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.

Q2 MONETIZATION BENCHMARKS

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. https://play.google.com/store/search?q=Jigsaw&c=apps

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 – Agar.io was available on mobile and also via the domain agar.io. 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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Marketing, Resource, Tips and Advice

The Best Mobile Gaming Conference To Attend And Why – Our Top Picks

We looked at what are the best mobile gaming conferences to attend and why.  Check out the breakdown of our top picks.

Every quarter, there are about a dozen, if not more, conferences that fall under the umbrella of mobile gaming / marketing / growth / data / analytics. Each conference tends to have a focal point, anywhere from growth, blockchain, indie gaming, monetization, retention and the list goes. Hopefully we will answer help answer “Which gaming conference should I attend / is the best?” by breaking down some of the top mobile gaming conferences out there.

The first question you should be asking yourself is, “What are my goals for the conference?”. A conference can be a great place to brainstorm ideas from peers, network, grow your client base, learn from speakers and lectures, play the upcoming games for 8 hours straight (I’m guilty of that) and of course connect with other people in your industry.

There is a pretty straightforward 10 point criteria system that should be looked at when choosing a conference:

  1. Goals
  2. Cost
  3. Location
  4. Who’s Going?
  5. Who’s Speaking?
  6. Topics / Tracks Offered
  7. Number of Relevant Topics / Sessions
  8. Conference Format
  9. Timeline
  10. Networking Opportunities

While I am sure none of those 10 criteria above are anything new to anybody who has contemplated whether or not to attend a conference, I do want to bring something new to the table here and give some insight how SOOMLA chooses which to attend.

First, let’s look at some of the conferences / events in the mobile gaming industry:

Delta DNA’s Game Industry Analytics ForumGame Industry Analytics Forum

Delta DNA’s Games Industry Analytics Forum is a series of events targeted at all industry professionals who seeks to make their games better through analytics. They bring a large mix of industry experts who speak on a variety of topics.

Name Games Industry Analytics Forum
Locations San Francisco and London
Avg. Companies NA
Avg. Attendees NA
Cost Free
Focus Analytics

Pocket Gamer Connects ConferencePocket Gamer Connects

Pocket Gamer’s Global Mobile Game Conference is held three times a year in various locations. The main focus tends to be on global game publishing strategies, opportunities in various markets. They have several (12 or so) content tracks each with a focus such as Monetization, Marketing, eSports, Growth and more.

Name Pocket Gamer Connects
Locations 3 per year: London, Helsinki, San Francisco
Avg. Companies 600+
Avg. Attendees 1200+
Cost Varies from several hundred $ to thousand +
Focus Mobile Gaming Industry

Casual Connect ConferenceCasual Connect

Casual Connects hosts several conferences across the globe, each with its own particular focus. Anywhere from developers, game design, east meets west, and game tech innovation.

Name Casual Connect
Locations 4 per year: USA, Europe, Asia, Eastern Europe
Avg. Companies 600+
Avg. Attendees 800-2000+
Cost Varies from several hundred $ to thousand+
Focus Mobile Gaming Industry

White Nights ConferenceWhite Nights

White Nights is considered to be a business conference for the gaming industry. They are focused on all aspects of the gaming industry, including mobile, PC, console, web, AR and VR.

Name White Nights
Locations 4 per year: Prague, St. Petersburg, Moscow and Berlin
Avg. Companies 800+
Avg. Attendees 1600+
Cost Several hundred $
Focus Business of Games

Mobile Growth SummitMobile Growth Summit

Mobile Growth Summit is a non-vendor conference targeted at those who work in the mobile growth industry. The conference aims to bring mobile growth and marketing professionals together to connect and learn from one another in areas such as UA, monetization, retention and eCommerce.

Name Mobile Growth Summit
Locations 4 per year: Prague, St. Petersburg, Moscow and Berlin
Avg. Companies 300+
Avg. Attendees 600+
Cost Varies from several hundred $ to thousand +
Focus Mobile Growth / Marketing

Mobile Games ForumMobile Games Forum

Mobile Games Forum brings together gaming industry decision makers to discuss the direction of the industry and potential strategies. The larger portion of attendees tends to be C level, senior managers and directors.

Name Mobile Games Forum
Locations 2 per year: London and Seattle
Avg. Companies 300+
Avg. Attendees 600+
Cost Several hundred $
Focus Upper Management / Decision Makers

Game Developers ConferenceGame Developers Conference

Game Developers Conference is a 5 day event that attracts thousands of attendees from all over the world. They have a wide variety of tracks / sessions on just about every topic from blockchain to VR to monetization. Many up and coming publishers use the conference as a great opportunity to show off their upcoming games.

