Author

Raised in the Kibbutz and reborn in the city, Yaniv is a certified entre-parent-neur. When he’s not busy doing SEO, content marketing, administration, QA, fund raising, customer support… [stop to breathe], you can find Yaniv snowboarding down the slopes of France and hiking with his kids. Yaniv holds a B.Sc. in Computer Science and Management from Tel Aviv University. He is also an avid blogger and a speaker at industry events. Before SOOMLA, Yaniv co-founded EyeView and INTENTClick.

Analytics, App Monetization, Tips and Advice

3 reasons to track 1st Impression eCPM and not average eCPM

App Publishers who monetize with ads often face the need to compare between ad-networks. Which one offers stronger monetization? Is the network declining in strength? Who should i put first in the waterfall? The common practice today is to look at the average eCPM but actually looking at the 1st impression eCPM is a much better approach. Here are 3 reasons for that.

Networks put their best campaign first

Each ad network has internal optimizations mechanisms in place. Some have algorithmic approach that try to predict the eCPM of each potential ad given who is the user and all the data they have about him. Others have more simplistic priority lists. Either way, when the network sees the user for the 1st time in a given day, it will try to put the best ad for that user. In later impressions, they have to circulate in other ads, their 2nd best, 3rd best and so on.

Average eCPM is a self fullfilling prophesy

Average eCPM on the other hand is influenced by many parameters other then the network’s stregth. In situations where the average eCPM is used to determine the priority between the networks it acts as a self fullfilling prophesy. To understand this, let’s look at the two ends of the priority list:
The Network with First Priority – This network gets more 1st impressions than any other network as long as it has fill for them. This drives the average up. At the same time, the network also wants to stay at the top and knowing that the publisher is looking at the average eCPM it is likely to set a price floor that will eliminate the low eCPM campaigns. This will also drive the average eCPM up.
The Network with the Low Priority – This network is getting less 1st impressions so their average will be lower. Even if the network landed a major campaign it will not get a lot of exposure and will not be able to drive the eCPM up. At the same time, the low priority network can’t shut down the low eCPM campaigns as that will completely choke the delivery for their advertisers and will cause a new bag of issues for the network.

 

FREE REPORT – VIDEO ADS RETENTION IMPACT

 

Changes in 1st impression eCPMs are clear triggers for action

Tracking different parameters is a good practice but tracking becomes much more powerful when it’s connected to actions. When you track the average eCPM and you see a drop in that paremeter for one of the ad-networks there could be a few potential explanations. For example, if that network is getting a high percentage of later impressions it would bring down the average. The 1st impression eCPM is less influenced by how you are using the demand source and is a better indicator of the quality of the demand. A drop in the 1st impression eCPM can be caused by the ad-network losing an important advertiser or by them changing the rev-share on their end. Either way, it’s a good reason to look for new partners to take the lead.

Tracking 1st impression eCPM – Easier than ever

The reason why more company focus on average eCPM rather than 1st impression eCPM is that this is the information the ad-networks are making available on their dashboards. Publishers that use SOOMLA, however, have easy access to reports about the 1st impression eCPM over time and the 1st impression eCPM of every single campaign by each ad-network in addition to the average eCPM.

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App Monetization, Tips and Advice

Hiring a ROI Monetization Manage, a full ROI formula and explanation

Many companies ask themselves these days if they should be hiring a Monetization Manager now or wait until it’s volume is larger. In this post we will try to provide a simple framework for thinking about this question.

Ads first games vs. IAP first games

There are two types of companies to consider for this question. Before you continue, you should ask yourself which type of company are you. The framework for evaluating the merits of hiring a monetization manager differs a bit between the two types of companies. Here is the profile for each one:

Ad first games – These are typically smaller companies. If you are an ad supported company and still debating the monetization manager question it’s unlikely that you have more than 15 employees. These companies tend to have a mix of at least 3 ad formats from this list: banners, native, interstitials, video and rewarded video.

IAP first games – These are typically more established companies who already do well with IAP and treat ads as a secondary channel. The ad formats in use here are mostly rewarded videos and sometimes offer walls.

The basic formula

There are 2 conditions to be met before you hire a monetization manager:

  • The ROI condition
  • The focus condition

The ROI condition

[monthly ad revenue] x [improvement opportunity ratio] x [risk factor] > [monetization manager full cost]

Where:

  • Monthly ad revenue – how much your app is making every month from advertising
  • Improvement opportunity ratio – Estimation of how much you can improve
  • Risk factor – the chance of that improvement actually happening
  • Monetization manager full cost – Salary + social benefits + taxes + direct overhead increase + cost of tech tools + cost of projects he will drive

The focus condition

The focus condition is looking at the same formula but instead of justifying the direct cost, you are estimating the opporunity cost. The focus condition is more relevant if you are projecting that the monetization manager will be driving many requirements to R&D and BI departments. We will see how to evaluate how much effort the monetization manager will require in the paragraphs below.
The way to think of opportunity cost is usually top down. Let’s say that the goal of the company is to double in revenue within 12 months. This means that each quarter you are looking to get 20% growth. Most companies can’t contain more than 2 focuses each quarter and some say 1 is enough. This means that if the monetization manager and all the tasks associated with him will not generate 10% increase it’s not meeting the focus condition. The formula will look as follows:

[improvement opportunity ratio] x [risk factor] > [Required quarterly improvement] / [Quarterly initiatives count allowed]

Estimating the improvement ratio

For IAP first games

  • Improving opt-in ratio for rewarded videos – high product and R&D effort – can double or triple ad revenue when combined with A/B testing.
  • Adding more demand partners – medium product and R&D effort – the improvement in ad revenue can be up to 50% depending on current status (see full explanation below)
  • Applying CPM price floors and cutting fixed CPM deals – no R&D effort – up to 15% improvement
  • Blocking low eCPM advertisers and optimizing volume for high eCPM ones – no R&D effort – up to 15% improvement
  • Setting different ad strategies for different segments – low R&D effort – up to 30% improvement
  • Acquiring users who respond better to ads – no R&D effort – up to 50% improvement

For Ads first games

  • Optimizing the frequency and mix of ad-formats – medium R&D effort – can improve ad revenue up to 50%
  • Adding more demand partners – medium R&D effort unless done as S2S – the improvement in ad revenue can be up to 50% depending on current status (see full explanation below)
  • Applying CPM price floors and cutting fixed CPM deals – no R&D effort – up to 25% improvement
  • Blocking low eCPM advertisers and optimizing volume for high eCPM ones – no R&D effort – up to 15% improvement
  • Setting different ad strategies for different segments – mid R&D effort – up to 30% improvement
  • Acquiring users who respond better to ads – no R&D effort – up to 50% improvement

 

FREE REPORT – VIDEO ADS RETENTION IMPACT

 

How much your ad revenue can improve by adding demand partners

The improvement ratio per ad-format is driven by how strong your demand and fill rates are currently. We included a basic formula that we found helpful but you should do a better job assessing this by looking at specific countries and diversified demand. We also recommend Jonathan Raveh’s post on this subject. Here is a simple formula to start with:

(2x[number of ad-networks serving banners]+1)x[banners revenue ratio from total]/2x[number of ad-networks serving banners]-1

Estimating the cost of the monetization manager

$8K/month or $96K per year is a nice salary for a monetization manager in US. The taxes and benefits in US can come to 25% to 40% on top of the salary. Office space and immediate overhead per employee can be around $500 based on WeWork rates. In addition, we should add the average license cost of SOOMLA ($3,000) since having a monetization manager and not giving him the right tools to optimize would be moot. The total comes to $13,500 – $15,000.

