Whales with Facebook’s Lookalikes – Case Study with Nanobit

A case study with Nanobit and how they acquired more ad whales with Facebook's lookalike campaigns

We are excited to showcase Nanobit in our recent case study on acquiring ad whales via Facebook’s lookalike campaigns. Ad whales today are responsible for the majority of ad revenue generated in mobile apps today and Nanobit has been leveraging SOOMLA’s platform for some time to identify and acquire more. This case study gives an in-depth look at the steps Nanobit took and the benchmark-breaking results that followed.

You are welcome to download the report through this link.

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App Monetization, Game Design, Marketing

15 Types of Hyper Casual and Arbitrage Games

The 15 types of hyper casual and arbitrage games for mobile apps

One of the biggest trends in the mobile game industry in the last year has been the explosion of the Hyper Casual genre. The acquisition of Gram Games by Zynga for $250M and Voodoo’s $200M funding round brought these type of games to the center of the stage.

The historical hierarchy in the app ecosystem

When we look back at the evolution of the app eco-system we can see that app monetization has shifted through 3 main phases:

  • 2007 – 2012 – Paid apps
  • 2013 – 2016 – Free apps with mostly In-App Purchases and some ads
  • 2017 – Present – Free apps with mostly Ads and some In-App Purchases

So if we look at the app economy in the past 5 years, free apps ruled the charts, and the grossing chart was dominated by apps who monetize exclusively with IAP while the top downloaded charts included mainly apps who monetize with ads.

When it comes to user acquisition and marketing, however, the only Apps that could afford it were the top grossing apps – the ones monetizing with In-App Purchases. In other words, the following hierarchy existed:

  • In the top – users pay money to the grossing apps in return for in-game goods
  • In the middle – The grossing apps were paying money to the top downloaded apps in exchange for qualified users
  • At the bottom – the top downloaded apps were getting users who organically discovered them via search, chart position and featuring.

Changes by Google, Apple and Facebook set the stage

In the last 18 months we saw a big change in the industry. Some refer to it as the Hyper Casual trend but it’s actually bigger than that. Here is the change in each one of the areas:

  • In the top – more users are willing to pay and grossing games improved at monetizing payers
  • In the middle – the increase in the top grossing apps along side increased demand from brands for mobile inventory created inflation in price of ads – per impression and per user.
  • At the bottom – the top downloaded apps experienced a few changes in how they acquire users:
  • Both Apple and Google introduced paid discovery into the app store and are gradually making it harder for apps to get free discovery without paying for it. The most recent example for this was the change in Google’s algorithms that put many indie developers out of business.
  • The growth in Instagram ads alongside the introduction of Facebook Audience Network as and Facebook’s recent focus on better user experience improved the chances of apps with wide appeal to receive advertising placements even though the price they can pay for users is a lot lower compared to top grossing apps.
  • The change in the top and the middle sections of the pyramids increased user value for the top downloaded apps and created a situation where these apps can afford to acquire users via paid channels

The emergence of Hyper Casual and Arbitrage Games

These changes set the ground for the emergence of Arbitrage Games. Some people call them Hyper Casual Games but actually some arbitrage games are just good old casual games and in general the main difference with this trend is not the game genre but actually the business model. Hyper causal games existed way before 2016 and you can be sure that games with jumping balls were not invented by Ketchapp and Voodoo. That part that is new about these games is the business model – or the fact that hyper casual games even have a business plan. This business plan can be summarized with one word – arbitrage. The idea is simple:

  1. Acquire a user for X cents through advertisers
  2. Make sure user sees enough ads to generate Y cents where Y is bigger than X

Usually the number of ads a user needs to see in order to pay for his acquisition cost is about 100 if we are talking about full size interstitial ads that usually contain un-skippable videos and playable ads. This numbers goes to 2,000 ads if we are talking about banner ads. These numbers are based on the following assumptions for US traffic: $1 CPI, $10 interstitial eCPM and $0.5 banner eCPM. In other countries the numbers might be different but the ratios remain.


In most cases it’s not a single ad format but rather a combination such as 500 banner ads and 75 full size ads. If these numbers sound crazy to you, it’s because they are. No game designer goes and designs a game thinking there will be so many ads in it and when companies look at their own games it’s often hard to get comfortable with the amount of ads they have.