Name Game Developer Conference
Locations San Francisco
Avg. Companies 700+
Avg. Attendees 3000+
Cost Depends on type of ticket: from free to several hundred.
Focus All things gaming

Game ConnectionGame Connection

Game Connection is a business convention for the game industry where publishers, developers, service providers and distributors come to find new partners and/or new clients. Considered to be the go-to conference for business.

Name Game Connection
Locations Paris and San Francisco
Avg. Companies 1500+
Avg. Attendees 2700+
Cost Several hundred $
Focus Business Creation for Game Industry

As you can see, each conference does have it own focus, and depending on what you are looking to achieve, one of the above conferences should meet your goals.

Criteria # 11 – The Meeting System

One of the topics that we did not cover however is the meeting systems that is made available to the attendees. Depending on the purpose of attending the conference, this could be irrelevant to you, but if you do plan to attend for networking purposes, a good meeting system can dramatically improve the success of your trip. Here are a few of the meeting systems we’ve encountered at SOOMLA:

Feature Pitch&Match MeetToMatch Let's Meet Bizzabo
Profile Creation Y Y Y Y
Search Filters Y Y Y Y
Internal Messaging Y Y
Mobile App Y
Block Meeting Time Slots Y Y Y
Agenda Export Y Y Y Y
“Smart” Scheduling System* Y

Smart System* refers to the meeting system’s method of organizing meetings. Let’s Meet is the only system that allows you to request a meeting without a set time before the event. Generally about a week before the event, all the meetings that have been accepted are automatically filled into your calendar based on a mutual time slot being available. This is significantly different and advantageous compared to the other meeting systems that require you to set a time, limiting your ability to be flexible.

Conclusion

While each conference has its own focus, you are bound to find a mobile gaming conference that meets your criteria. If you think there are any more that are worth mentioning, let us know in the comments below!

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Marketing, Resource, Tech Resources, Tips and Advice

Complete Mobile Advertising Glossary – Over 70 Terms Defined

SOOMLA's complete mobile advertising glossary - we've broken down the top 70 terms in ad tech to be easily understood.

From newcomers to mobile industry veterans, the amount of acronyms and terms that exist can be daunting, and frankly, often times confusing. The industry is constantly evolving, and with that, comes a steady flow of new terminology. We’ve compiled a list of 70 terms that are most prevalent in adtech and broken them down into easy to understand terms.

Here is the complete glossary to mobile advertising at your fingertips. Read, take notes and prosper.

1st Impression

Refers to the first ad displayed to a user within a session. Considered to be the most valuable in terms of eCPM.

1st Look

Ad-networks are prioritized in a a waterfall where the 1st ad-network gets to “look” at the ad request and decide if they want to provide an ad or not. If the ad-network passes the turn goes down the waterfall to the 2nd ad-network. When an ad-network asks for “1st look” it means they want to sit on the top of the waterfall and have the opportunity to see all the ad requests.

1st Price Auction

Where a bidder pays exactly what they bid. Often times leading to inflated prices and a lower demand for that publisher’s inventory.

2nd Price Auction

Where the top bidder pays the what the second highest bid was + $0.01. Allows for the saving of money due to overestimation of the value of a publisher’s inventory.

A/B Testing

A test in which all variables are identical aside from one. The purpose is to compare the two versions and see which performs better according to the established KPI.

Ad Exchange

An ad exchange refers to a platform that helps facilitate the buying and selling of advertiser inventory from multiple ad networks.

Ad Inventory

The amount of (virtual) space a publisher has available to be sold to advertisers. Also known as “ad space”.

Ad Mediation

A platform that sends requests for advertisements to multiple ad networks for publishers, ensuring the ad space is filled with the best possible deal.

Ad Network

An ad network connects advertisers and publishers looking to generate revenue by serving ads in a mobile app or website.

Ad Revenue

The amount of revenue generated from placing advertisements in an application / website.

Ad Whales

Refers to a group of users who make the most amount of ad revenue. Typically this is defined as the top 10% or 20% of the users who make 70% or 80% of the ad revenue. In other cases, the ad-whales are defined as a threshold of $0.7 (the equivalent of a $1 IAP).

Advertiser Blacklisting

The process of publishers blacklisting specific advertisers from appearing in their app. Some ad networks provide this as an option to publishers. The blacklisting is often based on competition, poor eCPMs or inappropriate content.

CASE STUDY ON OPT-IN RATES & SOOMLA INSIGHTS

Advertiser Identity

Identifying which advertisers are advertising in one’s app and their respective performance.

API

Application Program Interface. An API opens access to a limited part of a piece of software, allowing 3rd party developers to access previously unaccessible information.

ARPU

(Average Revenue Per User). Calculated by dividing the total revenue by the number of users across a time perdiod (generally monthly or annually).

ASO

(App Store Optimization). The process of improving the visibility of an app in the respective app store (Google Play, iTunes, etc.)