Estimating the risk

The risk ratio is slightly harder to estimate. You should think of all the things that can go wrong and try to assign probabilities. Here are some items to consider:

  • Bad hiring can set you back
  • If you can’t afford a SOOMLA license your risk will be higher
    • The monetization manager will not be able to a/b test the ad revenue so optimizations might have a negative impact
    • His ability to set the right price floors will be limited
    • He will not be able to analyze and optimize on a campaign level
    • Segmentation will not be possible for him
    • The users that are being acquired by the UA team will not be a good fit for ads
  • IAP first apps monetize mostly with rewarded video where negotiating eCPM price floors with ad-networks is only possible for high volume apps.

Example – finding the ad revenue threshold for hiring

Let’s look at one example of using the formula. We can estimate that the total opportunity to improve is 60%, the risk factor is 50% and the total cost of the monetization manager will be $15,000.

[monthly ad revenue] x 60% x 50% > $15,000

To satisfy this condition we need an ad revenue of at least $50K / month or $600K annually. The numbers we choose are reasonable so if you have this level of ad revenue and you are not hiring a monetization manager you are probably leaving money on the table. Of course, if you have $1M/month from IAP and only $50K in ad revenue, you might have bigger fish to fry first. This is where the focus condition comes in to play. Make sure you evaluate both before you make the decision.

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App Monetization, Tips and Advice

7 ad experiences that will kill your retention: freeze, decieve, frustrate, delay, bore, annoy, trick

When integrating ads, one of the biggest concerns is that users might churn away. There is an obvious trade off between the need to give the users a great experience and the need to turn revenue. Not all ads are created equally when it comes to their impact on user retention and it’s important to measure the impact of different ad types and monitor what ad experiences your users are getting from your ad partners. Below is a list of ad experiences to watch for:

 

FREE REPORT – VIDEO ADS RETENTION IMPACT

 

1. Crashes and freezes can impact mobile app retention

Ads are served by the ad networks who tend to require an SDK integration. Each SDK increases the complexity of the app and might conflict with other SDKs, in turn cause your app to crashes for your users. This happens especially in edge conditions such as old Android versions or uncommon devices. While crashes are typically reported through your crash analytics provider, there is another type of error that is trickier to track. In some cases, the SDK of the ad network will try to show an ad to the user but will end up freezing the device. This type of error is typically not detected and is harder to monitor but it could have the same negative impact. Both of these errors might cause users to churn away and reduce the overall app usage experience.

2. Close buttons that are hard to find frustrate users

In some situations a full size ad such as an interstitial, video or playable will load and the users will want to close it right away and continue using the app. The lack of an obvious way to skip the ad experience is a big turn off for users who are likely to stop using an app that consistently makes it hard for them to skip the ad experience. There are a few types of ads that have this negative experience. In some cases the X button will have a color that doesn’t pop up from the background, in other cases it will show up only after a few seconds without a clear indication of how long it will take and in other cases it might show up in a different way every time. Sometimes it’s all 3 together causing a very unpleasant experience for the user.

3. Lack of ad diversity will bore your users

It’s one thing to show a user 10 ads per day but it’s another thing to show him the same ad 10 times every day. In addition to being ineffective, repeating the same ad many times is a negative user experience. You may think that advertisers have enough incentive to make sure this doesn’t happen but in today’s mobile advertising eco system the lack of data transparency may result in the same advertiser showing their ads in your app through different channels and without them knowing about each other. In this situation, the frequency capping is not getting enforced.

4. Poorly targeted ads may get your users annoyed

Ads today can be highly targeted and users have come to expect targeted ad content. Poor targeting can range from an ad to a game you already installed and go all the way to inappropriate ad content being targeted to kids. The publishers typically don’t control ad targeting and usually leave it to the ad providers however some ad providers are better than others. While companies like Facebook are known for their hyper targeting, some ad networks have little targeting data to work with and placing the focus not on targeted ads, but rather on their revenue. If you are serious about keeping your retention high, you should monitor ad content and targeting closely.

5. Your users don’t want to wait for a slow loading ad

No one likes to wait but while waiting for something you desire can be tolerable, waiting for an ad to load is likely to be crime in your users’ book. Monitoring the loading time of every single ad can be hard to do on your own but the right monetization measurement platform can help you with it.

6. Deceiving ad creatives are hard to tell from your app buttons

Imagine a user that clicks on a “download” button only to realize it wasn’t a button but actually an ad that looked like the real button. Alternatively, picture someone trying to click on the “next” button but hitting an interstitial ad that popped up between the time his brain sent the command and the time the finger reached the button. These errors might be annoying for a savvy user but think how they impact the experience of a less savvy user who is now trying to figure out where the rabbit hole led him to and how he can get back.

7. Inconsistent ad skipping experience and long duration ads

Users are used to not being able to skip a rewarded video ad. These are opt-in ads that the user initiated and so it makes sense that he can’t skip them. However, other ad placements can have an opt-out experience or have no way to skip at all. Obviously, not having the option to skip is more annoying for users but what will really tick them off is when an ad placement will have a mix of:
  • Ads you can click skip right away
  • Ads that requires no action but just waiting
  • Ads that require a combination of waiting and than clicking to end
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Top 12 rewarded video ad providers for mobile apps including: Unity Ads, Vungle, Adcolony, Receptiv, Admob, FAN, Mopub, Ironsource, Fyber, Tapjoy, Chartboost and Applovin

Video ads are becoming an increasingly important monetization format. Even the biggest app companies are utilizing video ads as part of their monetization strategy and specifically, mobile gaming companies have widely adopted the rewarded video ad format that provides a positive experience for the user and is positively correlated with engagement and retention according to a few researches.

In this post you will find a list of the top 12 rewarded video ad providers divided into 4 categories:

  • Video only networks
  • Ad networks that moved strategically into rewarded video
  • Video ad networks with a mediation platform
  • Media giants who recently moved in

 

FREE REPORT – VIDEO ADS RETENTION IMPACT

 

Video Only Networks

These ad networks are purely focused on monetization through video ads. They don’t offer any other ad format and some of them played a major role in educating the market on the benefits of rewarded video ads.

Vungle logo - a video ad networkVungle

Vungle are a key contributor in popularizing video ads among mobile app publishers. When they started out they were focusing on 15 second videos and were offering to produce the videos as part of the deal. Vungle is a private company and is backed by a long list of investors and raised $25M to date.

