Industry Forecasts, Marketing

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If you have been marketing your app long enough you must have noticed a CPI increase. Getting users to install your app used to cost a lot less than it costs today. This change can be noticed globally and across different platforms.

The reason behind CPI increase

One of the drivers of the CPI increase we are seeing is the brand budgets starting to pour into mobile. when the internet just emerged, users adopted it first and a few years later bigger budgets started to follow. Facebook story also shows a lot of resemblece – the social network first had 1 billion users and 3 years later it was making $25B in advertising. Mobile advertising also follows the trend of the money following the eyeballs. Recent report from eMarketer projects mobile advertising to reach $195B by 2019 with most of the increase coming from brand budgets.

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Ad spend per user is growing

Here is another way to think about it – if you devide the projected ad spend growth by the projected user growth you can see that the average ad spend per user has been increasing but will continue increasing even more.

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So apps who want to get users face 2 options:

  • Try to relay on organic discovery
  • Increase their LTV in order to afford higher CPIs

Relaying on organic discovery however has proven more and more difficult due to the app stores being overly crowded. Apps today have to invest in marketing to gain momentum. So that leaves us with only one option – increasing LTV.

Adapting to change in CPI prices

In order to increase LTVs app companies must adapt to the change quickly and make the brand budgets work to their advantage. In other words, your company needs to make sure some of this new money finds it’s way over to you. The most effective way to increase LTVs is to introduce a view-to-play model in your app and targeting the 98% of the users that don’t pay. This puts your app in a position to enjoy the projected increase in ad-spend per user and not suffer from it. From a unit economics perspective, monetizing a larger portion of your user base allows you to increase ARPDAU and LTV. Combined with an adequate ad revenue measurement tool, you would be able to increase CPI bids with confidence, remain compatitive in the market and keep growing your app.

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App Monetization, Video

Blog header image - what's inside the advertising black box - video snaps from casual connect panel
Last Casual Connect in Tel-Aviv introduced many interesting lectures and panels. However, this is the one when ad-networks secrets got revealed. These are the top 9 moments of the panel presented in an easy video navigation tool.

 

Panel Participants

Lior Shiff – Co-founder and ex-CEO, Product Madness

Guy Tomer – Co-founder and CMO, TabTale

Niko Vouri – Co-founder and COO, Rocket Games

Yaniv Nizan – Co-founder and CEO, SOOMLA

Noam Neuman – VP Mobile Strategy at Matomy

Fernando Pernica – Mobile Monetization at Ad-Colony

Minute 5:29 – The Secret Guage

Lior asks Fernando whether there is a way for ad-networks to dynamically manipulate rev-share rates for publishers and create periods where they are more competative. Can you gues the answer?

Minute 12:06 – What Surprised Yaniv

Lior asks Yaniv what surprised him the most when lifting the hood of the black box. Not all app users are made equal apparently.

Minute 14:30 – When Ad Networks get Naughty

Guy tells the story about an ad-network that didn’t play by the rules and showed inappropriate ads to kids user audience.

Minute 32:11 – Brands – Friend or Foe

When a big change comes along you can either get defensive or find the opportunities that change creates. While the entrance of brands to mobile ads makes buying users harder it creates new monetization opportunities that translates back into the ability to place more competitive CPI bids.

Minute 33:09 – Is there an Unbiased Mediation?

Why is the ownership of mediaiton by ad-networks a problem? Bias and lack of transparency come into play here.

Minute 35:54 – Ad Networks’ Transparency

Guy explains that regardless of their various attempts to get more data from the ad-networks they still couldn’t get granular data and even aggregated data is sometimes tough.

Minute 37:07 – Lack of Transparency is a Double Edged Sword

Fernando explains how mediation is a black box for the ad-networks and how the lack of transparency goes both ways.

Minute 39:53 – Are There Ad Whales?

Lior is asking Yaniv and Guy whether or not Ad Whales exist. Guy explains that he can’t track it today but Yaniv is answering with precision: “We have seen $124 generated by a single user”.

