Analytics, App Monetization, Tips and Advice

Monthly vs. Daily Opt-in for Rewarded Video

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

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

What is daily and monthly opt-in for rewarded video

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

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

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

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

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

Why should you care about this

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

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

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

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

Create a habbit with the right incentives, segmentation and popups

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

Method 1 – Incentives and daily bonuses needs to work together

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

Method 2 – Simple pop-up for a segmented group

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

Method 3 – Selling Insurance

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

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

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Analytics, App Monetization, Game Design

Inside SOOMLA: Advertiser Breakdown

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

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

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

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

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

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

Case 1 – Advertiser Blocking Compliance

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

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

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

CASE STUDY ON OPT-IN RATES & SOOMLA INSIGHTS

Case 2 – Comparing Ad Networks

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

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

Case 3 – Doing Direct Deals

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

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

Conclusion

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

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

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Analytics, App Monetization, Marketing

Inside SOOMLA: Ad Waterfall Analysis

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

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

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

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

What is an Ad Waterfall?

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

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

Q1 2018 MONETIZATION BENCHMARKS

Waterfall Analysis Screen

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

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

Feature 1 – Ad Networks per Impression

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

Feature 2 – eCPM Decay Chart

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

Inside SOOMLA's Ad Waterfall - eCPM Decay

Feature 3 – Ad Network Comparison

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

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

Conclusion

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

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

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Analytics, App Monetization, Game Design

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

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

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

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

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

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

CASE STUDY ON REWARDED VIDEO REVENUE & SOOMLA INSIGHTS

Here are some related articles that can help:

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

Japan eCPM Benchmarks Series – Top Advertisers Comparison

Japan eCPM Benchmarks Series - Top Advertisers Comparison

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

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

Why 1st Impressions

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

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

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

Interstitials – 1st Impression Lovers / Non-Lovers

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

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

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

Top Advertisers for Interstitials – iOS

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

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

SOOMLA's Japan Breakdown - Interstitial Top Advertisers Android

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

Q1 2018 MONETIZATION BENCHMARKS

Rewarded Videos – 1st Impression Lovers / Non-Lovers

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

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

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

Top Advertisers for Rewarded Videos – iOS

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

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

SOOMLA's Japan Breakdown - Rewarded Videos Top Advertisers Android

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

Conclusion

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

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

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Analytics, App Monetization, Game Design

Japan eCPM Benchmarks Series – Ad Network Performance

Japan eCPM Benchmarks Series - Ad Network Performance

In the first part of our Japan eCPM Benchmark series, we kept a fairly broad approach to getting an understanding of how the Japanese mobile gaming market is performing. Before diving in to deeper breakdowns, it was important to look at the overall differences between iOS and Android.

There were some differences, but the most significant was how Rewarded Videos and Interstitials performed at near polar opposites. For Android, Rewarded Videos were far outperforming Interstitials in terms of eCPM payouts for 1st and overall impressions. On the other hand, we saw iOS dominating Interstitials with significantly higher eCPMs. Yes this is important, but at such a high level of analysis, it’s hard to gain actionable insights. This leads us to part two!

For the second part of our Japan eCPM Benchmarks series, we’re going to take a deeper look into the how the various ad networks are performing in Japan. Because we saw such a significant difference between iOS and Android in the ad types (Rewarded Videos and Interstitials), it only makes sense to keep the breakdown going in the same direction. It’s important to keep in the back of your mind that the majority of the mobile operating system market share in Japan is held by iOS, contrary to the rest of the world where Android maintains the larger share of mobile users. There are several reason for this, as one Tech blogger from Japan mentioned – if it interests you.

The Data

The data used for this series is based upon the data used in our recent Q1 Monetization Benchmarks Report collected through the SOOMLA platform. We analyzed the activity of over 30 million users in 8 countries over the span of 3 months (October 2017 – December 2017). Together these users viewed 600M impressions showing 2,500 advertisers in close to 100 apps. The app sample consists a higher ratio of games compared to the ratio of non-games in the app stores. However, we’ve seen the same patterns regardless of app category. The ad-formats analyzed through the study are: Interstitials, video interstitials and rewarded videos.

Interstitials – Premium Paid for First Impressions

This section looks at the premium paid in eCPM rates for 1st impressions compared to the overall average for ad networks prevalent in Japan’s interstitial domain. We compared this premium across all ad-networks who serve a high volume of interstitials. We’ve indexed the average eCPM as 100% and then presented the 1st in comparison.

SOOMLA's Japan Breakdown - Interstitial iOS - 1st Impression Lift*Only ad networks with over 1,000,000 total impressions during the data period were considered.

Japan eCPM Benchmark Series - Interstitials Android 1st Impression Lift*Only ad networks with over 100,000 total impressions during the data period were considered.

First and foremost, it’s important to note the vast difference in minimum impressions for Android and iOS. The majority of interstitial ad impressions recorded are from iOS, confirming the majority of Japan’s iOS adoptance. Furthermore, after a deeper look, the data sample has a slight bias due to a large portion of the impression counts originating from a few highly successful mobile apps. Regardless of this, we can still see that iOS does maintain significantly higher payouts for 1st impressions than the average eCPMs.

Q1 2018 MONETIZATION BENCHMARKS

Interstitials – Share of Voice

Share of voice refers to the percentage of impressions each ad network displays of the total. We broke this down into 1st impressions and total impressions for ad networks displaying interstitials in Japan.

Japan eCPM Benchmark Series - Interstitials Share of Voice
See original Android – Share of VoiceSee original iOS – Share of Voice

For iOS – we can see that AdMob take a large share of both 1st impressions and total impressions. Mopub for instance has a strategy more focused on 1st impressions compared to their total impressions. For Android – taking into consideration the previous comments, we see that AdMob maintains the lion’s share.