Name Vungle
Head Quarters San Francisco
Founded 2011
Employees (by Linkedin) 216
iOS Market Share (by Mighty Signal) 24% of top 200 Apps
Android Market Share (by Mighty Signal) 26% of top 200 Apps
Global Reach 500M

adcolony logo - the company was the first one to offer rewarded video ads in mobile appsAdcolony

Adcolony is the first company to offer rewarded video ads for mobile apps and they are still one of the top providers in the field. They are 100% focused on video ads and are high on the list of any app publisher who wishes to monetize his app with video ads. Adcolony was acquired by Opera in 2014 for $350M but remained a seperate entity.

Name Adcolony
Head Quarters San Francisco
Founded 2011
Employees (by Linkedin) 540
iOS Market Share (by Mighty Signal) 20% of top 200 Apps
Android Market Share (by Mighty Signal) 28% of top 200 Apps
Global Reach 1.4B

Unity ads logo - in 2014 Unity acquired Applifier to offer monetization through video ads to it's developer baseUnity Ads

Unity Ads came to life through the acquisition of Applifier by Unity. Since the acquisition, the video focused ad network experienced fast growth leveraging the dominance of the Unity game engine in the mobile space.

Name UnityAds
Head Quarters San Francisco
Founded Unity was founded in 2003 although video only came later
Employees (by Linkedin) 1,448 (Total Unity employees)
iOS Market Share (by Mighty Signal) 21% of top 200 Apps
Android Market Share (by Mighty Signal) 27% of top 200 Apps
Global Reach 770M
 
We also wrote up an in-depth full post on the comparison between ad networks. This will help provide all the details needed for choosing the right Ad Network for your mobile app. Check out the article or download the full comparison spreadsheet below for free.

FREE AD NETWORK COMPARISON SPREADSHEET

 


Receptiv, formerly known as Mediabrix is 100% focused on video ads and their unique offering to advertisers is that the ads will be exposed to users in the glory moments of the gaming experience.Receptiv (formerly Mediabrix)

Receptive who are also known as Mediabrix prior to their rebrand, have a unique offering compared to the last 3 companies mentioned. The company is based only on brand advertisers and has its head quarters in NY where they can be close to the media agencies. To the advertisers, they offer the opportunity to be associated with the winning moments of the user inside the game. To the publisher they offer diversified demand with high eCPM.

Name Receptiv
Head Quarters New York
Founded 2011
Employees (by Linkedin) 84
iOS Market Share (by Mighty Signal) N/A
Android Market Share (by Mighty Signal) N/A
Global Reach 150M

Ad networks who moved strategically into rewarded video

Applovin logo - the company offers video ads and rewarded videos among other formats but it's still considered a leading providerApplovin

Applovin was making waves in the ad-tech space last year by announcing it’s acquisition for $1.4B. The deal was experiencing some trouble and was not finalized as of today [July 2017]. Regardless of the acquisition, the company is operating as a seperate entity either way and is doing well financially. On the advertiser side, the company offers more control compared to other networks through their self-serve interface. On the publisher side they specialize in interstitials and video ads.

Name Applovin
Head Quarters Palo Alto
Founded 2012
Employees (by Linkedin) 135
iOS Market Share (by Mighty Signal) 22% of top 200 Apps
Android Market Share (by Mighty Signal) 25% of top 200 Apps
Global Reach 500M (2014)

Chartboost logo - the company started by offering interstitial ads but made a strategic move to get into video adsChartboost

Chartboost started it’s way as a marketplace for direct deals and was one of the main contributors to the adoption of interstitials as a tool to promote games within other games. Chartboost came a bit late to the video ads space but were catching up quickly by leveraging the distribution of their SDK.

Name Chartboost
Head Quarters San Francisco
Founded 2011
Employees (by Linkedin) 134
iOS Market Share (by Mighty Signal) 17%
Android Market Share (by Mighty Signal) 23%
Global Reach 1B

Tapjoy logo - one of the longest lasting independent providers who offers video ads among other monetization formatsTapjoy

Tapjoy started out in the Facebook games space where they were they specialized in incentivized offers. At the time they were known by the name Offerpal but had to rebrand after negative press related to the quality of the offers. They took on the name of a small company they acquired by the name of Tapjoy. Tapjoy had an early product for video ads back in 2012 but were not focused enough on the opportunity and saw the lion share of the market going to competitors.

Name TapJoy
Head Quarters San Francisco
Founded 2007
Employees (by Linkedin) 235
iOS Market Share (by Mighty Signal) 12%
Android Market Share (by Mighty Signal) 13%
Global Reach 520M

Video ad networks who also provide mediation

Iron source logo - the video devision came through the acquisition of Supersonic who offers a mediation platform as well as an ad-network for rewarded videosSupersonic / IronSource

Supersonic became part of IronSource via the all Israeli acquisition valued at $250M. Together they are now considered the leader in mobile video mediation. In addition to the mediation service they also have their own video ad network which helps publishers top their fill rates.

Name IronSource
Head Quarters Tel-Aviv
Founded 2009
Employees (by Linkedin) 667 working at IronSource and about 265 in the mobile video division
iOS Market Share (by Mighty Signal) 9%
Android Market Share (by Mighty Signal) 12%
Global Reach 800M (for video only)

Fyber logo - the company offers monetization through it's own demand as well as SSP and mediation platform for videoFyber

Fyber started as an offer wall provider by the name of SponsorPay but later on rebranded as Fyber and shifted more of it’s focus towards SSP and mediation with a strong emphasis on video ads. They acquired competing mediation service Heyzap to become a close second to fast growing IronSource / Supersonic platform.

Name Fyber
Head Quarters Berlin
Founded 2009
Employees (by Linkedin) 302
iOS Market Share (by Mighty Signal) 5%
Android Market Share (by Mighty Signal) 6%
Global Reach 500M

Media giants who recently moved in to the video space

Admob by Google recently moved into the rewarded video ad spaceAdmob / Google

Google needs no introduction and their mobile ad service Admob which became part of Google through the $750M acquisition in the early days of Smartphones is today the dominant way to monetize apps on Google Play. The giant rolled out rewarded video ads in March 2017. While they are showing later for the party we are sure that their size will allow them to gain momentum quickly.

Name Admob by Google
Head Quarters Mountain View
Founded 1998
Employees (by Linkedin) 76,510
iOS Market Share (by Mighty Signal) 33%
Android Market Share (by Mighty Signal) 70%
Global Reach 1B+

Facebook audience network also started offering rewarded video ads. As of June 2017 this offering is still in beta.Facebook Audience Network

Facebook dominates as a destination site for mobile ads but in recent years they have been evolving an ad network by the name of Facebook Audience Network and as of June 2017, FAN is also offering rewarded video ads.

Name Facebook Audience Network
Head Quarters Menlo Park
Founded 2004
Employees (by Linkedin) 19,150
iOS Market Share (by Mighty Signal) 28%
Android Market Share (by Mighty Signal) 39%
Global Reach +1B

Mopub logo - the twitter subsidiary is now also offering rewarded video adsMopub / Twitter

Mopub was acquired by Twitter in 2013 for $350M (read more here). It kept it’s identity since and is one of the top 2 mediation platforms and and SSPs in mobile apps when it comes to banners interestitials and native ads. They showed up a bit late to the video ads space and launched video ads marketplace and mediation towards the end of 2015. Their stronger push in the video market only happened in 2017 however.

