Marketing

Analytics, Marketing

Kongregate's recent blog post suggests that you can double your traffic by tracing your ad revenue

I recently came across a fantastic post by Jeff Gurian. Those of you who don’t know Jeff, he is the Director of Marketing at Kongregate. In his post he brings up a super important point – you can double your traffic by Tracing the Ad LTV or “counting the ads” in the language of the article.

Doubling your traffic only takes a 25% increase in LTV

According to Kongregate’s experience with user acquisition, Jeff explains, the correlation between how much traffic you can get and the bids you place is not linear but rather a power function. “There is always a tipping point where your traffic will increase exponentially relative to the increase in your bid.” says Jeff.

The chart in the post does a good job in explaining this point:

chart illustrating the power curve of the impression volume you can get at different bid levels

Image from original article at Kongregate developer blog

In this example – acquiring traffic with bids of $12.5 as opposed to $10 will allow you to get twice the amount of traffic. In other words, a bid increase of 25% transatles to a volume increase of 100%.

Tracing Ad LTV allows more room in your CPI bids

Not all games have ads but the ones that have added in-game advertising are seeing between 10% to 80% of their revenue coming from ads. 25% is a typical scenario in many games and is also close to the ratio reported by public companies such as Glu and Zynga. The example given in the article (see image below) is showing that tracing Ad LTV can modify your ARPU / LTV analysis by 25%-30%. As we know, higher LTV means that we can afford to pay higher CPI which leads to twice as much traffic per the explanation above.

Illustration of LTV and ARPU calculations with and without tracing-back the ad revenue

Image from original article at Kongregate developer blog

Let SOOMLA do the work and get you the accurate Ad LTV

Many companies skip the Ad LTV since the process for calculating it is often complicated, time consuming and in many cases it is not accurate enough. Their claim is that none of this matters if you are miscounting your Ad LTV. Counting impressions can lead to significant errors in LTV calculations which means your ROI analysis can be off and end up losing money for the company.

Fortunately enough, SOOMLA has developed a solution that automates the Ad LTV calculation and we do that with much greater accuracy so now you can enjoy the benefits of Traceback and double your traffic without worrying about accuracy or extra development effort.

To save valuable resources and ensure you are getting the Ad LTV correct for every cohort you need a specialized system like SOOMLA TRACEBACK. The platform traces the ad revenue and sends it to your attribution partner or in-house BI.

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Analytics, Marketing

BiggestMistake_in_Ad_LTV_Calculations

Recently I became aware of game publishers that implemented an in-house solution for Ad LTV tracing but were doing a huge mistake in how they think about ad revenue. We all know that any LTV calculation has 2 main factors:

  • Retention
  • Revenue
The Ad revenue is the factor that companies get wrong when they build in-house solutions for Ad LTV tracing. These solutions often assume that each impression pays the same level of CPM. This is a huge mistake that can lead to errors in orders of magnitude and ROI calculations that are way off.

If this is how your company calculates Ad LTV you should read the following examples carefully.

Example 1 – The Rewards Collector

  • User played during the first month and never came back after.
  • Watched 50 rewarded video ad impressions from Vungle – didn’t click or install any ads.
  • Average eCPM for this month from Vungle $15
Ad LTV Based on Impressions The True Ad LTV Error
$0.75 $0 $0.75

This type of error could lead the UA teams to a false positive ROI calculations. The UA team thinks the ad spend on this user is ROI positive while it’s actually a losing buy.

Example 2 – The Ad Whale

  • User played 5 days during 2 weeks
  • Watched 10 interstitial ads from AppNext, clicked on 2 and installed a Match-3 game and a Strategy game
  • Average eCPM reported by AppNext for those days – $5
  • CPI for that Match-3 game – $2, CPI for the Strategy game – $5
Ad LTV Based on Impressions The True Ad LTV Error
$0.05 $2 $1.95

Here the ROI calculation could be false negative. The UA team will stop buying these type of users since ther reported Ad LTV is $0.05 while it’s actually $1.95 and the buy was actually a good one.