Minute 45:43 – How Would You Leverage Ad LTV Data

Yaniv is asking Niko what would he do differently if he had the power to know. Niko explains how granular ad revenue data can impact their user acquisition decisions.

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App Monetization

How can one single user generates $74 in ad revenue - here are some answers

I received the following questions yesterday and wanted to share the answers so more users can learn.

Being an indie game developer I’m trying to understand all the low level stuff that happens around user acquisition but I often lack experience to close the gap. After reading the latest blog post
(http://blog.soom.la/2017/01/ad-viewer-of-the-month-generates-74-at-134-ecpm-for-publisher.html,
I have a few simple questions that I hope you can answer. Regarding the user that generated $74 in ad revenue at $134 eCPM.

The reason of such a high eCPM

Does the advertiser know the spending potential of the user (whale) and his playing patterns and such wants to acquire it in a game where spending can be in tens of thousands of dollars?

First, let’s start with some basic terminology so that we will be aligned:

ADVERTISERS – App publishers who want to spend money and get users.
PUBLISHERS – App publishers who want to get money and are willing to put ads inside their games

Now, let’s talk about the reasons for high eCPM. In most games, the advertising transaction model is based on performance – CPC or CPI. Advertisers are willing to pay high CPI and CPC when they believe that users will likely to do two things:

  • be loyal/engaged/retained
  • spend money in their apps

Most likely in this case, the ad-network was able to convince some advertisers that a segment of users that includes this one is worthy of these high CPC or CPI but this by itself is not enough.

In order for high CPC or CPI to translate into high eCPM and revenue for the publisher, users needs to take actions – they need to engage with the ads, click on them and most likely also go and install the apps that were advertised to them.

In CPC model – eCPM is CPC bid x CTR
In CPI model – eCPM is CPI bid x CVR x CTR

There is an interesting report from Comscore that identifies a group of users they call “Heavy app downloaders” – you can read more about these users in this link – http://blog.soom.la/2016/12/reward-abusers-and-heavy-app-downloaders.html. It’s highly posible that the user who generated $74 in a single month was a “Heavy App Downloader”.

Was it a game where spending can be in tens of thousands of dollars?

Yes, as a general rule all big mobile game advertisers are apps where you can spend thousands of dollars.

Was this a result of retargeting?

Was the advertiser was trying to re-target the player which has churned? from the game, but did invest a big amount of money before churning?

It is possible that this user have been part of one or more retargeting campaigns. Obviously companies who have already experienced good results with this user would try to get him back. However, I doubt that this is enough. It takes more than one advertiser to generate that sort of revenue for the publisher and retargeting campaigns are only a small piece of the advertising ecosystem.

Are the advertisers wasting money on this user?

Regarding the 555 impressions delivered, if all these impressions are advertising a single game, I guess at some point the advertiser will stop Targeting ads to that player because it is a simple waste of money (he can not acquire the player). Is that correct?

Actually not correct.
Most ad-networks charge the advertiser based on a performance model CPI and CPC. This means that if the publisher earned $74 the specific user should have not only watched the ads but also clicked and installed some of the apps that were advertised to him. Most likely the publisher will want to keep getting this money so he will continue to serve ads to that users. The ad-network will keep targeting more ad campaigns to him due to the high performance and revenue he is generating for them.

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App Monetization

Ad viewer of the month header image featuring the user who made the most amoutn of ad revenue in the month of November

To demonstrate the pure awesomeness of TRACEBACK technology and it’s superiority over the alternatives we decided to start doing an “Ad Viewer of the Month” celebration. The idea is simple – we scan through all the users tracked through SOOMLA TRACEBACK and find the users who made his publisher the most amount of ad revenue. Unlike with In-App Purchases, these users are not taking this money out of their pockets but rather generate revenue for the app publishers by watching large amounts of ads and engaging with them.

