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, 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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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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Announcement, Events, Marketing

Ultimate GDC Spreadsheet with 715 Companies

We've got the ultimate spreadsheet containing 715 companies who are attending GDC in San Fran this year.  Want to take a peak?

GDC is next week and we can’t be more excited. The San Francisco based Game Developer Conference is the biggest event of the year for mobile game developers around the world and attracts some of the biggest names in the industry for the entire week. The conference has many satellite events, mixers, dinners and parties. The biggest satellite event is Game Connection America which is more focused on business transactions and brands itself as the “deal making summit”.

Who is coming?

With so many things going around it’s easy to get lost so we wanted to offer a quick way to know what companies and people will be there. In the spreadsheet below you can find a list of 715 companies that will be at GDC or one of the satelite event. For some of them, the spreadsheet also includes the names and titles of the attendees.

Downloading, Copying and Editing this Spreadsheet

Here is a direct link to the spreadsheet.

You can download this Spreadsheet or copy to your own Google Drive from the file menu once you open it. Please do not use the “Request Access” option as we will not approve those.

You can also download an Excel version here.

If your company is not in there and you want to add yourself to the list, simply email us to scottie [at] soomla [dot] com. We will be happy to add you.

Of course, SOOMLA will be there too so if you want to meet – drop us a line to scottie [at] soomla [dot] com.

How to connect with other companies

GDC does offer a meeting system but it’s not considered a very good one. On top of that, most of the people who are coming to the event will not actually be buying a ticket to GDC. This is why the spreadsheet is even more important. To connect with some of these companies we recommend these 3 ways:

  • Game Connection – the ticket is a bit expensive but this is the most effective way to generate meetings during GDC week.
  • GDC Meet to Match – system to arrange and request meetings to registered attendees.
  • Linkedin – simply send people connection requests and ask for a meeting

 

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Analytics, App Monetization, Game Design

Playable Ads 101, Best Practices and Top Providers

Playable Ads 101 - Best Practices and Top Providers

One of the hot trends in the last 6 months in mobile game marketing has been playable ads. MZ, also known as Machine Zone, was an early adopter with Game of War and Mobile Strike but many ad-networks are offering them now, more advertisers have discovered their effectiveness and players are getting used to them.

Playables of different kinds

The first playable ads started as HTML5 ads served through MRAID protocol. However, following their success, more formats have evolved. The video ad networks started moving in and have evolved two formats.

  • Interactive video end cards – This format starts as a regular video that plays for 15 or 30 seconds and once the video is over it is replaced with an HTML5 playable experience.
  • Interactive videos – These videos are broken down into 3 or 4 parts and the user has to take a simple action like clicking a button in order to continue.

Serving playables in the publisher game

While the experience from the advertiser is quite similar, on the publisher side there are two main ways to get playables in the app. There are playable ads that get served through standard containers such as interstitial. Today, if the publisher implements Admob or Mopub SDK he is likely to get some playable ads unless he blocks them. With some providers and specifically with Admob, there is no way to block them. The same thing goes for the rewarded video container – most of the video ad networks are now serving the playable ads described in the previous section when the publisher calls a rewarded video ad. On top of these there are also companies who serve playable experiences through a dedicated SDK.

The dedicated SDK approach has some pros and cons. On one side it leads to an improved ad experience for the advertiser. From the publisher’s perspective it means better control and can lead to a more expectable user experience. However, it does requires the publisher to integrate another SDK which is always fun :).

Designing playable experiences inside the game

In terms of game design, publishers have 2 main choices. The first one is to integrate playable ads in standard containers such as interstitials and rewarded videos. This is the default option and unless blocked by the publisher most ad networks will hijack standard containers and serve playables in them.

The main problem with this experience is that it’s not expected by the user. A user might sign up for watching a rewarded video in return for some in-game incentive but than get a playable ad instead. Even worse, an interstitial container might contain a playable ad at the end of a regular play session where user expects a much shorter interruption if any. Based on the data SOOMLA collects, this hijacking has a high toll on user churn. Finally, the practice of injecting a playable ad experience into a regular container creates an unfair competition in your waterfall.

As explained by this analysis made by Kongregate the playable ads generate higher eCPM for the publisher so networks that serves high amount of playable ads are more likely to produce higher eCPM rates and win the first impression. The alternative is to introduce a specific inventory for playable. A publisher can design a special button with a game controller icon and offer increased rewards for users who are willing to try a new game. This creates an opt-in experience for the playable ad rather than an hijacked one.