Name Mopub/Twitter
Head Quarters San Francisco
Founded 2006
Employees (by Linkedin) 3,662 (at Twitter)
iOS Market Share (by Mighty Signal) 16%
Android Market Share (by Mighty Signal) 25%
Global Reach 1B+

 

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App Monetization

Header bidding in mobile

Header bidding created a big buzz in ad-tech spaces and the mobile app eco-system could not stay indifferent to it. There are quite a few problems slowing down the adoption of header bidding in mobile apps and it’s even possible that it’s the wrong model for mobile apps.

Header bidding – what it is

Header bidding works a bit like the role of SSP in the RTB model but different. In both SSP and Header Bidding – the publisher wants to get the best price for an ad impression that will be served to the customer. He runs an auction between the potential advertisers. Each advertiser submits a bid and the winner gets to serve the impression. This process is repeated for each impression.

There are a few differences however:

  • In SSP the auction is managed on the server side and in header bidding it’s on the client side
  • In SSP the winner pays the price of the highest losing bid (2nd price auction) while in header bidding the winner pays the full price
  • Header bidding allows combining a few SSPs in the same web page or mobile app
  • Direct deals can be treated according to their actual CPM and be added to the auction

 

FREE REPORT – Q2 AD MONETIZATION TRENDS

 

Why was header bidding created in the first place

The HB and SSP models are so similar that one might wonder why header bidding was created in the first place. This is partly related to unfair behavior by some SSPs. Specifically, Google was mentioned in a few conversations I had about the subject. The most popular SSPs including Double Click by Google has their own horse in the race – for Google that horse is Ad-x. Any SSP that is running the auction but at the same time placing a bid has motivation for to bend the rules. Real time bidding might appear to be a transparent process in which bending the rules is harder, however, when there is a will there is a why. Specifically in Google’s case it was a feature called “enhanced dynamic allocation” that allows Ad-x to cherry pick inventory from auctions being run by Double Click (their SSP) by seeing the other bids first.

Header bidding in mobile apps

As you can guess from the name. Header bidding was created for web pages and “header” refers to the part of the html code that is loaded first. As of July 2017, none of the top 200 mobile apps has implemented header bidding according to our checks and most vendors who focus on mobile apps as opposed to mobile web don’t support pre-bidding at the moment.

3rd party vendors moving in but diminishing the benefit

Of course, the opportunity for in-app advertising is huge and the players are giants such as FB, Google and Twitter among others. With billions of dollars on the table, there are strong forces who try to push the mobile app eco-system towards header bidding. This can benefit DSPs who are interested in more direct access as well as the exchange providers. However, the adaptation of header bidding to mobile apps is not trivial and some of the offered solutions are “Header bidding in a box” where the auction goes back to the server side. This of course, diminishes the benefits of header bidding as the auction is outsourced to a party that may have bias.

Mobile app advertising is CPI driven

There is a bigger problem that is clouding the future of header bidding in mobile apps. It is not even certain that header bidding can be applied successfully? One might be surprised that not many mobile app companies are pushing for header bidding despite the trend that it created in the mobile web and desktop space. The situation in mobile app advertising is a bit different than that of web advertising. Specifically, mobile app monetization relies heavily on CPI campaigns. These are campaigns that pay only if the user installed the promoted app after he watched an ad. On the other hand, header bidding requires all the parties who are interested in placing the ad to come up with a bidding price upfront. This creates an adoption problem for header bidding. As of now, not many CPI networks are willing to commit to an upfront bid before they know what their payout is going to be. At the same time, mobile app advertisers got very comfortable with the CPI based model as it minimizes the risk. On top of that, for header bidding to work it’s not enough that one CPI network will send bids upfront. You need all of them to do it. This creates a critical mass problem and no one benefits from being the first one to move.

Someone has to take the risk

Going back to the dilema of advertisers that want to pay per install and publishers that wants to earn per impression. This is one of the oldest struggles in advertising:

The publisher risk is high in the CPI model and low in the CPM model while the Advertiser risk is high in the CPM model and low in the CPI model

  • In CPI or CPA models – the publisher takes the risk and the advertiser enjoys guarnteed results
  • In the CPM model – the advertiser takes the risk and the publisher enjoys guarnteed payout

CPC used to be the middle ground but click fraud killed it and the only ones that can afford to do it is Google due to size, brand and massive investment into fraud prevention.

If header bidding gains traction while advertiser continues to pay CPI, the risk will have to be taken by the ad-networks. For example, the ad-network might be bidding $5 CPM. Let’s say they serve 1,000 impressions but these impressions don’t generate a single install. The advertiser will not be paying anything in this situation but the publisher should still be earning $5. At scale, this is a very dangerous position for the ad-network to be in. The ad-networks today have different tools on the advertiser side to monitor fraud and traffic quality and adjust the revenue retroactively. Header bidding will require a similar set of tools to be developed on the publisher side in order to minimize risks for both sides.

Monitoring of header bidding

One area that is still unsolved for header bidding is measurement. In RTB, the SSP manages the auction on the server, collects the money and pays the publisher. In header bidding, this responsibility falls on the publisher side. The auction is managed on the client side and each bidder pays the publisher seperately based on the aggregated amount in all the bids he ended up winning. This requires a system that will billions of impressions on the client side, collect all the winning bids and aggregate them to determine how much the ad-network should be paying. Without such system, the header bidding becomes useless as it will be too exposed to abuse. At the same time, the ad-networks who are now taking the risk will want more visibility into the context in which the ads are shown and to their viewability. The requirement for better measurement will come from both sides.

 

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Analytics, App Monetization

A browser screen with an eye representing impression, 29 percent written next to 1st impression, also the word volumes is written and eCPM next to a bar chart

About a week ago a friend asked me for a piece of information that should probably interest many others as well. He wanted to know how for rewarded video ads – how many impressions are first impressions vs. second impressions vs. third and so forth. In other words, he wanted to know how big of a deal first impressions are.

Impressions can be analyzed according to their sequence

To understand his quesion, we first need to understand the basics of user interaction with ads. When it comes to linear ad formats such as interstitials, video and rewarded video a user can only watch one at a time. This means that ad impressions have sequence and can be put in order. The first impression a user watches in a given day is considered the most fresh advertising experience he will get and typically yields more for the publisher while providing more value for the advertiser. A user might watch more impressions, a 2nd impression, a 3rd impression and so on. Checking the distribution of ads according to their sequecne means checking how many impressions are first impressions vs. second impressions vs. 3rd and so on and what percentage of the total volume each sequence position gets.

Results – the first 2 impressions give 46% of the volume

The results we found are presented in the chart below. We aggreagated data across all the apps using SOOMLA TRACEBACK and combined the results to a single chart. We excluded apps with less than 100,000 monthly impressiosn. The chart below represents the average with equal weights. In other words, the patterns of apps with high volume and the patterns of apps with smaller volume are equally represented.