November Ad Viewer of the Month

The user who did the most amount of ad revenue in November did no less than $74.76 for his app publisher. He used the app every single day in November – totaling 30 active days. More importantly, his eCPM and ARPDAU number are way off the charts and much higher than the averages for that app. Here is his score card

Attribue Ad Viewer of November
Country  United States
Device iPad
Ad Type Interstitial
Impresions 555
Active days 30
Revenue $74.76
eCPM $134.70
ARPDAU $2.49

Infographic showing details about the ad viewer for the month of november, this single user generated $74 for his publisher at $134 eCPM and $2.49 ARPDAU

NOTE ABOUT SHARING – Feel free to share this infographic and embed it in your blog. If you do this, we will appreciate a link to http://soom.la.

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Analytics, App Monetization

Ad revenue concentration hero image with chart and text

We are happy to report some interesting data points we recently looked at. The goal was to understand how concentrated ad revenue really is. Everybody knows already that the 80/20 law applies in IAP – at least 80% of the revenue is driven by the top 20% of purchasers. There is plenty of research showing how concentrated IAP revenue is. Ad revenue, however, is still a mystery for most publishers and very few companies actually have the data on how concentrated the revenue is. If you take the naive approach and believe all the users contribute revenue based on the average eCPM you might think that the ad revenue concentration chart will be flat. The reality however, is very different.

Comparing Concentration in Ad Revenue vs IAP Revenue

In the image below you can see a comparison of the revenue concentration between ad based monetization and IAP based monetization. These charts are based on data from 28 days of activity in a Match-3 game where most of the monetization comes from interstitials. The revenue model behind the ad monetization is CPC in this case.

On the left side, the IAP revenue is highly concentrated and 80% of the revenue is generated by the top 20% of the users. The top user generated more than $300 in revenues for the app.

On the right side, we see that the ad revenue is also highly concentrated. The top 20% are contributing more than 50% of the revenue here and the top user generates $2.5 while there are users who only contribute a few cents.

Comparison between concentration of ad revenue and IAP revenue

Ad Revenue Concentration with Reward Ads

One of the hot trends of 2015 and 2016 was the adoption of rewarded video ads by many game publishers. We wanted to look at the ad revenue concentration in rewarded video as well. The chart below does exactly that.

The data here is from a single day so obviously more concentrated than information aggregated over an entire month. The game here is an mid-core action game and the monetization is done with both rewarded videos and an offer wall.

The ad revenue concentration is much higher in this data set. The top 20% of the users are contributing 90% of the revenue and the top user is contributing more than $15.

Ad revenue concentration in rewarded video ads and offer walls

Who are the users contributing high amounts of ad revenue

Once you realize that the ad revenue is concentrated almost as much as iAP revenue, your next question is likely to be: “how are these users”. On a high level, these users are typically the users who download many apps as indicated by a Comscore Report highlighted in this article. But you can go a lot further than that. Using SOOMLA Traceback you can profile these “Ad Whales and target them in marketing activities.

 

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Analytics, App Monetization

Reward Abusers written in blue text next to a trophy and Heavy App Downloaders written in green text next to an app icon with two axes

As the market adopts TRACEBACK technology we are learning new things about how users interact with ads. This allows us to classify users into types. Let’s think about these two types of users who are highly relevant to rewarded video based monetization.

Reward Abusers – these are users who watch the videos to get the rewards but are not contributing any revenue in Neither IAP nor in Ad revenue.

Heavy App Downloaders – these are users who download and try multiple apps each month. Typically, these are the users who end up generating the most amount of video ad revenue for your app.

How these segments impact your business

Let’s say you are buying traffic from a new source. You probably ask yourself, how many installs I received but you should also ask the million dollar question – “what type of users am I getting?”

Why?