FREE REPORT – VIDEO ADS RETENTION IMPACT

Who makes the playable ads

Ads are traditionally made on the advertiser side of things but with playable ads the advertising company take a very active role. This is a typical step in the evolution of an ad-formats where newer formats are produced by the ad-network or ad agency and as the market get used to the format the advertising companies take on the production task. Today most of the playable ads are produced by the provider rather than by the advertiser with only a handful of advertisers producing their own playables.

How playable ads might evolve in the future

Today, there are 2 main challenges with playable ads. One is that they don’t accurately reflect the game play of the advertised app – this can lead to lower conversion rates. On the publisher side – users find them to be repetitive – one might have to play the same 2 moves over and over again every time the ad pops up. This might be some of the reason why playable ads tend to churn more users. One evolution that we might see in the market are ads that remember the state of the user and offer progression from one ad view to another. This can be a much better user experience on the publisher side and potentially more qualified installs for the advertiser.

Winning Playable Ad Experiences

  • Applovin – Word Cookies
  • Chartboost – Bubble Island
  • Ironsource – Lords Mobile
  • CrossInstall – Solitaire

Top providers offering Playable Ads

Today most of the top rewarded video providers are offering playables:

  • Ironsource 
  • Applovin
  • Chartboost 
  • Vungle  
  • Inmobi / Aerserv
  • Adcolony
  • Apponboard
  • Cossinstall
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Analytics, App Monetization, Tips and Advice

Data Based Formula – Which Advertisers to Block

Finally a Data Based Formula for deciding which advertisers to block

One of the oldest debates in the short history of in-app ads have been what advertisers should be blacklisted by publishers. Many companies have already started using SOOMLA to gain valuable data in support of such decisions as shown in this case study. However, we’ve noticed recently that many publishers face a problem, even when they have the data.

The problem – how do you weigh ad revenue vs. churn from ads

Even when companies have the full data of the eCPM rates paid by each advertiser along side the churn rates, it’s not always enough to reach a complete decision. What’s needed is a formula to weight the pros and cons. In other words, companies want to know what eCPM lift justifies a 1% lift in churn.
For example, let’s consider two advertisers:

  • Billionare Casino with eCPM of $17.54 and ad resulted churn 5.2% (from users who clicked the ad, how many haven’t returned)
  • WGT Golf with eCPM of $27.27 and ad resulted churn of 18.5%

Who do you think is better? Does the eCPM increase justify the additional churn?

The analysis – revenue lost vs. revenue made

To answer the question, it’s not enough to look at the basic parameters. The basic analysis that needs to be made is how much revenue was lost vs. how much revenue was made. To determine this, we have to first put a value on a lost user. A good place to start is the overall LTV of a user. If the ad is presented to the user in the first days of activity than the overall LTV of the user is pretty close to the value. For users who have been in the game for some time, the value of a lost user would be the future LTV from that point on. It’s important to note that the number could be higher due to users already having an emotional investment in the game but it can also be lower if the game doesn’t have a lot of depth. Right now, we will assume the value for all lost users is the overall LTV. Now that we figured out how much a user is worth we can multiply it by the number of users lost to determine the amount of potential revenue lost. This factors in the churn ratio but also the CTR as the churn ratio is calculated from the clicks. The revenue that was made is given directly by SOOMLA in the advertiser analysis screen.
Going back to our example – the value of a lot user was determined at $1.28:

  • Billionare Casino – generated a total of $623 and while their churn was only 5.2%, the number of users churned was 1,509 so potential revenue loss was $1,931 and the net revenue was a loss of $1,308
  • WGT Golf – generated a total of $1,573 and only churned 188 users which are worth $240. Net revenue made was $1,333

As you can see, comparison becomes much easier this way. One has a negative impact and the other has a positive one.

FREE E-BOOK – TOP 10 MOBILE GAMING REPORTS

Comparing 2 advertisers with positive net revenue by using nCPM

The analysis above does help weed out advertisers with negative contribution, however publishers also wants to be able to compare between advertisers and give more priority to the ones with higher eCPM and low churn. In many cases, there is a need to compare the net revenue of each advertiser on a quantity of 1,000 impressions to determine who the impressions should be given to. This ratio can be called the nCPM / nRPM (net revenue per mile) as opposed to eCPM / eRPM (revenue per mile).
So back to our example:

  • WGT Golf – generated a net revenue of $1,333 on 57.7K impressions which makes his nCPM $23.1

Improving the formula

One way to improve this analysis is to have a better understanding of the lost revenue. Some games don’t have the depth to keep users retained for a long time so the loss might be lower while for other games. Also, some of the games only expose users to ads once they predict the potential for IAP revenue is very low. If they are successful in such prediction, the revenue loss from churning such user would be much lower.