The popularity of these games created a growth in the amount of ad inventory which is filled mostly by ad-networks who quickly captialized on this trend and are creating in-tier transactions where on top-downloaded type app is being promoted in an ad that shows in another top-downloaded game.

15 Types of Hyper Casual and Arbitrage games

Below you can find 15 types of games who do well for arbitrage business model. Here they are – divided into 3 main categories.

Brain teasing games

1 – Word creation games
These are games where you create words based on a limited set of characters and clues related to the word length and sometimes pictures. Typically these games make at least 50% of their revenue from ads. Here are some examples in Google Play

2 – Solitaire
The well known card game became super popular since Microsoft included a free version called Microsoft Solitaire in different Windows versions starting Windows 3.0. In their mobile version these games tend to be completely ad driven with no IAP at all. They also tend to enjoy very long retention and players might come back to it even after months of not playing. Here are some examples of Solitaire Apps

3 – Jigsaw
Jigsaw puzzles existed since the 18th century where they were actually made using a Jigsaw to create the puzzle shapes. In their mobile version they attract users who want to relax while teasing their brain. Typically these games monetize with a mix of ads and in-app purchases but tend to be slightly heavier on the ads side.

4 – Soduko
This combinatorial puzzle game was made popular in the current version by Japanese puzzle company Nikoli but versions of it actually appeared in French newspapers 100 years before that. Mobile versions of this type of game usually do well with ads partly due to long session times and great retention. The typical monetization mix is over 90% in favor of ads. Here are some examples on the play store.

5 – Trivia games
Trivia games require users to demonstrate their knowledge in a variety of categories and do so under time pressure. The main monetization in these games are ads and they usually do well enough to also invest in user acquisition. Here is one Trivia example.

6 – Word search
Mobile word search games are the digital version of a popular puzzle that existed in printed version for about 50 years. These games tend to monetize mostly with ads and enjoy strong retention which allows high enough ARPU for UA. Here are some examples of mobile word search games:

7 – Mahjong and other tile games
Mahjong is a tile game that was developed in China. It had digital version for PC and in recent years was adapted to mobile as well. These games typically do well with ads and generate over 50% of their revenue with this channel. Here are some mobile Mahjong Examples.

8 – Other Card GamesYaniv is card game - this game type tend to do well with advertising
We covered some popular card games such as Solitaire above but there are more card games and many of them do well with ads. Well enough to allow for arbitrage and paid marketing. Some examples include: Uno, Canasta, 29 and Yaniv (yes – there is a game with my name and no – I didn’t invent it). Here are some examples from the Play store:

9 – Other “Real World” games
You may have noticed a trend that many of the games that do well with ads are real world games. This pattern can be extended into more types of games. Games like monopoly, mazes, number riddles, etc. tend to do well with ads and can fit in the category. Here is Monopoly for Example

Hyper casual games

10 – Games With Balls
This a rather broad category that features a ball as the main hero character and almost no meta game whatsoever. The games are typically hyper casual and can be played with a single finger and typically only one control action – tapping. The retention curve on these games is not very good so the game has to feature many ads as early as possible via multiple formats. In many of these games a big driver is a “save me” feature in return for watching videos. Here are some examples on Google Play.

11 – Coloring Books
This genre received great traction over the last 12 months with the introduction of pixel coloring books. In these apps the user colors by numbers where each tap fills out a single pixel and after filling out hundreds of pixels he can zoom out to the see the full picture. These apps enjoys good retention and long session times. Prior to the pixel painting, there were similar apps where the user would fill out areas.
Here are some examples:

12 – Piano Tile Games
Piano tile games feature a game play that is somewhat similar to popular console game – Guitar Hero. The piano tile mechanic is much more simplified and has less to do with the music of the song. Piano tile games are quite addictive and enjoy nice retention and session times. They tend to generate 95% of their revenue from ads. Here are some piano tile games Examples

13 – Io games
IO games are easy to recognize as they end up with io suffix. The game that started the genre – was available on mobile and also via the domain Other games in this genre copied the name format in addition to the game mechanics. The games usually have a simple look and users in them chase each other in an eat or be eaten arena. This game type tend to be mostly ad driven. Here are some examples of io games