Consider 2 possible sources:
Incent Campaign – this campaign gives users an incentive in another app in return for downloading your app. By nature these users are after the rewards so this source might be heavy in Reward Abusers
FB Campaign – now consider a campaign targeting lookalikes of users who are existing Heavy App Downloaders. This campaign is likely to bring more Heavy App Downloaders. You can learn more about this specific technique – here

How can you segment your users

If you are already convinced that knowing the Reward Abusers from the Heavy App Downloaders can impact your business your next question should be how to spot them. Let’s think about what features are similar and which ones are different between thm.
App Engagement – both user types have high app engagement
Video Ad Engagement – Reward Abusers will watch as much videos as Heavy App Downloaders
Post Impression Performance – this is the feature that sets them apart – Reward Abusers will only watch the videos while Heavy App Downloaders will also click and install the apps presented to them

Reward Abusers Heavy App Downloaders
App Engagement High High
Video Ad Engagement High High
Post Impression Performance Low High

So understanding what the user does after he watches the video ad is the key here. Today, there are two solutions in the market:
Developing In-house – this requires your engineering team to figure out specific ways to track post impression events with each ad-network and to keep updating the code every time there is an update to the ad-network SDKs
SOOMLA TRACEBACK – our platform does all the work for you, it requires a simple integration but once implemented you will be able to segment your users reliably, track ROAS and do many other mind blowing optimizations to your ad-revenue. CLICK TO LEARN MORE

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Marketing

Hero image showing ad whales tiled in a pattern to cover an areaEarlier this year we introduced the concept of Ad Whales. These are the users who are generating the most amount of revenue from advertising inside your app. In other words, if you had the full data about the ad revenue of each user you could put all the users in a spreadsheet and “sort by” the column that has the ad revenue in it. Your top 1% are the Ad Whales.

ad whales are users with high ad revenue - you can find them by sorting your user ids by ad revenue column

Ad whales are the users with the most amount of ad revenue

This time of year we are pleased to tell you about some new ways to use the TRACEBACK platform driven by the integration with Facebook Ads Manager that was announced recently.

Creating audiences based on eventsscreenshot from facebook audience creation process showing how you can create an audience of ad whales that keeps fresh all the time

The Facebook platform has a very powerful feature called audiences. There are multiple ways to create audiences but one of the best ones is to leverage existing events to do this. Once you have followed the steps to send data from TRACEBACK to FB Ads Manager, these events will already wait for you in Ads Manager. For now FB have us send ad revenue event as “Add to Wishlist” events so from the audiences dashboard create a custom audience for those events and then lookalike audience to expand it. TRACEBACK will continue to send ad revenue data to FB which means that both the custom audience and any lookalike audience will always stay fresh for you.

Studying the Ad Whales demographics and interests

Once you have set up an audience like described above, you can also use the audience insights tool provided by FB. You can start by looking at the demographics of the users in that audience. You will be surprised what you find out.

Screenshot from facebook audience insight tool showing the demographics of the ad whales

Once the audience gets big enough, you will also be able to get other insights like interests and activities. This should give you new ideas for ad sets in Facebook but you shouldn’t stop there. Having a good grip about who are your best users can completely change how you do marketing.

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Video

china_-_casual_connect02

In this video from the recent Casual Connect in Tel-Aviv, Omer Kaplan from Iron Source is giving some highly valuable lessons about the China market. If you are part of the mobile gaming industry, you must have spent a considerable amount of time thinking about this market. It’s already the biggest and it’s also the fastest growing. There are still lots of opportunities there and lots of money coming in and out of it.

China In vs. China Out

One of the interesting distinctions made in the presentation is that there are two ways to work with the China Market:

  • China In – trying to bring western products and apps into China
  • China Out – helping successful Chinese companies gain distribution in other companies (mostly referred to as Overseas by Chinese).

The path that Iron Source took is to start with the China Out and only then try to go China In. The presentation also includes some useful tips about what’s required in order to be interesting for Chinese companies in the China Out model. One of the interesting observations is that the companies who are looking to spread out of China are the ones that already dominated that market. It means that they are massive companies with hundreds of millions of users and they are looking for opportunities that move the needle. If you don’t have that level of scale (hundreds of millions of users) you will probably not be able to be a good partner for these giants.

For the full lecture – see the video below.