Bar chart representing the impression volume for every impression sequence place. The logo of SOOMLA TRACEBACK is also shown

The full data can also be viewed in this table. We also included the minimal and maximal numbers accross all apps.

Impression Min Avg Max
1 13.6% 29.1% 48.3%
2 13.3% 17.4% 22.5%
3 11.6% 12.6% 13.8%
4 6.9% 9.5% 11.4%
5 4.2% 7.9% 9.8%
6 2.5% 6.3% 9.3%
7 1.6% 5.3% 8.9%
8 1.0% 4.5% 7.9%
9 0.6% 4.0% 7.7%
10 0.4% 3.5% 7.2%

First impressions matter

We already talked about the importance of first impressions from an eCPM standpoint in this article and also in this one. According to the data presented here, firstl impressions in rewarded video also matter because they represents a big chunk of the volume.

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App Monetization

Ecpm of new users could be 2x higher compared to loyal users in mobile apps and mobil games

We already wrote here a few times about eCPM decay and some of our tips were also quoted in other places. In this post we are going to talk about another type of eCPM decay – the one that is rarely mentioned. I’m talking about the trend of eCPM going down as a function of how long the user was retained in the game as opposed to the decay that happens as a result of high frequency of ads during the day.

First Test – How eCPM behaves over the life of a user

In this test we looked for users who started playing the game in a certain month and than checked their eCPM in that same month versus the eCPM in the following month and the third month. We did this test across many games to make sure the results are not isolated to a single game. In this chart below you can see the average values, the maximal ones (the game with the highest rate in that month) and the minimal ones (the game with the lowest rate in that month) across all the games we tested. Note that this test was done only with US based users and only in the following ad formats: Offer walls, Rewarded Videos and Interstitials.

Ecpm decay over time in different games showing the ecpm of users in theif first month, 2nd month and 3rd month since started playing the mobil game

There is clearly a trend here. The eCPM is going down the longer the user is retained in the game. In fact, new users can have 2x or more the eCPM of loyal users. We can attempt to explain this finding of course. One assumption is that the same behavior pattern that impacts eCPM decay also comes into play here. Users tend to grow tired of advertising. However, here the situation is a bit different. Consider the case of a user who downloaded a new game this month but might also downloaded another game 3 months ago. It’s the same user so why is he responding better to ads in the new game he downloaded vs. the older game? The answer could be that the user gets tired of ads in a given context seperately. He might learn where the ads are placed and his brain is getting trained better to ignore them. It will be interesting to see what happens if we mix up the ad placements for loyal users to see if we can engage them with the ads again.

Second Test – Does it matter where the user came from?

Here, we tried to see if a user that came organically behaves differently compared to a user that came through paid UA or cross promotion. We compared only for US based users – here is what we found.
Ecpm for users who came through different channels

So it looks like the Cross-promo traffic had very high eCPMs in the first month. Paid installs that came from Facebook also appeared higher than Organic. However, the drastic difference in the eCPM of the usres in the 1st month almost vanished when looking at the the 2nd and 3rd month. Specifically, the cross-promo installs were lower compared to organic installs in the 3rd month. In general, the eCPMs converge to the same levels almost. It seems that the impact of the source of the user only lasts for the first month and after that month the user ‘forgets’ where he came from and users behave in a similar fashion. It’s possible that users who came from an ad into your game are more likely to respond to ads in your game. The fact that the impact only lasts for 1 month could potentially be explained by users response to ads is a temporary behavior and not a long lasting behavior pattern.

Third Test – Do we see the same trend across all ad formats?

We wanted to see if all ad formats behave the same way when it comes to this type of eCPM decay. Do users lose their interest in rewarded videos the same way as they do with interstitials? We compared 3 ad formats and this time we compared not just US traffic but we allowed international traffic. To make it easier to follow we indexed the results so they all fit in the same scale.
Ecpm of users across different formats as a function of how much time they were retained in the game

It’s easy to notice that the findings are consistent across all ad formats we tested. We didn’t check banners and native ads in this study. It’s possible we will do another post specifically focused on that.

Optimizing for the long retained users

One conclusion from this data is that there should be opportunities to better serve ads for loyal users so they monetize better. Here are some ideas to consider specifically for this segment:

  • Serving ads through SSPs – these ads come with an upfront bid price and are less influenced by users’ ad engagement
  • Closing fixed CPM deals for this segment
  • Mixing it up – changing the placements for user who have been playing the game long enough

The impact on LTV calculations

These findings might also impact how companies think about LTV prediction. Many LTV models assume that eCPMs and ARPDAU are not influenced by the amount of time the user played the game. If your existing model is predicting LTV based on the 1st month’S eCPM the actual result might be worse than the predicted LTV.

What about Apps

While the reseacrch was focused on games only we expect that to find the same patterns in Apps. At least that is true for the formats we checked: Rewarded videos, Offer walls and Interstitials.

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Analytics, App Monetization

Measuring and optimizing opt in rate in rewarded video

One of the areas in which a company can drastically increase ad revenues with a relatively low effort is through the optimization of opt-in ratios to rewarded videos. Here we will show how to track and optimize opt-in rates with SOOMLA TRACEBACK.

What is opt in rate and why does it matter

Rewarded video is a unique format in the sense that it’s not forced on users. The game is offering some reward in return for watching a video and the user can accept the offer or not. These offers can be made with a pop up message, a button on different screen in the app or sometimes by replacing a call to action that would normally be prompting the users to pay. Regardless of the offer method, the users can accept or decline. The number of users who decide to take the offer is often called engaged users and the dividing them by the total number of active users is considered the opt-in rate.
One thing that is clear is that users who don’t engage with the rewarded video don’t contribute any revenue so by increasing the opt-in rate we are making the pool of monetizing users bigger. It is also known that users monetize best in their first impression and so getting more users to opt-in means you are getting a lot more of those valuable 1st impressions. Our experience has shown that increasing the opt-in ratio by x% often translates to a similar increase in the total ad-revenue.

Measuring the opt in with SOOMLA TRACEBACK

One of the easiest ways to measure the opt in rate is to use the TRACEBACK platform. You can see your overall opt in rates and number of engaged users but you can also look at specific segments and breakdowns across these dimensions:

  • Countries
  • Platform/OS
  • Versions of your app
  • Traffic sources
  • Date ranges

Looking at specific segments allows you to find improvement opportunities. The way to spot these is simple – a low opt in rate means there is a room to grow it.

What is a good opt in rate

Depending on your game of course and how well you are doing with IAP monetization you can reach as high as 80% opt in rate according to this study by Unity Ads. However, apps that focus more on IAP would be smart to first convert the users into payers and only then try to push them harder to videos. From this reason we should look at the opt in rate on a cohorted basis and set different goals depending on the lifetime of the users. These are good benchmarks:

  • 1st month – 20%
  • 2nd month – 50%
  • 3rd month – 60%

Optimizing opt in rates

Once you have identified a segment that falls below the target opt in rate, you can use TRACEBACK to optimizie it. One way to do it is to track the opt in rate in different versions of your app. Whenever you launch a new version you can immediatly compare the opt in ratio to the one of the previous version and check if you are moving towards the goal or away from it.