Attribution

The process of accrediting a traffic source with the conversion. Each platform has it’s own methodologies for doing so.

Audience

A group of consumers / users within a specific target market which are targetted for a specific ad campaign.

Blacklist

A list of advertisers that a publisher has decided to not allow to appear within their app.

Churn Rate

The rate at which users have stopped using an app during a specific time period

CLV

(Customer Lifetime Value). Has the same meaning to LTV – the total value of a user if given enough time to fully exhaust all opportunities to pay or watch ads in the game. This value is often averaged across a group of users (cohort) and most publishers try to create models for future prediction of CLV/LTV based on the activity of the first few days so they can make quick decisions on the marketing side.

Cohort

A group of users that share one or more similar characteristics. Used for grouping in data analysis.

CPA

(Cost Per Acquisition). The cost a publisher incurs to bring a new user through various paid channels.

CPC

(Cost Per Click). A campaign where the advertiser is charged everytime a user clicks on the ad they are shown.

CPI

(Cost Per Install). A campaign where a price is paid when a user views an ad, goes to the app store and installs an app.

CPM

(Cost per Mile). A campaign were advertisers pay for every thousand times the advertisement is shown.

CTR

(Click-Through-Rate). The ratio of users who click on an ad / opt-in to the total users who were displayed an ad.

DAU

(Daily Active Users). How many users open your app on a daily basis. This is one of the key metrics used to measure ad revenue.

Direct Deal

Rather than going through an ad network or mediation platform, publishers can make direct deals with advertisers, cutting out the middle men.

DSP

(Demand Side Platform). A platform in which marketers can buy ad inventory from multiple ad exchanges in one place.

eCPM

(Effective Cost Per Thousand). A metric used to measure ad revenue generated. Generally calculated by dividing total revenue earned by total number of impressions in the thousands.

eCPM Decay

The inevitable decrease in eCPM as ads begin to be displayed to your users. There are however methods proven to help slow it down.

Fill Rate

The number of ads requested that are successfully filled in relation to the total number of ads requested. Tends to be displayed in a percentage format.

Frequency / Frequency Capping

The rate at which users are shown ads. For example, some apps place a frequency cap of 4 ads per day for users.

GDPR

(General Data Protection Regulation). A comprehensive new set of regulations designed to give EU citizens more control / oversight on their personal data and the entities that collect it. Under GDPR, companies will need to notify their customers when collecting personal data. Consent can be given or denied based on the purpose of the data usage.

Header Bidding

The process by which publishers open ad space to be bid upon by multiple ad exchanges, resulting in an increased yield and revenue.

IAP Cannibalization

The notion that the integration of incentivized rewarded videos are damaging to the revenue gained from in-app purchases.

IDFA

The “Identifier for Advertisers” is a random identifier number given by Apple to a user’s device. It is used for advertising targeting.

Impression

Refers to the moment when an ad is fetched from its source and dispalyed to the user. Each ad type has a varying distinction of when the impression occurs. For banner ads, once an ad is fetched from its source and the user sees the ad. For video ads, the impressions is logged once the first frame of the video is displayed.

Interstitials Ads

Ads that are full-screen which are typically displayed at natural transition points in the flow of an app. For example, between levels or when the game is paused.

Q1 2018 MONETIZATION BENCHMARKS

Lookalikes

A method of targeting additional users based on similarities to existing users. Generally based on geo, interests, gender, age etc.

LTV

(Lifetime Value). Has the same meaning to CLV – the total value of a user if given enough time to fully exhaust all opportunities to pay or watch ads in the game. This value is often averaged across a group of users (cohort) and most publishers try to create models for future prediction of CLV/LTV based on the activity of the first few days so they can make quick decisions on the marketing side.

MAU

(Monthly Active Users) How many users open your app on a monthly basis. This is another of the key metrics used to measure ad revenue.

Mobile Ad Fraud

The process which fraudsters cheat advertisers into displaying ads to fake users / bot traffic.

Multi-Touch Attribution

Allows for the attiributing of all channels in the conversion process. Different from the existing last touch approach.

Offer Wall

A list of incentives which a user can receive by performing a specific action, typically downloading another app, watching a video ad, or sharing content on social media. The rewards for doing so tend to be some form of digital currency or other in-app incentives.

Opt In

When a user actively agrees to participate in viewing an ad.

Pipelining

The process of moving data from one point to another.

Playable Ads

Ads that request the user to interact, often showing off the basic gameplay of the game being advertised.

Postbacks

The ability to notify a third party of a specific event that occurs within an app (install, in-app event, etc.)

Programmatic

An automated process for purchasing digitial advertising, eliminating the need for a human touch.

Reach

The number of unique users who can potentially be targeted by advertising.