Example 3 – The Retargeted User

  • User played 10 days during 1 month
  • Watched 20 video ads through Inneractive
  • Average CPM reported by Inneractive for those days – $5
  • This user was a whale in Game of War and was part of a retargeting campaign so specific CPM bids for that user were high – $80 x 4 ads, $90 x 2 ads, $100x 8 ads, $110 x 2 ads, $120 x 4 ads
Ad LTV Based on Impressions The True Ad LTV Error
$0.10 $2 $1.9
The ROI calculation in this example is also likely to be false negative. The UA team might think this was a bad user to bring to the game although his Ad LTV alone was $2.

 

If your company needs to calculate Ad LTV you should try to avoid these costly mistakes. Check out SOOMLA Traceback – Ad LTV as a Service.

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Analytics, Marketing

What-Are-Ad-Whales

Targeting lookalikes of your best users has been the easiest and most effective way spend mobile ad budgets since Facebook first introduced the feature in 2013. Google and Twitter are now also offering similar features and advertisers use them with similar levels of excitement.

What happens if your app is monetizing with ads and not IAP?

Apps that monetize mostly with advertising have a much more complicated job when trying to acquire new users. With ads it’s really hard to figure out who are the best users of your app:

  • The users who had the most amount of sessions?
  • The users who watched the most amount of ads?
  • Users who performed social actions?
  • Some other in-app event?

Ideally you would want to create a group of the users who generated the most amount of revenue from advertising in your app and get more users like that.

What are Ad Whales and how to find them?

2% of your users install other apps after viewing ads in your app, these users contribute more than 90% of your ad revenue and can be referred to as “Ad Whales”. This group of users highly resembles the users who make purchases in your app. They are a small group that contribute most of the revenue.

Understanding who your ad whales are could be very useful if you want to spend your advertising budget smartly. You could learn more about the demographics and interests of these users and find more users who share similar characteristics. Better yet – you can let the lookalikes algorithm do this job for you and simply sit back and see your user acquisition campaigns target only users who are similar to the Ad Whales you found.

Tracing your ad revenue is critical for discovering Ad Whales

Unlike In-App Purchases, ad revenue events are not generated inside your app. Finding the Ad Whales is almost impossible unless you have an ad traceback system in place. Traceback is a technology that allows you to trace ad revenue back to the user level. Once you have such a system in place, it’s easy to see who are the users that contribute the most amount of ad revenue.

 

SOOMLA TRACEBACK is a platform for tracing ad revenue. It allows you to get granular data about each and every user and identify the users who contribute the most ad revenue.

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Marketing

ltv calculation for Apple subscription models

Apple recently announced a new model is available in the app store and apps will no longer be sold for a $1 but will charge a monthly subscription instead. The subscription model is a middle ground between the premium model and the free2play model with in-app purchases. It doesn’t force users to pay upfront for apps they are not sure they are going to need long-term and on the other hand it lets users evenly share in the monetization instead of relaying on psychological models that exploit people’s weaknesses. This model still requires LTV calculation but it works a bit differently than with free2play (Or IAP models for non-games).

What’s the difference in LTV calculation

With the subscription model the amount paid every month is fixed.  This makes things simpler for us when it comes to LTV calculation. It also works in monthly intervals where churn requires an action. In other words, once you started it’s an opt-out model vs. opt-in in free2play / IAP. This means that we can look at churn as a static ratio. The formula is this one:

Screenshot 2016-07-03 16.25.44

Using an LTV calculator is easy

The calculator below can give you a feeling of how the LTV is affected by these two parameters. You can put in the numbers and get the result.

  • Monthly subscription fee – here you can put whatever value you configured in the app store
  • Monthly churn rate – this is the number of people canceling each month as a ratio of the number of people who started that month

 

UA aspects of subscription models

The main use cases of LTV calculations are in marketing and user acquisition. One thing to know is that in subscription models the retention is much longer compared to free2play. It’s common to see monthly subscriptions with a lifespan of 3-5 years whereas in free2play we usually calculate LTV for 180days or 365days. This also means that you would be able to run campaigns that only get to ROI after 6 months or even 12 months if you have the right funding resources.