Other games

14 – Idle games
These games could be quite fascinating if you weren’t exposed to this type before. The user earns in-game coins mainly by tapping on the screen or simply by waiting. These games do well specifically with rewarded videos. Here is a list of Top 10 Idle games As well as some other example from Google play:

Idle Games

Clicker Games

15 – Play to Win Prizes
This is not exactly a game but rather a combination of 1 rewards app and a portfolio of games. The users are generating revenue for the publisher by watching ads but on the other hand, they can gain real life rewards for playing games. Here are 2 examples:


If you have more examples of game genres that do well with ads and can work with an arbitrage business model we would love to hear about them. Feel free to add in the comments or tweet me at @y_nizan.


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Analytics, Announcement, App Monetization, Resource, Tech Resources

Q2 2018 Mobile Monetization Benchmark Report is Out!

Header image - the SOOMLA ads and churn case study is out for Q4 2017, full of insights

We are excited to announce the release of our second part of the Mobile Monetization Benchmarks report for Q2 2018 today. This is one of the many industry data reports that we will continue to publish providing important insights related to monetization through ad revenue. This report gives an in-depth comparison of eCPMs for 1st impressions and overall and providing a ranking of monetization providers in the mobile industry.

The report is based on information collected through SOOMLA’s platform. The data set includes over 100M users in over 100 countries over a period of 3 months. The report focuses on the 9 countries which produced over 2.5 billion impressions. The analysis breaks down per country, platform, ad type, as well as per ad network and advertiser.

You are welcome to download the report through this link.

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Analytics, App Monetization, Industry News

3 Thoughts on Apptopia’s Top Grossing Study

Looking at Apptopia's recent top grossing study and some thoughts regarding.

Last week, Apptopia published an interesting study about the apps in the top grossing charts. You can read more about it here. This report triggered some interesting thoughts about the mobile eco-system and particularly about games.

1st Thought – 2,624 games in the top 50 – wait, what?

One of the most interesting points of the study is that over 4 years, 2,624 have been in the top 50 grossing chart in US. That’s a bit counter intuitive since one might expect only 50 games in the top 50. However, there is obviously games coming in and out of the charts which increases the number of companies that have been there.

When thinking about the size of the mobile game ecosystem, people tend to think it’s highly concentrated in a small number of companies but this study means there are at least 2,624 meaningful games which clearly indicates the existence of a strong mid market. We can also play around with the numbers and extrapolate what would happen if the analysis was to be made on the Top #200. Based on the shape of the curve, this is a power function and so the same number of games in the top #200 might have been 50,000 different games. I think it’s safe to assume that all these games made significant revenue taking into consideration that they generated money in other countries and not only from IAP.

Here is the extrapulation.

Spot Apptopia Extrapolated
Top #1 14
Top #2 25
Top #5 60
Top #10 142
Top #25 525
Top #50 2,624
Top #100 10,000
Top #200 50,000

2nd thought – App intelligence companies still focus on IAP

Looking at the analysis that Apptopia made about the top grossing games immediately led me to think “what would happen if they made the same analysis for the top downloads chart. This chart has more games come and go and while these games don’t show up in the top grossing charts most of them make very nice revenues from advertising.

As noted in this Pokcet Gamer article, one of the biggest trends of the last 2 years in mobile was hyper-casual. An analysis that is more focused on the top downloaded chart or one that would fix the top grossing one to include ad revenue would be much more interesting. However, this is exactly where app intelligence companies come short as noted by Eric Suefert. and also on our blog post analysing AppAnnie’s top 52 publisher report.

3rd thought – Who will win the app intelligence race

If you look at the app intelligence market there is a very clear winner today – Appannie. An evidence to the lack of a true contender is that it charges annual licenses of $500K – only a monopoly can do that. Their leadership position hasn’t stopped other companies from trying and there are multiple providers who try to compete: Priori Data, Sensor Tower, Apptopia, Similarweb and probably others

The weakness of this space as mentioned above is in tracking ad revenue and this weakness could also be the biggest opportunity. More than half of the revenue in mobile today is made by placing in-app ads inside the app. While the half that is made by IAP is pretty well covered, the half that is made with ads is not covered at all.

It’s not an easy task but some companies already made some progress in this direction. Apptopia specifically are offering estimations for ad revenue but those might not be accurate enough to win yet. If they were, maybe Apptopia’s study would have been about ad revenue.

