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 The most serious ones are that you can’t test unpublished apps and you’ll never find exactly what worked due to the lack of on-page analytics.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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Announcement

The waterfall screen shows the ecpm decay for your mobile app

We recently added a new screen to TRACEBACK dashboard called “Waterfall”. You can access it once you are in the analytics mode viewing one of your apps. This post will go through the basics of this screen and how to use it. Here is what we will cover:

  • The goal of the screen
  • How to set up a view that is productive
  • Comparison Table section
  • eCPM decay chart
  • Ad Network Comparison section

Goal of the screen

This screen is the first time publishers get a chance to look at their eCPM decay. It expands the horizons of what data publishers can access and opens up a world of possibilities. It is an important screen since ad-networks’ yield should be compared Apples to Apples. Publishers often develop a sentiment towards one ad-network since they see higher eCPM coming from that network. In reality this could simply be a result of waterfall position and not related to the ad-network. This screen fixes that!

There are many other implications. Publishers can realize that they need to change their ad-network mix, they might change the way they integrate ads into the game as a result and they will also have more productive discussions with their ad-network partners.

Setting up the view

This screen is highly flexible which means users can easily set up views that are not productive for them. Our recommendation is to set up as follows:

  • Single country at a time
  • Single ad-type at a time
  • Select a date range that brings the total #1 impressions to at least 100,000 impressions
  • Focus on 2-3 ad-networks that generate volume and remove
  • Ignore eCPM rates when impression volume is lower than 10,000

These guidelines will allow you to make the most out of this screen.

Comparison Table section

The first section of the screen is a table with full details about what is hapening in every impression number. Publishers can look at each ad-network and see the number of impressions, the total revenue generated by that impression number and the eCPM all broken down by the impression number.

img_3557

eCPM Decay chart

The middle section gives the avaraged eCPM that the publisher receives for every impression number. The darker bars are the Actual eCPM – the avarage across all selected ad-networks for the segment filtered by the user. The optimal eCPM represented by the brighter bars is the maximal eCPM given by one of the ad-networks. Note that today the Optimal eCPM bars ignores the fill rate of the ad-networks and is therefore better than the achievable eCPM.

In the example below you can see quite a big difference between the average and optimal eCPM. This is likely due to a low fill rate by AdMob.

NOTE: It is wise to ignore eCPM rates when the impression volumes are lower than 10,000. Here this happens on the 5th impression for the Optimal eCPM and on the 9th impression for the average eCPM.

img_3558

Ad Network Comparison section

The last section includes a chart and a table and it visualizes very easily what ad-networks got to serve what impression. The stacked bar chart represents impression volume and each color is a different ad network. The table below the chart shows the eCPM paid by each ad-network for each impression count.

In the example below, you can see that Chartboost and Admob split the first impression 50%-50% but Admob part is declining quickly and almost disappears by the 5th impression. The table below the chart shows that AdMob eCPM is much higher compared to Chartboost for this segment – which bags the question – why is AdMob not serving more ads. This could be a result of low fill rate by AdMob or bad choices made by the mediation.

img_3559

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App Monetization

deltadna_publishers_are_unsure

A few days ago DeltaDNA released a survey about in-game advertising. The company collected responses from mobile game publishers about 11 questions and compiled a 25 page report showing the results and suggesting analysis for some of them. One of the most interesting findings is related to the confidence of game publishers in their in-game ads strategy.

In-game advertising is confusing many publishers

The report asks game makers to estimate how certain they are that they are taking the optimum appraoch about their in-game monetization. The results presented in the table below shows that most publishers are actually uncertain of their in-game ad strategy.

Chart visualizing the answer to the question - how certain are you that you are taking the optimum approach towards in-game advertising.

Image Credit: DeltaDNA

Publishers targeting casual players are less confident

The report later goes on and finds which publishers are more certain and which ones are less certain. It compares between different target audiences and finds that publishers who target casual players tends to be less confident about their in-game ads when compared to publishers who target mid-core and hard-core players. One way to explain this is that companies who target mid-core and hard-core players tend to have more resources and access to data.

Small publishers are unsure of in-game ads strategy

The report analyzes the confidence levels in small publishers vs. bigger publishers and finds that it is the smaller ones who are less confident. Small publishers usually don’t have access to premium advertising measurement tools such as SOOMLA Traceback.