 

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Marketing, Tips and Advice

cross promoting between your apps requires creativity - here are 9 ideas how to do cross promo

Most companies in the mobile app ecosystem today have more than one app. Once your company reached this stage, you should start considering cross promoting your new app in the existing apps. You should probably read The Complete Guide to Cross Promotion ROI in addition to checking out these 8 awesome ways to cross promote your apps.

Interstitials and App Trailers

This method is the most obvious way and has been in use for as long as people were making apps. You have one app, you launched a new one. Simply make an app trailer or at least a full page banner ad and add them in the existing app. Most mediation platforms supports this practice and it’s easy enough to do. Keep in mind however that you are taking away from your potential ad-revenue with this method.

Virtual goods / coins bonus

This method is for games only. Virtual goods and currencies are an integral part in most mobile games today. Once your new game is ready you can offer the virtual goods or coins of the new game as a bonus to the users of the existing game. This way the cross promotion message makes the users feel special and has more chances of attracting the users.

In-app notifications

While google and apple are not allowing using push messages for cross promotion, in-app notifications are still allowed. The in-app messages a simple yet effective tool that pops up a “system notification” style message to the user which immediatly grabs his attention.

More games button

This is a classic but still very effective, simply plant a button in your lobby/home screen and allow your users to check what other games you developed for them. Users normally assume that if they liked one of your games they are likely to like another.

Email messages that cross promote a new app

If your iOS app asks users to login or use a social network to connect, you should be able to leverage this method. Android apps can ask a permission to access the user email in the operating system or revert to social login. Once you have a long list of user emails you can leverage them to announce the coming of a new app. If you do this, make sure you include a way to unsubscribe in order to comply with Can Spam Act

Retargeting on Facebook and Google

This method costs some money but could still be effective if your new app monetizes well. Both Facebook and Google allows to target a list of customers and promoting a new app to them. You will need to either integrate an SDK in your existing app in advance for this or if you are a using an attribution provider you can probably ask them for a list of identifiers you can upload for this purpose.

crossy road cross promo method is to introduce avatars from other games

Adding the new game avatars in the existing game

Another method that can only used by games. It was first used by Hipster Whale in their game Crossy Road very effectively except it was used to promote other games. The users get acquinted with the new characters and are interested to explore the new game.

Bonus levels featuring the new app

This method is inspired by the playable ads that are getting a lot of momentum lately. The playable ads allow a user to play a few moves in the existing game before deciding to try the advertised game. Since your company is the developer of both games, you can actually build a better experience and incorporate a short bonus level in the existing game to get the users interested in the new one.

Name hints as a cross-promo tool

This is a generalization of sequales. Obviously, adding the number “2” to the title is an effective way to get users of an existing app interested in the new app. However, sequales requires the apps to be very similar and is a method only for games. Creating a name that hints to the other app creates a softer association that allows the new app to inherit the trust that the users generated towards the existing apps. Some examples:

  • Candy Crush Saga – King created at least 5 more games with a name ending in “Saga”
  • Clash Royale – Supercell hinted that their new game is related to their top game – Clash of Clans
  • Du Apps Studio – Android utility apps maker is dominating the top free charts while all their apps start with “DU”

 

If you would like to measure the tradeoff between cross promotions and ad revenue you should probably start attributing your advertising revenue. Check out SOOMLA Traceback – Ad LTV as a Service.

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Marketing

LTV calculation for mobile apps that use Google Analytics.

Google Analytics is a popular choice among app developers. However, Getting LTV using GA is harder than one might think. I created this slideshare to explain how to find the required retention rates and the DAU data in the Google Analytics Dashboard. The slides also show how to use an online calculator tool for the lifetime value calculation.

When calculating your LTV, make sure you are including your ad revenue in the mix. If you need a tool to accurately report ad revenue and ad LTV in different segments, cohorts and traffic sources you should check out SOOMLA Traceback.