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Analytics, App Monetization

Podcast - The role of analytics and data in ad based monetizationThis is a recent interview I gave at Cranberry Radio. I’m talking about the following topics:

  • The role of unbiased measurement companies on the advertiser side and the publisher side
  • Insights that SOOMLA have seen by measuring ad-revenue to an unprecedented granularity level
  • Insights from our latest study – Rewarded Video Ads Retention Impact in Match 3 Games

You can listen to the podcast here.

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Analytics, Marketing

4 top mobile a/b testing tools header image

This is a guest post by Natalia Yakavenka from SplitMetrics

Ask any mobile marketer what is the best way to optimize conversion rates for your app page and you’ll most likely get A/B testing as a response. While A/B testing is still most often associated with the web, the concept of a/b testing for mobile app pages is not new. The very first solutions growth hackers used were custom coded landings, but such approach requires time and effort. However, app page conversion optimization only became popular when self-service platforms like SplitMetrics and Storemaven emerged. These platforms brought a completely new level of A/B testing for mobile pages as they provided insights on top of showing the winning variation. Later on, the introduction of Google Play Experiments in 2015, brought A/B testing of app landing pages into the “must have” category for app marketers. Since that time, plenty of new solutions have emerged but we recommend sticking to the 4 most popular tools presented here.

Google play store allows experiments - a limited way to do mobile a/b testsGoogle Play Experiments

When it comes to selecting the best A/B testing tool, the most common question is why go elsewhere if you have the free Google Play Experiments. Indeed, it allows mobile publishers to run free experiments on their app pages, but it comes with significant limitations. The most serious ones are that you can’t test unpublished apps and you’ll never find exactly what worked due to the lack of on-page analytics. Still, Google Play is the perfect solution for those who are not familiar with paid traffic and user acquisition as it doesn’t require driving traffic to the experiment from ad resources. The other three tools require sending traffic to their experiments and are usually for more advanced marketers.        

Distinctive features: absolutely free + requires no additional traffic

Split metrics logo - this mobile a/b testing tool offers many advanced ASO featuresSplitMetrics    

Founded in 2014, SplitMetrics was among the first ones to provide every marketer with an easy-to-use, unlimited, and flexible A/B testing tool. In addition to the regular icon/screenshot testing, it offers pre-launch experiments for unpublished apps and Search, Category and App Store Search Ads testing. Unlike the Google Play service, it offers a multi-armed bandit approach which helps reach significant results fast. But it’s not as ideal as it seems to be — you have to pay for it. Though the price is very reasonable and you have a 30-day trial, you will need to pay a monthly fee for your subscription.

Distinctive feature:  pre-launch experiments and App Store Search Ads testing

StoreMaven is one of the pioneers of the mobile a/b testing and ASO spaceStoremaven

StoreMaven provides easy-to-use A/B testing for the entire app store landing page experience. One of their advantages is offering benchmarks based and best practices based on their broad client base in each of the app store categories. On top of that, StoreMaven clients benefit from their money saving algorithm, StoreIQ. This algorithm helps conclude tests with fewer samples and lower costs by leveraging historical data to quickly determine the winning creatives. StoreMaven provides a fully dedicated Account Manager to make sure clients make the most of their testing budgets. This tool is also a paid tool that is offered as a monthly subscription.

Distinctive feature: Professional Services

One of the features offered by the tune platform is A/B testing to improve your ASOTUNE’s A/B Testing

4) Tune offers many services for app marketers. They are mostly known for their attribution service — measuring paid app installs. However, they also offer A/B testing and optimization tool for the app landing page. Launched in spring 2016, it already provides solid functionality, offering the basic functions of testing different types of assets and showing all measurements and stats collection. While Tune offering is more complete compared to the other testing tools, it’s biggest limitation is that it doesn’t work with other attribution providers. The tool is also limited with regards non-US regions and only supports a small list of regions. In terms of pricing, Tune’s A/B testing tool is not available as a stand alone and so customers have to buy it as part of a suite of services.

Distinctive feature: works very well with other Tune capabilities

A/B testing can be easy with the right tools and is recommended for any app marketer as part of a data-centric growth strategy. Feel free to also try our quiz — test yourself to see how data-driven your game is.

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