Real-Time Bidding

Bidding on inventory in real-time. The bid is often dynamically generated based on past performance of creatives, inventory, user groups, and other parameters.

Retargeting

A marketing effort in which targeted online advertisements are shown to users based on their previous behaviour.

Rewarded Video Ads

Video ads that are offer the user the option to opt-in to view them in order to receive some incentive. Can range from free coins, extra lives to free uprades.

ROI

(Return on investment). A performance measure used to determine the efficiency of an investment / effort.

SDK

(Software Development Kit). A set of software development tools that can be added to existing apps to support new capabilities.

SDK Mediation

Mediation of ad-networks allows publishers to use a few ad-networks in parallel and maximize fill rates and eCPM. SDK mediation is a form of mediation where each ad-networks installs an SDK on the publisher’s app and the mediation is done on the client side.

Server Side Mediation

Server side mediaiton is a form of mediation. Unlike SDK mediation, the goal of maximizing fill rates and eCPMs for the publisher is acheived with a single SDK provided by the mediaiton company and the ad-networks have to serve their ads through that SDK. This means that the mediaion is done on the server side

Share of Voice

Refers to the portion that one company controls out of the total. In terms of ads, can refer to an ad network who maintains a larger share of voice in rewarded video displays in comparison to other ad networks.

SSP

(Supply Side Platform) A platform that allows publishers to sell their advertising inventory in an automated way.

Target Audience

A group of consumers / users within a specific target market which are targetted for a specific ad campaign.

Traceback

SOOMLA’s unique ability to traceback individual user’s ad behavior.

Tracker

A tracker is a link structure generated by the attribution platform on behalf of the advertiser. The tracker is than passed along to the ad-network so clicks on the ads of the advertiser which are associated with the ad-networks can be identified through the unique link structure of the tracker.

Unified auction

On open auction where all ad networks are given an equal opportunity to bit on an app’s inventory, without any preferential treatment, in which the highest bid tends to win.

UDID

(Unique User ID). A unique identifier assigned to each device. Apple and Google have their own methods for doing so.

User Acquisition

The marketing efforts to acquire new users through advertising campaigns.

VAST Tag

(Video Ad Serving Template). A universal XML-based protocol specification created by the IAB for serving video ads.

Whitelisting

The opposite of blacklisting in which certain entities are explicitly approved.

Yield Management

The use of tools and business practices to maximize revenue.


Find a term that we didn’t define here? Let us know in the comments below and we’ll get them added ASAP.

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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 STUDY ON OPT-IN RATES & SOOMLA INSIGHTS

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.

Conclusion

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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Announcement, Industry News, Tips and Advice

Hyper Casual Games – Thoughts about Voodoo and Gram Deals

Hyper Casual Games - Thoughts about Voodoo and Gram Deals with SOOMLA's CEO, Yaniv Nizan

Towards the end of May, the mobile game industry received news about 2 mega deals in 2 consecutive days. First it was Voodoo that announced a funding of $200M with an estimated valuation of over $500M. Then, the day after, Gram Games announced it’s acquisition by Zynga for a sum of $250M with additional sums to be paid against future results. Not surprisingly, these two companies share a similar philosophy and have been focused on games that monetize via ad revenue. Also, not surprisingly both, apply advanced measurement and optimization techniques for their ad monetization.

The new type of games that have been dominating the app stores since the beginning of 2017 is often called hyper casual games. The term first appeared in a series of articles written by Johannes Heinze from Applovin and published in the Applovin blog and in Pocketgamer. While the games have been visible on the app stores, the companies responsible for them often went undetected by press and analysts and remained an industry secret. One good example of these companies not getting detected is that both companies as well as Outfit7 (bought for $1B) were not included in AppAnnie’s top 52 publishers list.

Also, interesting to note is that these new direction of innovation is coming mainly from Europe. In addition to these 2 companies we can also add Ketchapp games, Outfit7, Tabtale as well many other smaller companies in Europe who are embracing ad driven models alongside a data driven approach and are dominating the hyper casual genres. From the US, companies such as Zynga starts to understand the potential and are compensating by acquiring such studios. Specifically for Zynga, the acquisition of Gram seems to be part of a strategy as the company also bought Harpen and paid Peak games $100M for their casual card game studio.

SOOMLA blog also picked up on the trend as early as 2016 when we noticed $300M going into companies that pioneered ad driven games. And then in March 2017 immediately after Harpen’s acquisition by Zynga, we identified Voodoo and Gram as strong potential for bigger deals in the future. Here are some additional companies who specialize in ad driven games that we recommend following.

  • Tabtale
  • Mobilityware
  • Etermax
  • Gazeus
  • Kwalee
  • Ilyon
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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.

Q1 2018 MONETIZATION BENCHMARKS

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

Conclusion

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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