Learn More

Calculating LTV with Google Analytics Caption

1. MOBILE APP LTV Calculation Using Google Analytics
2. About Me MD @ Kontera (Blog/Text Monetization) Co-founder / CEO @ SOOMLA (Ad LTV as a Service) Co-founder / VP Sales @ Eyeview (Video Ads & Analytics)
3. About SOOMLA Traceback Flexible integration with your BI and Attribution via S2S APIs Leverages listener SDKs that require zero client side code Unique technology that extracts ad revenue per user from inside the ad-networks
4. LTV Calculation Steps Find your ARPDAU Your key retention rates – where to collect them Use online LTV calculator to get the result
5. FINDING YOUR ARPDAU
6. Aggregated Daily Revenue Monthly Revenue Revenue from Google Play $2,000 Revenue from Apple $4,000 Ad Revenue $3,000 Total $9,000 Daily Revenue – the monthly revenue divided by number of days. In this case it’s $300
7. DAU in GA – Step 1 Go to the “Active Users” view and select a date range of 1 week and “1 Day Active Users”
8. DAU in GA – Step 2 Either collect the data points one by one by copying and pasting or simply download the CSV
9. Averaging DAU DAU Sunday 562 Monday 907 Tuesday 1,071 Wednesday 1,244 Thursday 1,019 Friday 940 Saturday 2,278 Average 1,146 Tip – the DAU usually fluctuates during the week so it’s important to use average of at least one week
10. ARPDAU Calculation ARPDAU – the daily revenue divided by the average daily active users (DAU) Calculation Average Daily Revenue $300 Average DAU 1,146 Total $0.26
11. KEY RETENTION RATES
12. Retention in GA – Step 1 Select “Cohort Analysis” and set the date filter to “Last 30 days” and the resolution to “by day”
13. Retention in GA – Step 2 Collect the points from the top row – you need day-1, day-7, day-14 and day-30
14. Key Retention Rates Retention Rate Day1 7.37% Day7 3.53% Day14 3.10% Day30 2.70% Note – we took a slightly lower number for Day30. Flurry had only Day28 Retention
15. USING THE CALCULATOR http://blog.soom.la/2016/04/clv-calculation-modeling-lifetime.html
16. Feeding Retention Data In Use the key retention rates from step 2 in the top part of the LTV calculators
17. Feeding ARPDAU Data In Use the ARPDAU we calculated in step 1 in the bottom part of the calculator
18. The Result The result is shown in the bottom part of the calculator
19. Thank you!

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Marketing

Calculating LTV for mobile apps with flurry analytics and an online calculator.

In the early years of the mobile app ecosystem, Flurry analytics was the only way to go. The free platform offered basic KPIs, charts and trends that will satisfy mobile app developers measurement needs. Flurry remained a popular choice among smaller app developers even when more competitors entered the space. While the platform gives many KPIs, calcualting LTV is still pretty complex for app publishers who relay on Flurry. The presentation below provides an easy solution and walks the reader through collecting information from flurry and inserting it into an online LTV calculator.

 

When calculating your LTV, make sure you are including your ad revenue in the mix. If you need a tool to accurately report ad revenue and ad LTV in different segments, cohorts and traffic sources you should check out SOOMLA Traceback.