Better way to prioritize advertisers

nCPM is a better way to prioritize advertisers than eCPM. However, the tools available to publishers for optimizing are limited to blacklisting. In reality, the task of prioritizing advertisers for the publisher mostly falls on the shoulders of the ad-networks. The ad providers have an algorithm that tries to predicts the eCPM of each ad. In an ideal world, there will be a way for a publisher to add a “toll rate” for each advertiser rather than just blacklisting them. This will allow the ad-networks to prioritize based on nCPM instead of eCPM.

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App Monetization, Tips and Advice

Gazeus – Measuring Ads Allowed Us to Save 66% of Our Budget

How Gazeus saved 66% of their budget by measuring ads

At SOOMLA we are always advocating being more data driven when it comes to mobile app monetization. This way we are happy whenever we see a publisher stepping up their data game and starting to measure their monetization. In an earlier post we showed how Kongregate doubled their traffic by measuring ads and in this post we will see how Gazeus saved 66% of their budget.

About Gazeus

If you don’t know Gazeus I highly recommend watching the video below. The presentation is given by Paula Neves, CMO @ Gazeus.

 

 

Here are a few key points to know about Gazeus:

  • Owner of the Jogatina brand that is known from web/PC games
  • 40+ Titles
  • 9M MAU in mobile
  • 99% of mobile revenue coming from Ads
  • Strongest Geos – US, Canada

Measuring Ads – Critical and Challenging Problem

Since 99% of the revenue is coming from Ads, says Paula: “LTVs are lower and UA has to be spot on”. She then explains that they had ROI calculations only at the app level as a whole but not per campaign. On the product side, they didn’t have sufficient feedback loop for product to know if the features they are making are good. For these reasons, ad measurement is critical for Gazeus. In the video, Paula explains that her background is in the eCommerce domain where she took LTV for granted but when she came to Gazeus, she discovered that an entire infrastructure has to be created just to receive an answer for this question. This root cause of that is, of course, lack of user level data from the ad-networks.

CASE STUDY ON ADVERTISERS CHURN & eCPM

The outcome – 66% of marketing budget saved without sacrificing growth

Skipping to the bottom line quickly. Paula explained that the ability to measure ads allowed them to be much more focused in their UA efforts. In one example she gave, having the granular ad revenue per user data allowed them to chart the LTV curve for each cohort. By comparing the curves of iPad vs. iPhone users coming from different campaigns they were able to save a big portion of their ineffective marketing spending. The bottom line is that they were able to spend as little as 1/3 of what they were spending and still get the same revenue growth.

Gazeus ad measurement infrastructure and SOOMLA

In the middle part of the video, Paula explains how they built their ad measurement infrastructure. They had to collect API level data from each one of the ad-networks. For each one, different types of fields and dimensions are provided so normalizing that and the timezones is another challenge altogether. From the client side, they had to set up an event to be fired every time an ad is served. The server than allocates the average eCPM of the previous day for the relevant country and ad-format for each impression and multiplies by the number of impressions to generate user level revenue data.

Of course, a publisher doesn’t have to go through all this in order to get user level ad revenue. They can simply work with SOOMLA to get the data through API, directly into Amazon or via SOOMLA Dashboard. For Gazeus, they decided to go with their own solution since they use a few unique networks in Brazil that SOOMLA doesn’t support as Paula explains but for most publishers it’s not an issue. Here is a table to help you evaluate both options.

Build In-house Using SOOMLA
Initial Effort Up to 6 man months 1 week
Calculation Method Average eCPM Method* True eCPM Method**
Ad Network Support Tailored The 25 Most Popular Networks
Maintenance Internal resources Outsource to SOOMLA
Extra features N/A Advertiser identification, ad whale lookalikes, postbacks
Cost (for 5M MAU) Increase in hosting bill can reach $2,000 $2,500 to $3,500

*Average eCPM Method

  • In this method, the ad LTV system queries the API of each ad-network to retrieve the average eCPM of the previous day for each country and ad-format.
  • At the same time, the client side of the app reports to the ad LTV system each time an impression is shown to a user.
  • The ad LTV system calculates the revenue for the user by multiplying the number of impressions the user made in each ad-format by the average eCPM of that ad-format/country in that day and than dividing by 1,000. Than it sums the results for each ad-format for that user.
  • The process is repeated each day and data is aggregated on a user level basis and on a cohort basis.