Example – Optimizations results in higher opt in rate in later versions 

Here is an export from our dashboard into Excel showing the opt-in rates in different version of the app.

While comparing opt in rates between different versions is easy to do and comes as a built in feature of the platform, a better approach would be to use TRACEBACK alongside an a/b testing tool. This allows customers to compare between different versions and configurations simulatnously in a randomized testing environment. TRACEBACK will present the opt in rate for each testing group in the dashboard so you can easily compare and pick the winning configuration.

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Analytics, Industry News

IMG_4209

Last Thursday, the mobile ad-tech space was surprised to hear the news about Oracle buying mobile viewability company MOAT for 850 million dollars ($850M).  This reflects a very high multiple on revenue and a very nice return for the investors that put in an aggregated amount of $67.5M – most of it in 2016. If you have been mostly involved in mobile apps – you might have not heard about mobile viewabilty at all and are wondering how can such an unknown aspect of mobile advertising can be worth so much money to someone. To understand this – we need to dig a bit deeper.

Over 95% of the impressions are ads for installing other apps

In a recent blog post we announced a new and super exciting feature that was recently added to the Traceback platform. At SOOMLA, we are all about giving visibility to the publishers to know more about their in-app ads so knowing who is advertising in your app is a big part of that.

One of the things we quickly realized when starting to look at this data is that most of the advertising activity is driven by demand coming from other apps. These are typically CPI campaigns that only pay when the user installed the advertised app. The surprising part is that the number of ads coming from brand advertisers is very low. In the last 2 years we have seen a growing number of indications that the brands are coming to mobile.

Chart showing US mobile ad spending by industry in 2015. Retain comes first with $6.65B followed by the financial services with $3.49BIn this link you can find a report from eMarketer breaking down mobile ad spend by category. In the image to the right you can see that while retial ad spend might have a mix of brand and app install campaigns, the following categories are dominated by brand ads:

  • Financial services (Capital One, Geiko) – $3.49B
  • Automative – $3.43B
  • CPG (Procter and Gamble) – $2.33B

With at least 10 billion dollars ($10B) being spent on brand campaigns, we would have expected more of these ads to show up in mobile apps. We are not alone in our expectation of course, in 2014 Eric Seufert wrote:

“If the largest brand advertisers shift just 10% of their overall budgets to mobile, they’ll match or exceed the money spent by the app economy’s behemoths – and they’ll be competing for the same ad inventory.”

Well, according to the eMarketer report, almost 50% of the digital ad spend of these brands have shifted to mobile and yet these ads are no where to be seen. Eric was not wrong to expect a change but we all missed one important thing – the ‘other’ mobile industry.

Mobile web and mobile apps – two separate ecosystems

From a user perspective, mobile is a single experience. Opening the URL www.weather.com or opening the weather app will result in a very similar experience. The technological aspects are very different between mobile web and mobile app and each one has a separate eco-system when it comes to mobile advertising with very little overlap in between. The mobile web ecosystem was pretty much inherited from the desktop space. Each desktop advertising player in the ecosystem gradually started adding mobile web support so eventually the mobile web and desktop web ecosystems operate in a very similar way. The mobile app ecosystem evolved mainly from the need of gaming apps to acquire massive amounts of users. This resulted in an install focused industry with a great focus on attribution – a concept that mobile web companies haven’t even heard about.

What is viewability and how it evolved

In 2013, early reports started coming in showing that when brands are paying for users to watch their ads, they are often not getting what they paid for – nearly half of ads are not seen according to Comscore report from 2013. In 2014, a report from Google claimed it’s actually 56% of ads that are not viewable. This made brands worried about buying display ad inventory online and gave a big push to a category called viewability measurement. Here are some of the problems causing low viewability rate:

  • Ads can be shown In a window that is in the background and hidden by another window
  • Users sometime scroll away from the part of the page where the ad was shown
  • Time on screen is to short or video playback was stopped early
  • Traffic generated by bots rather than humans

MOAT has put it together nicely in this web image:
Non-viewable impression can be caused by 4 things: Out of focus, Out of sight, missed opportunity (area), missed opportunity (time)

The need for a solution forced the digital advertising industry to act together and set official guidelines. The Internet Advertising Bureau (IAB) together with the Media Rating Council (MRC) has published a first set of guidelines in June 30th 2014 which later evolved into version 2.0 of the guildelines in August 18th 2015.

Viewability in mobile web

As mentioned before, the mobile web ecosystem is a replica of the desktop web ecosystem in terms of advertising at least. Viewability measurement quickly became an issue in this industry as well and in June 2016, MRC published their mobile guidelines for viewability measurement. These guidelines are for mobile apps but we will touch on that later on. The main guidelines in the publication are these:

  • Client side measurement
  • Measurement of offline activity for apps
  • Filtering non-human ad views
  • Differentiate viewing from pre-rendering and pre-fetching
  • Detect when ads are out of focus
  • Pixel requirement: at least 50% of pixels were on screen
  • Time requirement: 1 continuous second of a post-rendered ad that meets the pixel requirement

The MRC is also accrediting viewability measurement companies who meet the criteria based on an annual audit.

Who can measure mobile viewability

The top 3 providers for desktop viewability measurement have all been accredited for mobile viewability measurement. Here they are:

These companies cater for both advertisers as well as publishers.
Brand advertisers are willing to pay for viewability measurement to know that their ads are getting viewed. They would typically deduct the non-viewable ads from the media campaign when calculating delivery and payout. However, most brands would also consider publishers with low viewability scores as non-safe and illegitimate media. It is common for brands to have a viewability threshold of 90% or 95% for publishers. This means that publishers with a low score will simply not get any brand ads.

On the other side, publishers also have an incentive to have their sites and apps measured. The basic reason is that if you don’t allow measurement you will not receive brand ads. On top of that, publishers who sell directly to brands or ad-networks who represent them want to know the metrics and data points about their media so they can use it in their pitching.

Mobile apps have been a slow adopter of viewability

When you check how many app publishers have a viewability measurement SDK installed you discover that most of them don’t. We can look at the 200 most downloaded apps (top free chart) on both iOS (Top chart link) and Android (Top chart link). These are typically the apps who will have advertising in them as they attract a lot of users.

  • On iOS – only 5 out of the top 200 have viewability SDK – 2.5%
  • On Android – only 21 out of the top 200 have viewability SDK – 10.5%

Our assumption is that the more you advance towards the long tail the less likely apps will have a viewability SDK since the long tail apps are still focusing on more basic aspects.

SDK fatigue slowing down adoption and enticing provider collaboration

One of the problems slowing down the adoption of viewability measurement in the app ecosystem is the need to integrate an SDK. To make things even worse, an app would need to integrate the SDKs of all 3 providers (MOAT, IAS and DV) to enjoy the full benefits. A recent open source initiative is aiming to solve at least part of the problem. It started out of IAS but was later handed over to the IAB to manage the project. The 3 providers have agreed to collaborate and support the single open source SDK that will make things a bit easier on app-publishers.
At the same time, some of the ad-networks have increased their interest level in viewability measurement as a way to be more attractive to brand campaigns. Some of them are working on bundling the viewability SDKs inside their SDKs so that their entire inventory will become available to brand campaigns. That however, brings another risk. Most apps user multiple ad-networks so if many ad-networks follow the same path an app could carry a number of viewability SDKs at the same time.