Learn More

Calculating LTV with Flurry Analytics Caption

1. MOBILE APP LTV Calculation Using Flurry Analytics
2. About Me MD @ Kontera (Blog/Text Monetization) Co-founder / CEO @ SOOMLA (Ad LTV as a Service) Co-founder / VP Sales @ Eyeview (Video Ads & Analytics)
3. About SOOMLA Traceback Flexible integration with your BI and Attribution via S2S APIs Leverages listener SDKs that require zero client side code Unique technology that extracts ad revenue per user from inside the ad-networks
4. LTV Calculation Steps Find your ARPDAU Your key retention rates – where to collect them Use online LTV calculator to get the result
5. FINDING YOUR ARPDAU
6. Aggregated Daily Revenue Monthly Revenue Revenue from Google Play $6,000 Revenue from Apple $2,000 Ad Revenue $40,000 Total $12,000 Daily Revenue – the monthly revenue divided by number of days. In this case it’s $400
7. DAU in Flurry Analytics – Step 1 Go to the “Active Users” view and select “Last Week”
8. DAU in Flurry Analytics – Step 2 Either collect the data points one by one by copying and pasting or simply download the CSV
9. Averaging DAU DAU Sunday 2,450 Monday 2,305 Tuesday 2,773 Wednesday 3,054 Thursday 2,957 Friday 2,597 Saturday 2,278 Average 2,631 Tip – the DAU usually fluctuates during the week so it’s important to use average of at least one week
10. ARPDAU Calculation ARPDAU – the daily revenue divided by the average daily active users (DAU) Calculation Average Daily Revenue $400 Average DAU 2,613 Total $0.2
11. KEY RETENTION RATES
12. Retention in Flurry Analytics – Step 1 Select “Return Rate” and set the date filter to “Last Month” and the resolution to “days”
13. Retention in Flurry Analytics – Step 2 Collect the points from the bottom row – this is the average retention rate for that day
14. Key Retention Rates Retention Rate Day1 37.90% Day7 8.70% Day14 4.10% Day30 1.10% Note – we took a slightly lower number for Day30. Flurry had only Day28 Retention
15. USING THE CALCULATOR http://blog.soom.la/2016/04/clv-calculation-modeling-lifetime.html
16. Feeding Retention Data In Use the key retention rates from step 2 in the top part of the LTV calculators
17. Feeding ARPDAU Data In Use the ARPDAU we calculated in step 1 in the bottom part of the calculator
18. The Result The result is shown in the bottom part of the calculator
19. Thank you!

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App Monetization, Marketing

Apps with ad revenue try to track the LTV but get it wrong
If you have been following the mobile app ecosystem closely you know that there are more and more apps that are relaying on advertising revenues to create profitable businesses. Zynga reported significant growth in ad revenue in their recent financial statements and leading speakers in the industry have talked about the trend towards ad based monetization in industry events.

This situation leads to new challenges in ROI measurement. Apps need their LTV to exceed the CPI and LTV calculation require you to know the ARPDAU at some point. Lets see how ARPDAU is calculated in different monetization methods.

Measuring ARPDAU for Apps that use IAP

If your app only uses IAP for monetization, your life is quite easy. To get the ARPDAU, simply divide the daily revenue by the number of DAU. both parameters can be obtained from your analytics platform or in-house BI. Figuring the ARPDAU in a specific segment, cohort or traffic source is easy since the data is available for each user so all you have to do is repeat the exercise for the group of users that are in the segment.

Ad Revenue per user is not available for ad-supported apps

The life of ad-supported apps is more complex. Here are some of the challenges:

  • Using multiple ad-networks means that the revenue information needs to be collected from multiple dashboards
  • The data you can get from the ad-networks is aggregated to the country/day level – no data per user is available
  • Understanding who are the users who click on the ads is very hard and getting install data is almost impossible
  • The 90% of the users who don’t click don’t generate any revenue on CPI or CPC campaigns (most of the campaigns today)

Using an average leads to errors

Mobile app companeis have been using an average to calculate the ad revenue per user, LTV, and ROI. This method is pretty simple – they take the revenue generated in a specific country in a given day and divide by the amount of users that day to receive the ad revenue per user. This of course, assumes that all users are contributing the same amount of revenue which is very far from the truth. In reality, only about 2% of the users in a given app actually go and click on the ads and install the advertised apps. This means using an average is wrong 98% of the time.

Counting impressions per user is also not accurate

More advanced app publishers have implemneted ways to count the number of impressions served per user and per segment and have been using that to claculate LTV. This method is also wrong. In most ad-networks the revenue is driven by the CPI and CPC campaigns and therefore the impressions are not a good indication of revenue. For example, a user with 100 impressions could have generated $0 while a user with 2 impressions who also clicked and installed could have generated $2.

 

If you want to get your ad revenue right 100% of the time you can now. Check out SOOMLA Traceback – Ad LTV as a Service.

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Marketing, Tips and Advice

Who should calculate ltv header image with different kpi highlighted, cvr, ctr, cpm

Many companies in the mobile app ecosystem today try to figure out the LTV of a customer also known as CLTV or CLV. In fact, some of them probably shouldn’t. In the past companies simply focused on the value generated in a single session and count page views and conversions and for many situations this method is the right one to chose.