Putting it in a formula – this would be the calculation of the revenue for user x (Imps is short for impressions here):

**True eCPM Method

  • In this method, the ad LTV system queries the API of each ad-network to retrieve the average eCPM of the previous day for each country and ad-format in addition to other dimensions that are available.
  • At the same time, the client side of the app reports to the ad LTV system multiple events for each impression: impression event, clicks, installs, ad interactions, advertiser identity, business model, bid levels.
  • The ad LTV system leverages advanced algorithms to deduct the true eCPM of each impression based on the data collected from the client side as well as the averages reported through the APIs. It then calculates the revenue for each user by summing up the contributions of each impression he viewed.
  • The process is repeated each day and data is aggregated on a user level basis and on a cohort basis.

The formula below shows the revenue calculation for user x where ‘n’ is the number of impressions he made in that day.

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Analytics, App Monetization, Tips and Advice

10 Mistakes that Will Keep Your Ad Revenue Low

10 Mistakes that will keep your ad revenue low

Most of the companies I talk with are interested in increasing their ad revenues. For many, increasing ad revenue is critical as they need to be profitable on their UA activities. Despite that, I see many mistakes companies are making that prevent them from improving on this front. Here are the top 10.

No new SDK policy

Some companies seem to have decisions made from the engineering to the business and not the other way around. It’s true that there are many SDK companies knocking on the door at any given time and many of them promise more than they deliver. However, not adding any new SDKs is like saying – our strategy is to not change anything. In a dynamic space such as the mobile app market – this is a big mistake.

How to fix it:

Establish a process for vetting new vendors based on business potential, risk and costs and make sure you include alternative cost as part of that equation. This will help you re-gain the confidance of the engineering team that you are not just making random requests.

No ads for payers

In 2018 mobile app publishers will make more revenue from advertising than from IAP and publishers needs to start thinking about ads as a main source and not a secondary one. In 2016, SOOMLA revealed the existence of ad-whales. These users can contribute $30 or $50 or $100 in your app – more than some of your payers contribute.

How to fix it:

It’s time to design monetization patterns where ads and IAP complement each other. Think about a user that just purchase a bag of coins maybe he can get 25% extra coins for watching a video right after the purchase. There are many other options to experiment with just make sure you measure the impact accurately.

Not measuring your ad monetization

You can’t manage what you can’t measure” said Peter Drucker in a famous quote. In other words, the path to improvement goes through measurement and the domain of app monetization is not different. Ad revenue has been lacking proper measurement tools but the situation is quickly changing and monetization measurement is becoming a neccessity for any company who takes their ad revenue seriously.

How to fix it:

Implement a monetization measurement system that allows you to get granular data about how your users interact with ads in your app. As Jeff Gurian from Kongregate said: “Ads count so count your ads

Banning competitor ads

The debate whether or not to show competitor ads in your app has been going on for years. Some companies simply block any app that seems competitive while others enjoy the high eCPMs that competitors could be generating. A recent study by SOOMLA revealed that ads showing competitors might not be the ones that take your users away and each app needs to be evaluated on a case by case basis before deciding to blacklist.

How to fix it:

There are several SDK providers that can give you insight into who is advertising in your app. Implementing such an SDK in your app will enable a more data driven approach to the decision whether or not to advertise a specific app or block it.

One size fits all

When it comes to ads, not all the users are created equal. For example, some users may respond better to rewarded videos while others yield more revenue from banners and native ads. Serving the same ad experience to all the users is a great way to keep your revenues low.

How to fix it:

Track the yield of each user from each ad-type (for example – by using SOOMLA) and use the data to segment them into groups. You can then build more tailored ad experiences that will improve your ad arpdau.

Introducing rewarded videos late

While it’s clear that most ad formats have a negative impact on retention and payer conversion it’s also clear already that rewarded videos do not have such impact. Since 80% of your users are likely to not survive the first week, delaying ads for 7 days will surely have a negative impact on revenue.

How to fix it:

Introduce rewarded videos as early as possible and even include them in the tutorial. If you want to be a bit more cautious, you can do this only for users who are not likely to pay.