Viewability might not be enough

It’s clear that brands would not advertise in mobile apps without having viewability measurement. If you haven’t heard about the P&G $2.8B Ultimatum to the media industry – you can read about it here. However, it’s not clear if having viewability measurement is sufficient to make brands start advertising in mobile apps. The condition might be necessary but not sufficient. In other words, there could be other road blocks for brands to advertise in mobile apps. Here are some other reasons why brands could be staying away from mobile apps:

  • 50% of mobile app traffic is in games and brands have shied away from games historically
  • Brands often have issues with incentivized advertising and rewarded video falls into the category
  • The lack of audience data and tools for advertisers to target specific segments
  • Today brands are separated from mobile apps by multiple hops which takes a cut and reduces the eCPMs for the publisher so his mediation provider might not allow them to show

Oracle might be the last one laughing

So if you think of the projections for mobile viewaiblity. MOAT is already positioned as the one of the top 3 and some say the leader of mobile viewability in a market that is expected to double itself in 3 years. Considering that mobile viewability hasn’t even made it into mobile apps – the market can double in size again when Apps realize that they can get the brands competing for their inventory as well. So if MOAT can still grow 4x in 3 years, maybe the price Oracle paid is not that high after all.

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Analytics, Research

Mobile Attribution 101 - the complete guide. Header banner.

For a while now the business of advertising apps have zoned in to one standard business model – cost per install (or CPI). The CPI business model has pros and cons like any other business model but it proves less fraudulent than clicks, less risky for the advertiser than CPM and more uniform compared to more advanced CPA models. With that, measuring installs and deciding which sources they came from became a highly significant part of any marketing campaign. For the advertisers, it’s not only about fair business models but also about measuring marketing effectiveness. Attribution of installs to a sources, is a key ingredient in understanding which marketing activities and campaigns are the profitable ones. For the ad-networks, being able to receive the post-backs and post-install conversion data is an important feedback loop that allows them to optimize their campaigns and maximizing yield as well as returns for the advertisers.

3rd party attribution vs. in-house

There are a few companies that tried building attribution solutions in-house. Sure, the technology is quite complex but app companies are technologically capable. Especially the gaming companies tend to prefer in-house infrastructure when it comes to analytics. The ones that did figure out how to build their own attribution solution soon realized that there is not much use for it. The most prevalent business model between app advertisers and ad-networks is CPI. Since advertisers pay the ad-networks based on installs – both sides needs to agree about the number of installs so when the advertisers build their own attribution, they soon realize that many ad-networks are not willing to trust it’s measurements. Instead, they request that a 3rd party partner will be in charge of keeping the score.

FREE E-BOOK – TOP 10 MOBILE GAMING REPORTS

Facebook’s attempt to become a player

In 2015, in light of the growing market, mobile advertising giant, Facebook also made an attempt to become a player in this market and own a bigger piece of the end-to-end marketing solution. The market, however, rejected the move. Publisher demanded that a 3rd party will be in charge of the measurement.

The battle of the attribution providers panel at Casual connect where the only agreed upon topic was the lack of legitimacy of Facebook as an attribution provider

In the picture (credit: Venture Beat): the battle of the attribution providers panel at Casual Connect where the only agreed upon topic was the Facebook move legitimacy (or lack of).

How did this market get so big?

The 4 big attribution providers track about 18 billion dollars in ad spend worldwide. This is based on various points of information showing that Kochava measured 3 billion dollars in 2015 and Appsflyer measured 6 billion dollars in 2016. We can estimate based on the pricing of the providers that the attribution fees reached about 1% of that – 180 million dollars in 2016. The market is already big and is expected to double it’s size by 2020 reaching over $360M. Some of the driving forces behind this growth are the ad-networks themselves. Each attribution provider supports hundreds of networks and each ad-network has a lot of people in the field, meeting customers on a daily basis. Thousands of people going to every show, attending every conference and talking about CPI campaigns. Once ad-networks agreed that 3rd party attribution is a must-have, it was only a matter of time before the customer accepted that.

Attribution methods and approaches

Since the early days of attribution, there have been people saying that the models are not accurate. We live in a world where each user is being exposed to dozens of advertisements every day, often across different mediums. By the time a conversion, an app install, has occurred, the user is likely to have seen an ad for that app more than once and sometimes even as much as 10 times. Despite that, attribution models always give credit to a single source, single campaign and single ad-group. The credit is always given to the last click if such a click happened recently and if there is no click, it will be given to the last impression if it happened recently. It’s always the last one. Critics say rightfully that this is an inaccurate way to do it and there are better models. However, this is one of the areas where the delicate balance between ad-networks and advertisers mandates a simple model that is easily decide and so last-click attribution, while not perfect, is the best we have and will stay the attribution model going forward.
Last click, view thru, first click and other methods can compete but the winner is last click

The evolution of click fraud detection and IAP measurement

Being a key ingredient for measuring marketing returns or ROAS (return on ad spend). The attribution providers got pulled into providing more analytical services. The attribution dashboard became the go to screen for understanding install volumes from different sources. As the industry became more aware of the issues around traffic quality, attribution providers got pulled in to provide additional reports.
One direction in which attribution providers evolved is the area of post-install metrics such as retention tracking and conversion to payers via in-app purchases. Publishers also started using post install metrics reported by the attribution providers to set goals for user acquisition channels. It’s common these days to see an IO (Insertion Order) with traffic quality criteria. Beyond measuring the in-app purchases, attribution providers also developed the ability to distinguish real purchases from fake ones.
The other direction in which attribution developers were asked to evolve is install fraud detection and prevention. While generating fraudulent installs is much harder than clicks, publishers have a lot of motivation to do so and methods were developed to manufacture installs. All big 4 attribution providers today have developed mechanisms to detect such activity and remove it from the reports. By doing so, they are providing more value to the advertiser who only pays for real installs.

The top 4 mobile attribution providers

appsflyer logo - mobile attribution provider

Appsflyer is the most VC backed provider out of the bunch. They hold an impressive share of the top 200 companies and have the biggest penetration in far east countries. They are based in Hertzelia, Israel with offices all around the world. Their unique pricing model allows app companies to start for free which makes them highly attractive for smaller companies. Appsflyer started in the Microsoft Accelerator and is still rumored to be using Microsoft cloud infrastructure – Azure.

Name Appsflyer
Headquarters Hertzelia, Israel
Employees (by Linkedin) 208
Market Share (by Mightysignal) 12% of Top 200 Apps
Notable Customers Hulu, Cheetah Mobile, The Weather Channel
Funding raised $83M
Founded 2011
Supported ad-networks 2,148 (as of 1/1/2017)

logo image of adjust, the mobile measurement partner

The Berlin based provider has made itself a name in quite a short time. They are an official measurement provider for Facebook and pride themselves as being the leader among the top 200 apps. Adjust is leveraging their own private cloud infrastructure rather than a hosted one which also allows them to be the best at protecting user privacy.