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Analytics, App Monetization, Tips and Advice

Monthly vs. Daily Opt-in for Rewarded Video

Daily vs Monthly Opt In Rate - How to improve them and what value they can bring!

One of the charts we always recommend our customers to look at is the comparison between Monthly Opt-In and Daily Opt-In. This chart generated some of the more impactful insights and customers that acted on these insights ended up having nice revenue lift.

What is daily and monthly opt-in for rewarded video

Since rewarded videos are not forced on users there are users who watch them vs. users who don’t. The ratio between the number of users who watch videos and the total number of users who were active in the same time period.

We already discussed opt-in ratio in other posts including this one. This post however concentrates on one important aspect – the ratio can be measured on different time periods. When measured on monthly basis it will be the number of users watching at least one video ad in that month divided by the MAU.

At the same time, we can also look at the daily ratio. In this case we will be looking at the ratio between the number of users watching video ads in a give day and the daily active users, the DAU. In order to get a more reliable result, this ratio needs to be measured across multiple days and then aggregated to a single ratio using weighted average.

Monthly opt-in is always equal or higher than the daily opt-in

When comparing the monthly rate to the averaged daily rate over the month, the monthly opt-in will be at least the same number as the daily rate. To understand this let’s consider a very simple scenario with an app that only has two users. Both users were active in all the days in a given month. In all the even days, the first day user #1 watched a video and in all the odd days user #2 watched a video. Both the DAU and MAU will be 2. When we look at the monthly opt-in both users are watching videos so 2/2 = 100% opt-in. When we look at the daily opt-in however, in each of the days only 1 users watched a video so 1/2 = 50% in each day.

Why should you care about this

When thinking about opportunities to improve revenue, it usually comes down to how much more revenue can be generated compared to the cost of the additional effort. To address these for the opportunity at hand – we will need to make some assumptions. The 1st assumption is that optimizing opt-in rate trnaslates directly into the same proportion of revenue lift. This is something we have noticed pretty much in every app we are monitoring and was also reported by Ketchapp games in this talk. The 2nd assumption is that every users who watched a video in one day in a month and came to play in a 2nd day of that month can be convinced to watch a video again. There are a few reasons for that:

  • This user already showed that he interested in getting ahead in the game
  • The user is willing to watch videos
  • In apps that only allow some users (the ones less likely to pay) to watch video if a user already watched means he is in the right group

If these are true then the potential revenue lift in this opportunity is the precentage difference between the monthly opt-in and the daily opt-in multiplied by the daily revenue.

To put this into an example, an app that makes $200K monthly revenue from rewarded video ads and it’s Monthly opt-in is 50% while the Daily is 40% will be able to make $50K more per month by focusing on this opportunity.

Create a habbit with the right incentives, segmentation and popups

There are a few methods we can use to improve the daily opt-in to the monthly level. The most important step is to track this ratio to see which method creates an impact as we experiment. If you have the right setup for a/b testing this will allow you to get results more quickly.

Method 1 – Incentives and daily bonuses needs to work together

  • In many cases, users start a game with some coin balance and watching a video might increase that coin balance to a level that allows them to buy something meaningful with the coins. The second time the user comes into the game he will not have that initial coin balance so watching 5 or 10 videos to accumulate enough coins will seem less appealing. Bottom line – to improve daily opt-in, the daily bonuses needs to be designed along side the incentives for videos to amount to something meaningful together.

Method 2 – Simple pop-up for a segmented group

  • Sometimes, users needs to be reminded. If your platform allows you to pop up an in-game message to a segment of users you can target users who already watched a video in previous sessions with a prompt suggesting they should do so again.

Method 3 – Selling Insurance

  • People tend to buy insurance every time they fly abroad. However, if the insurance company will allow them to only buy the insurance when they need it, less people will end up buying insurance. Similarly, allowing a user to “save himself” by watching a video is less effective than allowing a user to obtain a “save yourself once” credit in return for watching a video at the beginning of a session.

So in terms of effort estimation, the effort might amount to a few days of studio work to set up such tests, a few hours here and there of testing and analysis. All in all I would be surprised if the efforts on this will exceed $10K in labor costs. This means that the return time will be 1 week for the numbers mentioned above so pretty good investment.