 

If you want to gain more confidence and start being data driven about your in-game advertising. Check out SOOMLA Traceback – Ad LTV as a Service.

Learn More

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Fun Stuff

top 15 mobile advertising jokes and memes header image featuring conspiracy keanu

During Casual Connect Tel-Aviv that took place last week I had a funny conversation with some colleagues about ad mediation. I explained a game developer that the ad-mediation don’t know in advance how much an ad-network will pay for the impression since the payout is determined only later based on the performance. His reaction looked very much like Conspiracy Keanu Meme Character and inspired this post.

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App Monetization, Resource

top_10_resources_for_understanding_customer_lifetime_value

Customer Lifetime Value is one of the most important metrics to track for every service oriented business including free mobile apps, websites, free to play games, SAAS, music subscriptions, phone subscriptions and many others. There  is still, however, a lot of uncertainty around LTV calculations, how to use it and where to find the data to feed the formules. This post is trying to sort through the life time value maze.

Customer Lifetime Value Resource List:

1) Who Should Track LTV and Who Shouldn’t

Published: May 25, 2016

There are many resources showing how to calculate ltv but it’s really not for everyone. Some apps and websites should use other models.

 

img_33462) 6 Customer Lifetime Value Calculators

Published: Apr 26, 2016

6 calculators showing how to calculate customer lifetime value. Full explanation and links to additional resources such as spreadsheets and excel files. This is a great resource for anyone who is a beginner in LTV. Bookmark this link for future use.

3) Calculating LTV for Your First UA Campaign

Published: Jun 21, 2016

How do you calculate Life Time Value (LTV)? There are a number of formulas circulating but how do you know which is the right version for you? Here’s some tips and pointers that are specifically targeted for publishers are measuring LTV for UA campaigns.

4) 7 Analytics Platforms with Built In LTV Reporting

Published: Jun 30, 2016

Some analytics platforms offer LTV reports and also prediction of the lifetime value. This research brings together 7 platforms to consider and provides details about their LTV models and the pros and cons of each one.

5) Calculating LTV with Google Analytics

Published: Jun 8, 2016

Google Analytics for mobile apps doesn’t show LTV. These slides explains how to retrieve retention numbers and find the DAU in the Google Analytics screens. The slides show how to feed the data into an online calculator to get the LTV prediction.

5 surprising facts about customer lifetime value of mobile games6) 5 Surprising Facts about Customer Lifetime Value in Mobile Games

Published: Aug 7, 2016

LTV – life time value is a key metric many mobile game publishers follow. Here are 5 things that even some of the experts didn’t know.

 

7) Calculating LTV for a Mobile Game – Methosd for Different Stages

Published: May 9, 2016

Calculating LTV for a game in design phases is different compared to the soft launch phase which is again different from the lauch phase. This post describes the calcualtion in different phases and suggests additional resources such as CLV calculators.

8) Flurry Analytics – Calculating LTV (Slides)

Published: Jun 6, 2016

App developers who use Flurry analytics have hard time getting their LTV – user lifetime value. This presentation shows how to do it easily with a free online calculator. The first section is showing how to retrieve retention and DAU data from flurry dashboard and the second part explains how to use the calculator to ge the results.

 

9) Apple Subscription Model – LTV Formula

Published: Jul 5, 2016

Calculating LTV for apps that use the apple subscription model. This post includes a formula as well as a calculator.

 

10) Easy to use Customer Lifetime Value Spreadsheet 

Published: Sep 11, 2016

Our best LTV model brings together simplicity and accuracy. Input only 6 parameters and get d60, d90, d180 and d365 LTV results.

 

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Fun Stuff

Top 10 Donald Trump Games header image - the man himself over a mountain background

In a previous post we discussed the shift towards IP based games and mentioned that there are some opportunities for smaller studios to leverage some free IP if they stay alert and move quickly. Politics is one area that provides free IP and especially this is true in an election year. Donald Trump, however, brought this to new levels. Never have a candidate generate so many scandals and gained such a big portion of the media time. For game studios, Trump is the gift that keeps giving. At least until the elections. We collected the 10 most popular Donald Trump games from the app stores and are presenting them here.