FREE REPORT – VIDEO ADS RETENTION IMPACT

Incentives that don’t add up

In many apps and more specifically in games, the users are rewarded for watching videos. The rewards can range from an extra life to coins or pretty much any upgrade that the game has to offer. Some games have invested time and effort to carefully design the reward opportunities into the game but in other cases the reward for watching videos is in-game currency in small amounts that don’t allow the player to buy anything of value. Offering incentives with little value will discourage users from watching ads.

How to fix it:

First, you should measure opt-in on a cohort basis to see if your users lose interest in the rewards over time. If you detect such an issue you can consult this post for ideas how to improve the incentives – Top 7 Incentives for Video Ads in Mobile Games

Not managing the ad networks

Ad networks are an important part of your monetization but their interests are not always aligned with yours. Typically, for each $1 you are making, the ad-networks are also making a $1. This means that there is a lot of opportunity for the publishers who can closely monitor and negotiate the prices up. Without doing this, your ad monetization not be optimal and might decline over time.

How to fix it:

First, you need a way to track ad revenue per impression sequence so you can manage the waterfall. Additionally, you may also want to get visibility into the campaigns the ad-networks are bringing you. If you don’t have those, you can still negotiate with the ad-networks but it will be harder. To successfully negotiate – remember that your leverage will  be the share in the impressions they are getting as well as their position in the waterfall. In return, you will want to get guarantees for eCPM and fill rates.

Focusing your UA only on payers

If you ask most marketing teams what users are they trying to get for the app they might answer “payers” or “high value players” but in both cases they mean payers actually. What this means is that the monetization manager is trying to monetize with ads users who were supposed to be payers. This strategy is far from ideal. If you are not giving the UA team a task to bring ad whales don’t be surprised that your users don’t do well with ads.

How to fix it:

Step 1 is to figure out who the ad whales are. Once you do that, you can start sending postbacks for ad whales so ad networks can send you more of these high value players. At the same time, you can have the marketing team profile the ad whales and learn more about them so they look for the right media to reach this audience. Finally, targeting lookalikes of the ad whales on FB ads manager has brought great results for some of our customers.

Not doing direct deals

Do you know how many ad-tech middle-mans are between you and the advertiser placing ads in your app? You don’t. It could be 1 or 2 or 5 or 10. One way to know for sure is having no middle-mans. Not doing direct deals means you are leaving money on the table but what’s more important is that you don’t even know how much revenue you are missing out on which means you have very little leverage when you negotiate with ad-networks.

How to fix it:

You should start by getting visibility into who is advertising in your app. This will give you a priority list of who you should be approaching. Getting to the right contact in these companies is not very hard but if you get stuck, shoot me an email to yaniv [at] soom [dot] la and I will try to connect you. Also knowing the eCPM you are getting from each advertiser through the ad-networks could prove useful.

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Analytics, Announcement, Industry News

New Loot Box Regulations – What it Means for Mobile Games

New Loot Box Regulations - What It Means for Mobile Games

About 3 weeks ago on December 21st, Apple added a small term to it’s T&C for app publishers.

Apps offering “loot boxes” or other mechanisms that provide randomized virtual items for purchase must disclose the odds of receiving each type of item to customers prior to purchase.

Most of you probably missed it during the holiday season as did I but a few major publications caught the change and reported it quickly. Here is the report in PocketGamer and in The Verge. This change might be a response to an increasing interest of some of the regulators in the loot boxes concept. It started with Starwars Battlefront 2 criticism on reddit when a response by EA became the most downvoted comment on reddit ever. The “loot box” monetization strategy along side the game appeal to kids caused a few regulators to take notice. Most notabely, the State of Hawaii announced they will work on legistlation to ban loot boxes and Belgian officials said they would like loot boxes banned as it’s a form of gambling.

The top 25 grossing games were not effected

One might think that the change made by Apple will make a big impact on game publishers given how popular loot boxes are. In reality, however, none of the top 25 grossing games made any changes. The reason, is that none of them are selling loot boxes as in-app purchases. While loot boxes are popular, they are commonly sold for virtual currency which can be bought for cash. If you look at the top 25 grossing apps, the items that are listed in the app store for purchase are packs of: quartzes, diamonds, crystals, gold, coins, gems. The only game we could find that will have to make a change is Hearthstone.

What about in-game loot boxes

Apple didn’t entirely ban loot boxes that can be purchased inside the game with in-game currency. The regulators however might decide to address this type of loot box as well. There are many game elements that are randomized, therefore the question arises of where the distinction will take place. It’s one thing to ban something that is purchasable, but if any randomized game element is banned, pandora’s box will open. My guess is that regulators will focus on purchasable items and games that are made for kids.