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Analytics, App Monetization, Game Design

Inside SOOMLA: Advertiser Breakdown

Inside SOOMLA: A sneak peak into our Advertiser Breakdown screen.  One of the many unique and invaluable features within SOOMLA

In this installation of “Inside SOOMLA”, we’re going to show off our “Advertiser Breakdown” screen. In a nutshell, the entire purpose of this feature is to provide publishers with invaluable data about who is advertising in their app. Whether you want to understand which advertisers are paying out the highest eCPM, make direct deals, or see which advertisers are causing churn – this the place to get it all.

Ultimately, the ad experience is a double edged sword. On one end, ads can provide a significant boost to revenue and counter in-app purchase cannibalization by being properly monitored. On the other end, if not controlled, ads can ruin a user’s experience in the app and send them running for the uninstall button.

There are a few related posts to this – so I recommend checking them out for some context:

  1. 10 Mistakes That Will Keep Your Ad Revenue Low
  2. Data Based Formula – Which Advertisers to Block
  3. Q4 2017 Ads and Churn Case Study

There are several use cases that we’ve seen throughout the market for this data, so let’s take a look:

Case 1 – Advertiser Blocking Compliance

Ad-networks sometimes provide the ability for publishers to block specific advertisers. There are several reasons why publishers tend to do so:

  1. Publishers suspect that their direct competitors are causing churn (despite SOOMLA’s report on advertiser churn).
  2. Certain advertisers are deemed inappropriate for the target audiences of some apps.

These are valid reasons to want to block advertisers, however how does a publisher know that the ad-network is complying with their request. This can easily be tracked by drilling down into the specific ad networks and seeing all of the advertisers it pushes through via the campaigns.


Case 2 – Comparing Ad Networks

More often than not, multiple ad networks are running the same campaigns, however not necessarily paying out the same eCPMs the the publishers. By drilling down into each specific advertiser, publishers can see understand which ad networks are offering what terms for one advertiser allowing you to compare ad networks to each other.

Publishers can begin to maximize their revenue potential on the per impression level like never before.

Case 3 – Doing Direct Deals

You’ve set up a deal to get ads in your app. Great. But do you know how many middle men there are between you and the advertiser? It could be 1, but it also could be 10. Each consecutive step in the process, someone is taking a cut, meaning publishers are leaving money on the table.

By knowing who is advertising in your app, you can build a priority list of advertisers you should approach and attempt to close direct deals with. Even if you don’t choose to close a direct deal, knowing the eCPMs of the advertisers through the ad-networks is still useful to establish benchmarks.


The Advertiser Breakdown analysis is another unique feature to SOOMLA that bring value to publishers who can utilize the data. Our quarterly Monetization and Insights reports can help make sense of all the data, providing some actionable insights. Check out our recent case study with Applife where our report insights boosted their rewarded video revenue by 94%.

In case you missed the previous “Inside SOOMLA” on Waterfall Analysis – be sure to check it out!

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Analytics, App Monetization, Marketing

Inside SOOMLA: Ad Waterfall Analysis

Inside SOOMLA's Ad Waterfall Analysis - an invaluable took for publishers to optimize their eCPM

As a marketer for SOOMLA, I’m often disconnected from the customers / potential clients themselves. Much of my time goes towards content writing, web design, SEO, conferences and the tons of other micro tasks that arise. However lately I’ve found myself sneaking into some of the demos our sales team gives to potential clients because.. well, it’s amazing to see.

Each customer has their own current setup, pain points, ad revenue, integrations, in-app purchases, ad types but one thing I have consistently seen is the reaction from some of the capabilities that SOOMLA brings to the table. This is why I started the “Inside SOOMLA” series to show off a bit, but also to give a sneak peak into our system for those who have yet to sign up and request a demo (which you can do here… shameless plug).

One of the most common scenarios that we see are app publishers leaving money on the table. There are a number of ways that this can occur, however specifically let’s look at the “Ad Waterfall”.

What is an Ad Waterfall?

Also referred to as daisy-chaining, simply put, the ad waterfall works as a prioritized series of ad networks or exchanges arranged from top to bottom in order of performance set by the publisher. The performance tends to be based upon the network’s history of payouts (eCPM), their fill rate, latency delays when serving ads and many more other potential reasons.