From a commercial perspective, we don’t have full data on these Donald Trump games but at least the first three should be doing well based on the number of ratings they received. My bet is that the Trump vs. Hillary slots game is the best one in terms of monetization. Slots normally do well in terms of monetization but this one specifically was made by Super Lucky based on their existing Obama Slots game which indicates previous success with this IP recipe.

Donald Trump games – counting 10

Icon Name Publisher Game Links User Ratings
Punch the Trump game Punch The Trump Brutal Studios Android     iOS    Google Play: 135,672
Turmp vs. Hillary Slots game Trump vs. Hillary Slots Super Lucky Android     iOS    Google Play: 27,189
Another Donald Trump game Trump Dump Day Dream Android     iOS     Google Play: 14,505
Campaign Clicker featuring Donald Trump and Hillary Clinton Campaign Clicker Spring Loaded Android     iOS     Google Play: 9,839
Runner game where Donald is the running character Trump on the Run Squad Social iOS    iTunes: 1,980
Candidate Crunch by Game alliance feating the 2 candidates: Donald and Hillary Candidate Crunch Game Alliance Android     iOS      Google Play: 1,959
This Donald Trump game focuses on his days as a Tycoon - it's a variation of a clicker game where you endlessly collect money Trump Tycoon FME Fun Free Games Android     iOS     Google Play: 1,068
Trump wall game icon Trump's Wall The BLU Market Android     iOS     Google Play: 943
Icon of the game Gravity Trump Gravity Trump Hoodclips Android     iOS     Google Play: 546
Trumpy wall is using pixel art like Crossy Road and other copycats of that game Trumpy Wall Harambe Corp Andrdoid Google Play: 257

If you are interested in additional top 10 game lists from SOOMLA you should check out:

 

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Analytics, App Monetization

Header image showing how complex it is to a/b test your ad based app monetization

A/B testing has been an integral part of marketer toolbox for a good reason – it takes a great deal of the guess work away from marketing. In online and mobile companies it also became a popular tool for product managers. Every time a new version is released, why not a/b test against the existing version and make sure nothing got broken. In mobile app monetization, however, this tool is not available.

Why ad based app monetization is so hard to A/B test

The core requirement for A/B testing is to be able split your users into two groups, give each group a different experience and measure the performance of each one so you can compare it later. There are a number of tools who can facilitate the split for you including Google Staged Rollout. If you are measuring IAP monetization it’s easy enough to associate purchases to the users who made them and then sum the revenue in Group A and Group B. In ad monetization however, it’s impossible to associate ad revenue to individual users. The ad partners mostly don’t report the revenue in this level of granularity.

Method 1 – interval testing

One alternative that companies have been using is interval testing. In this method, the app publisher will have one version of the app already published and will roll out a version with the new feature to all the devices. To make sure all the users received the new version publishers will normally use force update method that gives the user no choice. The impact of the new feature will be measured by comparing the results over two different time intervals. For example, Week1 might have contained version 1 and week 2 might contain version 2 so a publisher can compare version 1 vs. version 2 by comparing the results in different date ranges.

Pros

  • Very simple to implement – no engineering effort

Cons

  • Highly inaacurate and subject to seasonality
  • Force update method has a negative impact on retention

Method 2 – using placements or different app keys

This is a pretty clever workaround for the problem. Most ad providers has a concept of placements. In some cases, they are called zones or areas but all 3 have the same use – they are planned so you can identify different areas in your app where ads are shown for reporting and optimization purposes. The way to use this for A/B testing is to create a zone A and Zone B and then report Zone B for users that received the new feature while reporting Zone A for the control group. If you are already using the zones feature for it’s original purpose, you might already have zone 1, 2, 3, 4 and 5 so you would create 1a, 1b, 2a, 2b, ….

Of course, if you are using multiple ad-networks you would need to repeat this set up for every ad-network and after the test period aggregate the results back to conclude your A/B test.

A variation of this method is to create a new app in your ad-network configuration screen. This means you will have 2 app keys and can implement one app key in group A and the other app key in group B.