Name Adjust
Headquarters Berlin, Germany
Employees (by Linkedin) 127
Market Share (by Mightusignal) 17% of Top 200 Apps
Notable Customers Spotify, Zynga, Rovio, Miniclip
Funding raised $29M
Founded 2012
Supported ad-networks 700+ (as of 1/1/2017)

Tune Logo - among other marketing and analytical services, tune also offers attribution solutionTune is a popular attribution choice. The company had two products going by separate brand: Has Offers and Mobile App Tracking. They are the only one of the four that is not focused completely on attribution and are also not an official Facebook measurement partner. To overcome this problem, Tune provides a solution for Facebook attribution leveraging deep linking.

Name TUNE
Headquarters Seattle, US
Employees (by Linkedin) 371
Market Share (by Mightysignal) 11% of Top 200 Apps
Notable Customers Uber, Lyft, Supercell
Funding raised $36M
Founded 2009
Supported ad-networks 1,062 (as of 1/1/2017)
 

FREE AD NETWORK COMPARISON SPREADSHEET

 

kochava logo - attribution company catering to big media companies and others

Kochava is the only bootstrapped provider out of the top 4. They made themselves a name by attracting top tier media companies such as ABC, CBS and Disney as well as the mobile gaming giant – MZ. The company recently launched a new product called Kochava Collective to help their customers reach relevant audiences and also started offering a free version of their platform under the name – Free App Analytics

Name Kochava
Headquarters Sandpoint, US
Employees (by Linkedin) 82
Market Share (by Mightysignal) 11% of Top 200 Apps
Notable Customers MZ, ABC, CBS, Bigfish
Funding raised  Bootstrapped
Founded 2011
Supported ad-networks 2,800 (as of 4/15/2017)

Mid market solutions with an attribution feature

The 4 top providers mentioned above are highly focused on attribution and cater to customers who often build their in-house analytics and use the API offered by these providers to pull the data into their own BI or data warehouse system. However, there is a group of companies below the top tier that are relying on 3rd party analytical tools rather than a tool that is built in-house. These companies are preferring a full-solution approach that includes both analytics as well as attribution. This opportunity is considered the mid market of the mobile attribution space and there are companies who are starting to cater for it. Here are 2 providers in this category:

  • Tenjin – provides analytics solution with built in attribution
  • Apsalar – provides analytics, attribution and audience building under the same roof

The ability of companies to simply add attribution as a feature on top of something else is a result of the maturity of the market. The connections with the ad-networks became standardized and there are now also tools to attribute Facebook traffic with no need to get their official blessing. This is the reason why we also see companies like Singular adding attribution into their marketing and cost aggregation platform.

It’s important to note however, that these solutions are often lacking in terms of how many ad-networks accept them as an authority to counting installs. One of the key features of the attribution providers is the ability to serve as an unbiased 3rd party that is accepted by both sides when it comes to discrepancies in install counts that impact how much is paid by the advertiser to the ad-network.

Free attribution – what are the options and what’s the catch

Publishers might be surprised sometimes by the fees for the attribution service. Take Appsflyer for example – they recently tripled their price from $0.01 per install to $0.03 per install. Furthermore, if a certain app has a low volume of installs, the price is even higher. Tracking 100,000 installs for example will cost $4,000 per month. Quite a high fee for apps that are just starting out. This is part of the reason why many publishers are looking for alternative free solutions.

Option 1 – Branch Metrics deep linking

Branch offers a free deep linking solution. This means that every time an ad is shown a savvy marketer can provide a dedicated click url with additional parameters and those parameters will magically find their way to the app. Unlike other deep linking solutions, Branch takes the extra step to report these parameters back to their own dashboard and present it for the marketer to monitor the performance of each channel.

What’s the catch:

  • Ad-networks that get paid based on CPI might not agree to trust this solution
  • Advanced features like: postbacks and fraud protection are not available
  • Not scalable – every new ad requires generating additional links and passing them through to ad-networks

Option 2 – Facebook and Google attribution

For apps that only buy media on Facebook and Google, this might be a good enough solution. Both companies have an SDK that allow the app to report post install events. The impressions, clicks, installs and post-install conversions will be shown in a dashboard along side the cost. This means that marketers can calculate ROI on each campaign very easily and take action right away without leaving the dashboard.

What’s the catch: Facebook and Google are not neutral. They actually have an interest to attribute installs to themselves and given Facebook’s history with reporting errors one might be worried about putting all his faith in them. In addition, installs might be reported twice as there is no 3rd party overseeing both platforms.

Option 3 – Free App Analytics by Kochava

This is an interesting option for app publishers. Free App Analytics offers the same features as the main Kochava solution and is using the same infrastructure and reporting interfaces with one major difference – it’s completely free.

What’s the catch: Publishers most provide Kochava with a license to use the data in Kochava Collective. This means that advertisers will be able to apply more advanced targeting for these users but the ads might not necessarily appear in the app that is using the Free App Analytics solution. Might be a small price to pay for saving a few thousands of dollars a month.

In addition to these 3 options, we have also heard the names – Attriboost and DCMN as potential free attribution vendors.

How to compare between different providers

With so many options to choose from, it’s easy that some companies are finding it hard to choose. The first question to ask yourself is – do I have an in-house analytics solution. The answer to that will tell you if you are looking for a point solution for attribution or a full solution that includes both attribution, analytics and flexible visualization system that allows you to create dashboards focused on different aspects of the data.

If you want to take the scorecard approach for comparing providers that’s a good approach according to Saikala of SpaceApe. Here is her free template for comparing attribution providers with a spreadsheet. She is also giving her opinion and tips how to use the spreadsheet in this article.

Where is the attribution market headed next

One of the areas that is still unresolved is the single ROAS view. Advertisers are surprised that even after years of evolution in marketing measurement tools, there is no single provider that allows them to see a per source/campaign/ad-group view with the following components:

  • Cost of the marketing activity
  • Returns on the marketing generated via in app purchases
  • Returns on the marketing generated via in app advertising

This is the very fundamental view that allows marketers to evaluate the business merit of each marketing activity. However, today it is still very hard for marketers to get this view generated. Attribution providers realized this problem is an opportunity for them to provide a more complete solution and are now looking into two areas of expansion:
Aggregating cost data from all the different ad-networks and bringing them to the same view that summarizes the install counts and in app purchase revenues. While Singular was the first company to offer this type of aggregation, attribution providers identified the value for the marketer is far greater if he can get both elements from them rather than using an additional provider.
Assigning ad revenue to users and attributing them back to marketing activities to complete the ROAS picture. This area is gaining a lot of momentum as apps are expected to increasingly rely on ad-revenue according to industry forecasts. The first company to provide a solution in this field is SOOMLA but attribution providers are now working to add these capabilities by partnering or building in-house.

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