To gain some context on what makes the ad waterfall so important, we recently published a monetization benchmarks report which specifically looked at the importance of first impressions. TL;DR – Advertisers payout exorbitant eCPMs for first impressions as they understand their importance.


Waterfall Analysis Screen

The entire purpose of this feature within SOOMLA is to give publishers the ability to make more data-driven decisions rather than biased ones. Publishers often times have a strong biased towards one ad-network since they see a higher eCPM coming from that network however this has been shown to be misleading. The position of the ad network in the waterfall often dictates the higher eCPMs and not necessarily the caliber of the ad network.

There are however other key features of this screen. By giving publishers the ability to visualize the data, they can make data-driven decisions towards changing up their ad-network mix, as well as helping to leverage this information for more beneficial discussions / negotiations with the ad networks. How is this all achieved you ask? Here goes…

Feature 1 – Ad Networks per Impression

This particular section shows full details about what is happening throughout the first ten impressions broken down by ad-network. Publishers see the number of impressions, the total revenue generated by that impression and the current eCPM, all broken down by the impression # in the ad waterfall.
Inside SOOMLA's Ad Waterfall - Ad Networks per Impression

Feature 2 – eCPM Decay Chart

What publisher wouldn’t like to know if they are achieving the optimal eCPM and not leaving money on the table? Thanks to this feature, publishers are now able to see just that. For the first ten impressions, publishers are displayed the “Actual eCPM” (the average across all selected ad-networks) while the “Optimal eCPM” represents the maximal eCPM attainable for the given impression by one of the ad-networks. For a more in-depth explanation about eCPM Decay, check out one of our posts on it.

Inside SOOMLA's Ad Waterfall - eCPM Decay

Feature 3 – Ad Network Comparison

This section visualizes for the publisher which ad-networks serve at which impression and how many ads they server daily. Furthermore, you can see exactly the eCPM paid by each ad-network for each impression count.

This is an invaluable tool in conjunction with the eCPM Decay feature as it allows you to break down why certain ad networks, while having higher eCPMs, are not displaying more ads as I’m sure publishers would like them to be. Low fill rate or bad choices vis a vis the mediation are often the culprits here.
Inside SOOMLA's Ad Waterfall - Ad Networks Comparison


Our Ad Waterfall analysis feature is unique to SOOMLA and one has one of two effects on potential clients of ours: 1) They are amazed and want to see a direct business case via their data, or 2) The stream of questions comes, asking how we achieve this, is the data credible and so on.

If you have any of these questions, or want to see a far more in-depth demo of SOOMLA (not just the Ad Waterfall feature), reach out and we’ll get one set.

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Analytics, App Monetization, Game Design

How Applife’s Rewarded Video Revenue Jumped By 94% In 100 Days With SOOMLA’s Insight Reports

Case Study with Applife and SOOMLA's Insight and Monetization Reports

One of the great benefits afforded to our clients, is our tailored Insight and Monetization Reports that we produce for them on a quarterly basis. Just like it sounds, we have dedicated customer success managers that use mobile industry benchmarks and powerful analysis tools to make sure our customers can convert their data into actionable insights.

Our Insight and Monetization Reports have been beneficial to our clients and for Applife it was no different. Here you can find a copy of a sample Monetization Report.

Applife, has been a customer of ours for a little over 4 months at this point. They have several apps, however the one we took a look at is “Parking Escape”. Parking Escape is a casual sliding block puzzle game. The goal of this game is to get the blue car out of a six-by-six grid full of automobiles by moving the other vehicles out of its way. The game contains 6 difficulty levels with thousands of puzzles to be solved.

Our analysis noticed a severe drop off in users’ opt in rate to rewarded videos after the first week of and immediately noticed a strong opportunity to boost Applife’s rewarded video revenue. Get the full report and case study below, seeing how Applife was able to boost their rewarded video by revenue by 94% within 100 days of using SOOMLA.