Pros

  • More accurate compared to other methods

Cons

  • The effort for implementing a single test is very high and requires engineering effort
  • Will be hard to foster a culture of testing and being data driven

Method 3 – counting Impressions

This method requires some engineering effort to set up – every time an impression is served the publisher reports an event to his own servers. In addition, the publishers sets up a daily routine that queries the reporting API of each ad-network and extracts the eCPM per country. This information is than merged in the publisher database so that for every user the impression count for every ad-network is multiplied by the daily average eCPM of that ad-network in that country. The result is the (highly inaccurate estimation of the) ad revenue of that user in that day. Once you have this system in place, you can implement A/B tests, split the users to testing groups and than get the average revenue per user in each group.

Pros

  • After the initial set up there is no engineering effort per test

Cons

  • Settting this system up is complex and requires a big engineering effort
  • Highly inaacurate – it uses average eCPM while eCPM variance is very high
  • Can lead to wrong decisions

Method 4 – leveraging true eCPM

This method leverages multiple data sources to triangulate the eCPM of every single impression. It requires significant engineering effort or a 3rd party tool like SOOMLA TRACEBACK. Once the integration of the data to the company database is completed, publishers can implement a/b tests and can get the results directly to their own BI or view them through the dashboard of the 3rd party tool. Implementing A/B tests becomes easy and a testing and optimization culture can be established.

Pros

  • The most accurate method
  • Low effort for testing allows for establishing a testing culture
  • Improvement in revenue can be in millions of dollars

Cons

  • The 3rd party tool can be expensive but there is usually very quick ROI

 

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Industry News, Marketing

Apple search ads - what's the master plan - header image

About a week ago, Apple launched it’s Search Ads platform into existence and there are many app publishers who are trying different tactics to use this medium to their advantage. You can read about some of these here:

Once the dust settles, you might want to ask yourslef what is Apple’s master plan for this? Why is Apple doing it? Here are my thoughts on this subject.

Search Ads aren’t enough for Apple

Apple’s revenue is well over $200B. When they go after a new revenue stream they ask themselves – is this channel going to be meaningful for us. If the channel has the potential of being at least 5% of their total revenue or in other words $10B/year they might consider it. If it’s lower, it might not be worth the efforts and risks. For example, the app-store revenue for Apple were $6B in 2015 but it’s growing fast and are likely to reach $15B in a few years. App-store search ads can’t deliver these revenue volumes on their own. There is simply not enough supply.

Apple is looking at the mobile ad spend forcasts

Some of you might have seen this report by eMarketer forcasting $195B in mobile ad spending by 2019.

Emarketer's forecast showing global ad spend on mobile will reach $195B by 2019

[Image credit: www.emarketer.com]

Everything we have seen until now tells us that this forecast is going to come true. Apple is 75% percent ahead of Google when it comes to App Store revenues but it knows that it’s far behind when it comes to advertising. The Apple ad-network – iAd was shut down as of June 30th 2016 after failing to become a major monetization channel for apps but Apple didn’t give up hope. Their mistake was that they didn’t understand the importance of consistent demand. They were fucosing on bringing big brand campaigns which is important in order to drive eCPMs up and stay competative but without consistent demand, publishers remove your SDK in favor of other SDKs. Apple learned from this mistake.

When Google launched their search ads product Apple were watching and they realized that Google did a sloppy job this time. Unlike Adwords, the Google Play search ads are not available as a market to the public but are instead offered as a managed service through Google account managers. Apple saw this opportunity to create a more appealing open product that allows anyone to set up their own campaigns. They created high demand for it’s search ads and more importantly this time the demand will be consistent.

Apple’s next move is for the supply side

The revenue potential of search ads alone is limited as we mentioned before. The demand is huge but the supply is the problem. Following the moves of other tech giants can give you a hint as to what Apple’s next move might be:

  • Google generated consistent demand with Adwords and then launched Adsense to improve supply
  • Facebook generated consistent demand with Feed Ads and then launched Facebook Audience Network (FAN) to enhance supply.

It’s likely that Apple will try to do the same once they aggregate enough demand for their ad products. They have tons of data about their users so they can offer the same levels of demographic targeting offered by facebook in addition to leveraging search data to indicate interests of users. Since they are the platform owner, developers will trust their SDK and give them a shot again. This time the demand will be consistent and allow developers keep them as part of the monetization mix. It might take a year or even 2 years but eventually this has to be Apple’s plan.

 

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