The choice between these 2 distinct methods often impact the choice of analytics platforms, attribution methods and many other things. If you are developing a new application, you should figure out which method suits your application.

Services should calcualte LTV

The industries that have traditionally focused on revenue per user calculations and user life time value were the service companies: phone carriers, utility companies providing electricity and cable companies. The models were later adopted by SAAS companies and these days, freemium games started the trend of “Games as a Service” and are using the same models as well. At the heart of these models there is an assumption that the customer will stay for a long time. This usually happens when the cutomer relies on the service for a basic need or when the service becomes part of the user daily routine and habits.

Companies who sell products should use atransactional measurement model

On the flip side, eCommerce sites are relaying more on transactional models where each purchase is measured separately even if performed by the same user. When games started, the Premium model was popular and games were considered products so the transactional measurement model was the standard. Users go to the app-store and buy an app for one or two dollars and that’s it. There is no retention measurement, no LTV analysis, ARPDAU, nothing like that. Transactional models are much simpler.

Habit creation is the deciding factor

The main question you should ask yourself when you are coming to chose between a transactional measurement philosophy or the LTV one is whether or not you are creating a usage habit. We are all used to turning the lights on as we walk into our house. We all flip on the TV as soon as we sit on the couch and many of us open the candy crush app when they wait for the subway. These are habits, they indicate the service became a part of the user life and there is a real chance he will stay for a long time.

Apps that monetize over time but don’t have a habbit

Let’s think about the example of Kayak. It’s an app that helps you compare travel deals: flights, hotles and car rentals. The customer might install the app and use it once but the next time he will use the app is only when he is booking another vacation. By that time, it’s very possible that he will have another phone. There is no habit being created that is attached to a daily or weekly routine. Every purchase is a stand alone transaction and should be thought of it that way. The value of acquiring a user through a CPI campaign is pretty low. And the LTV vs. CAC formula doesn’t really apply here. From this reason, companies like Kayak focus most of their marketing efforts on users that are booking travel in the near future. They might try to promote their app or entice user to bookmark their site but this should be considered more as a way to put an ad banner for free in front of the user’s eyes. There are other models that can be applied to assess the business merits of banner advertising.

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Industry Forecasts, Marketing

what's the average spend per app on cost per install campaignsAbout 2 weeks ago a friend asked me how much do app companies spend on getting app installs – what’s the average cost per install campaign size. He was pretty happy with the answer I gave him and the research I referred him to so I thought I would share the answer.

Concentration in cost per install campaignscost per install campaigns account for growing part of the total mobile ad spend and forecasted to reach $6.8B in 2019

First, you have to realize that the average spend is really irrelevant here due to strong concentration in the market. According to eMarketer there were $4.6 billions spent on App Install campaigns in the US during 2015. You can assume that the numbers are 2x if you look at the global market so $9.2B. The reality is that the top companies are doing a big chunk of that. You can look at the public companies to get a sense of that:

  • Zynga spent $169M on Marketing in 2015
  • King spent $344M in 2015
  • Glu’s marketing spend was $45M in 2014

The rumor is that Machine Zone and Supercell are spending close to $500M each on cost per install campaigns. The real answer is that the distribution is estimated as:

  • Top 3 companies: $1.2B ($400M each)
  • #4 – #10: $1.2B ($150M each)
  • #11-#50: $4B ($100M each)
  • #51-#100: $2B ($50M each)
  • #101-#200: $0.75B ($15M each)

The average spend per app is around $4,600

If you still want to figure out the average spend you would need to divide the total campaign spend by the number of apps. Surely enough Apple and Google can provide this data. The latest numbers that were released were 1.5M apps for Apple and 1.4M on Google Play –  http://techcrunch.com/2015/06/08/itunes-app-store-passes-1-5m-apps-100b-downloads-30b-paid-to-developers/. Obviously there is a lot of overlap so we can assume 2M apps in total and an average spend of $4,600 per app.

 

 

 

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