Here are some related articles that can help:

  1. Measuring and Improving Opt-In Ratio with SOOMLA TRACEBACK
  2. 4 Proven Tips for Improving Opt-In Rate – Based on Data
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Analytics, App Monetization, Game Design

Japan eCPM Benchmarks Series – Top Advertisers Comparison

Japan eCPM Benchmarks Series - Top Advertisers Comparison

We’re back for another installation of Japan’s eCPM Benchmark Series! In the 3rd (and final) part, we’ll be looking to compare the performance of advertisers who serve interstitials and rewarded videos in Japan. In order to be as concise as possible, we’ll be looking into the top 10 performing advertisers in each category. In case you missed the previous parts, can find part part 1 and part 2 here.

For each ad type, we will look into advertisers who were first impression focused, as well as those who maintained a low amount of first impressions. Furthermore, we looked at the top performing advertisers, broken down by iOS and Android in terms of first impression volume and eCPM.

Why 1st Impressions

By focusing on 1st impression monetization, we are able to provide a better measure of the strength of different monetization channels. More importantly, it allows us the compare between advertisers on a more level playing field.

Ad networks will be able to see which advertisers are buying aggressively for each format and platform, while publishers can gain some insights on which advertisers are a potential fit for direct deals.

Note: As a base filter, we looked at apps with a minimum of 5,000 first impressions for the date range selected.

Interstitials – 1st Impression Lovers / Non-Lovers

The chart below shows advertisers that served a higher ratio of first impressions in the day compared to the total impressions.
SOOMLA's Japan Breakdown - Interstitial 1st Impression Lovers

To show the contrary, the chart below displays advertisers that have a lowest ratio of 1st impressions to the total impressions. These advertisers have not adopted a strategy focused on the importance of the 1st impression.
SOOMLA's Japan Breakdown - Interstitial 1st Impression Non-Lovers

While these charts might not be indicative of anything in this context, the next few charts showing the eCPMs can help give insights about advertiser specific strategy.

Top Advertisers for Interstitials – iOS

The chart below ranks the top 10 advertisers who placed ads in other apps via different channels. The comparison of these advertisers is based on 2 dimensions – 1st impression eCPM and 1st impression volume.
SOOMLA's Japan Breakdown - Interstitial Top Advertisers iOS

We can see that Kurashiru, Homescape and Wooden Block Puzzle are the only 3 advertisers that are performing above average (green line) for both 1st impression volume and eCPM. Another interesting note is that Fill has a very high eCPM payout in comparison to the other advertisers despite having a fairly lower volume of impressions.

SOOMLA's Japan Breakdown - Interstitial Top Advertisers Android

For Android, we can see that only Hidden City – Mystery of Shadows maintains an above average 1st impressions volume and eCPM in comparison to other advertisers.


Rewarded Videos – 1st Impression Lovers / Non-Lovers

The chart below shows advertisers that served a higher ratio of first impressions in the day compared to the total impressions.
SOOMLA's Japan Breakdown - Rewarded Videos 1st Impression Lovers

Yes, 96% and 91%. I saw it as well and was positive there was an error in my data, however after triple checking, the data was in fact accurate. Both of those apps are ENTIRELY focused on 1st impressions.

To show the contrary, the chart below displays advertisers that have a lowest ratio of 1st impressions to the total impressions. These advertisers have not adopted a strategy focused on the importance of the 1st impression.
SOOMLA's Japan Breakdown - Rewarded Videos 1st Impression Non-Lovers

Top Advertisers for Rewarded Videos – iOS

The chart below ranks the top 10 advertisers who placed ads in other apps via different channels. The comparison of these advertisers is based on 2 dimensions – 1st impression eCPM and 1st impression volume.
SOOMLA's Japan Breakdown - Rewarded Videos Top Advertisers iOS

For this case, we can see that no apps are performing above average for both 1st impression volume and eCPMs. However we do see that Hidden City is dominating the 1st impression volume, while Matchington Mansion and Seeker’s Notes maintains very high 1st impression eCPM payouts.

SOOMLA's Japan Breakdown - Rewarded Videos Top Advertisers Android

For Android, we can see an almost mirroring of iOS. There are no apps that are performing above average for both 1st impression volume and eCPMs. Hidden City however has appeared on the high end of 1st impression volume for both iOS and Android.


This concludes our first eCPM Benchmark Series who’s sole focus has been on Japan. In our next series, we will be looking at India and how the growing gaming market is now one-tenth of all global gamers.

In the spirit of being big in Japan, enjoy the closing song!

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