CASE STUDY ON ADVERTISERS CHURN & eCPM

If gambling with VC is illegal than what about social casino

The comment by Belgian officials was that loot boxes are a form of gambling and therefore should be banned. What is a bit odd is that there are more obvious forms of gambling that are not banned yet – the social casino apps. Either these officials have a double standard when it comes to mobile games or they haven’t opened the top grossing charts recently.

The clear winner is rewarded video

Aside from kids and their parents who might get to keep more money in their wallets, the companies who provide monetization via ads and rewarded videos will probably benefit from these regulations if they happen. Ad based monetization models are becoming more popular in the mobile game industry and the fact that Apple is adding limitations on in-app purchases pushes more game developers in towards ads.

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Analytics, App Monetization, Game Design, Tips and Advice

4 Proven Tips for Improving Opt-In Rate – Based on Data

4 Proven Tips for Improving Opt-In Rate - Based on Data

If you have been following the SOOMLA blog, attending mobile game conferences or keeping up with the latest mobile monetization trends in some other ways you should already know the following important fact. Improving Opt-in to rewarded videos usually results in an increase of the same proportion in your total ad revenue. This is why many companies that use rewarded videos have been focusing on the opt-in parameter and have been trying to optimize it.

While getting the basic opt-in ratio is easy, there are a few advanced methods for finding hidden opportunities around opt-in rates.

1 – Look at daily opt-in vs. monthly opt-in

Typically, app companies focus on the monthly opt-in – this is the ratio that is normally available by platforms such as Ironsource mediation and what most will allow you to analyze if you send them the impression events. The monthly opt-in, however, only tells part of the story and in many cases we have seen that the daily opt-in can be significantly lower. What that means is that there are users who opt-in to the videos some of the days while not watching videos on other days. Fixing this can usually yield 20-25% more in ad revenue and the way to do it is by taking a close look at your incentives. Will the users need the incentive on a daily basis? If not, try to figure out an incentive that the users will need more regularly.

Definitions
Monthly opt-in – the number of unique users who watched at least one video in a given month out of your total MAU.
Daily opt-in – the number of unique users who watched at least one video in a given day out of your total DAU. The daily opt-in has to be averaged across multiple days to smooth out the fluctuations.

2 – Analyze opt-in for cohorts

Cohort analysis is hardly a new trick for marketers but when it comes to monetization managers it actually is. Comparing the opt-in rate for new users vs. existing users can lead to some pretty interesting insights based on our experience. This might requires some help from your BI team (or simply using SOOMLA’s dashboard) but the hidden opportunity should justify the effort as we have seen up to 2x differences between the two segments. If opt-in is high for new users and declining for long-term users it could be a sign that your incentives are not meaningful enough for your users. In other words users are willing to watch videos but they soon realize that what they are getting in return doesn’t get them very far so they stop. In other situations, the opt-in for new users is low. This could indicate an awareness and training problem. Making your users aware of the option to watch videos early on can fix the problem.

FREE REPORT – VIDEO ADS RETENTION IMPACT

3 – Differentiate users from different traffic sources

One of the interesting patterns we have seen is that users from different traffic sources behave differently when it comes to opt-in ratio. Users who came from paid channels and specifically from video ads often present a higher opt-in ratio compared to organic users. To improve the opt-in ratio for organic users, consider adding some more guidance to highlight the opportunity of watching videos for in-game rewards.

4 – Treat your ad whales to nice Incentives

In recent research we showed that the top 20% of the users contribute 80% of the ad revenue. These so called Ad Whales are the most important segment from an ad revenue perspective. You should focus a lot of your attention to make sure the opt-in rate for this group is as high as it can be. These users typically contribute more than $0.99 and sometimes up to $100. This means that they are as good as payers and you can offer them in-game items that are normally reserved for an actual purchase. However, since you want users of this group to watch a video daily it’s better not to offer them a perpetual item. Some examples of incentives you can give for ad whales:

  • A tank that is normally worth $100 – watch a video to use for a single day
  • Shortening a waiting time that normally costs up to $1
  • 10x coin boost for a short period instead of 2x

Identifying the ad whales is possible by attributing the ad revenue accurately to the user level. The only way to do this accurately today is with SOOMLA Traceback.

We’ve put out a series of posts on the wide topic of Opt-In rates and the importance of them. Feel free to check them out:

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