Rewarded Videos – Premium Paid for First Impressions

This section looks at the premium paid in eCPM rates for 1st impressions compared to the overall average for ad networks prevalent in Japan’s rewarded videos domain. We compared this premium across all ad-networks who serve a high volume of rewarded videos. We’ve indexed the average eCPM as 100% and then presented the 1st in comparison.

Japan eCPM Benchmark Series - RewardedVideos iOS 1st Impression Lift*Only ad networks with over 300,000 total impressions during the data period were considered.

Japan eCPM Benchmark Series - RewardedVideos Android 1st Impression Lift*Only ad networks with over 300,000 total impressions during the data period were considered.

For iOS – we see, as expected, the majority of the ad networks have a higher first impression eCPMs compared to the total, however AdColony is the only ad network which the first impression eCPM is lower than the average. For Android – we see TapJoy with a significantly higher first impression eCPM ratio compared to the other ad networks.

Rewarded Videos – Share of Voice

Share of voice refers to the percentage of impressions each ad network displays of the total. We broke this down into 1st impressions and total impressions for ad networks displaying rewarded videos in Japan.

Japan eCPM Benchmark Series - Rewarded Videos Share of Voice
See original Android – Share of VoiceSee original iOS – Share of Voice

Across both iOS and Android, we see that Ironsource servers large portions of the 1st and total impressions that are served, only to be surpassed by Applovin in Android. It seems like Ironsource’s dominance as a mediation for rewarded videos allows it to obtain a high number of impressions without paying a premium for it. For Applovin, it’s possible that their self-serve interface for advertiser is able to generate higher demand diversity which translates into better results in later impressions.

Conclusion

This concludes part two of the Japan eCPM Benchmarks Series where we took a deeper look into the performance of ad networks for interstitials and rewarded videos. In the next part of the series, we will be looking into specific advertisers : which love being first (impression), which don’t, which have high volumes and which have high eCPMs. See you then!

In case you missed part one, you can find it here.

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Analytics, App Monetization, Game Design

Japan eCPM Benchmarks Series – iOS vs Android Breakdown

Japan eCPM Benchmarks Series

We’ve received a lot of great feedback based on our recent data report, so we’ve decided to conduct further drill-downs on a country basis.

Japan is well known for its expansive gaming market that has been growing rapidly over the past few years, and according to a recent study by AppAnnie, mobile gaming revenue increased by 35% in 2017 year over year.

For the first part of our Japan eCPM Benchmarks Series, we will look breakdown on how iOS and Android are performing.

The Data

The data used for this series is based upon the data used in our recent Q1 Monetization Benchmarks Report collected through the SOOMLA platform. We analyzed the activity of over 30 million users in 8 countries over the span of 3 months (October 2017 – December 2017). Together these users viewed 600M impressions showing 2,500 advertisers in close to 100 apps. The app sample consists a higher ratio of games compared to the ratio of non-games in the app stores. However, we’ve seen the same patterns regardless of app category. The ad-formats analyzed through the study are: Interstitials, video interstitials and rewarded videos.

Q1 2018 MONETIZATION BENCHMARKS

Overall Android vs iOS

In this section we’ll keep it fairly broad and as we progress, we’ll get more in depth. For now, we will look at the high level eCPM benchmarks for Japan – how Android is performing in comparison to iOS. Similar to the main report, the aim is to show the vast differences between the eCPMs being paid out for the first impressions.

SOOMLA's Japan Breakdown - by OS

To no surprise, we do see a similar trend in Japan as we do for overall Android and iOS. iOS does tend to overall have higher payouts for eCPMs, while both maintain first impression eCPMs that are up to 1.43x higher than the average impression eCPM.

Ad Type Breakdown

The next drill down will be looking at the overall performance (in terms of eCPM payouts) of ad types in Japan. For the purpose of this section, we’ll be looking at Rewarded Videos and Interstitials (includes video ads and playable ads).

SOOMLA's Japan Breakdown - Android

SOOMLA's Japan Breakdown - iOS

Generally speaking, the comparison between Interstitials and Rewarded Videos is nearly identical at this level of breakdown, however as we can see above there is a significant difference between Android and iOS. While it’s difficult to say exactly what the reason behind this is, it’s worthwhile to understand the unique features of the Japanese mobile gaming market which can provide some insights.

Interstitials iOS have significantly higher eCPMs payouts as well as a ratio of 1st to average impression eCPM.

This is the first part in the series, so the breakdown is kept to be very high level. In the next part, we will be looking into the performance of the individual ad networks. Stay tuned!

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Analytics, Announcement, App Monetization, Resource, Tech Resources

Industry’s First Monetization Benchmark

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

We are excited to announce our industry first “Q1 2018: Monetization Benchmarks” report today. This is one of the many industry data reports that we will continue to publish providing important insights related to monetization through ad revenue. This report gives an in-depth comparison of eCPMs for 1st impressions and overall and providing a ranking of monetization providers in the mobile industry.

You are welcome to download the report through this link.

Here are the quick take-aways from the report:

  • Advertisers and monetization providers are clearly paying a premium for first impressions. The premium can be as high as 100% of the average eCPM, sometimes higher
  • Monetization providers and advertisers have different bidding strategies when it comes to first impressions. Some are more aggressive while others seem indifferent to the impression sequence
  • Games tend to have a bigger focus on getting the 1st impression in comparison to non-gaming advertisers who appear to be indifferent to whether or not they are shown 1st.
  • There are a few advertisers who repeatedly show up in the top 10 across different ad formats and platforms. They are able to do that by having a clear data advantage. When negotiating prices for 1st impression – make sure you have enough data.

 

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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.

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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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