Analytics

Analytics

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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Analytics, App Monetization

there are many things even the experts don't know about life time value - here are 5 of them. this image is a header image for the blogpost

You might have heard some industry experts talk about LTV (life time value) and how important it is. Here are 5 things even some of the experts don’t know about LTV.

1 – Life time value (LTV) is not just for marketing campaigns

You might have heard that you need to know your life time value to do marketing. This is correct but there are actually more reasons. The first reason for for calculating LTV is related to the early design phase. Before you even start making the game you should analyze the potential LTV based on benchmarks from similar games. This important for fundraising as well as for choosing the right games to build. The second reason is even more important. LTV is the one KPI that wraps both ARPDAU and retention and it is highly correlated with long term success. By actively tracking LTV your team will be focused on the right thing when making decisions about the game and monetization techniques.

2 – There is no real life time value – only predicted life time value

Knowing the real LTV requires waiting a very long time – technically you will have to wait a lifetime. You can assume some maximal lifetime – in games 180 days and 365 days are common values for the maximal lifetime. These time frames are just too long to make any meaningful decisions about marketing, product or monetization. Lets say you made a new feature and want to know if you should keep it or not – waiting 180 days for a decision is just impractical. Whenever someone is talking about life time value he means the predicted life time value. That’s the only parameter you can actually work with. To predict yours, you can use one of these 6 LTV calculators

3 – You can succeed with low LTV but not with declining LTV

There are successful games with LTVs as high as $20 or as low as $0.3. You can succeed with low lifetime value and many games have – this is especially true if you are able to constantly increase it. However, you can’t succeed if your LTV is declining – it means that something is fundamentally broken with your game.

4 – Most companies have both CPI > LTV and CPI < LTV

LTV has to be greater than CPI! There are a ton of articles that explain that If your get the basic formula right you are golden. In fact, there was even a conference with that name (http://ltvgtcpi.com). In real life however, you can’t be golden in all segments so the trick is more around finding your golden segments and expanding on them. If your app uses ads, you will need to trace ad LTV per segment using a traceback platform.

5 – In successful games most of the life time value is created after day 30

If you build a life time value spreadsheet and play around with the numbers you will soon see that typically the first 30 days contribute between 25% to 50% of the total life time value. Plugging in the known ratios of 40%,20%,10% for d1, d7 and d30 retention shows that the yield in days 31 to 180 is twice as much as your first 30 days. This means that you should invest time in giving your most loyal users reasons to play for a really long time. King has mastered that art well and Candy Crush has 1,880 levels in the game. I’m sure they are working on some new ones as we speak.

Plugging in 40%, 20% and 10% as the values for d1, d7 and d30 retention shows us that only one third of the LTV is generated in the first 7 days.

 

If your game uses ads and you want to track the LTV per cohort, segment and testing groups, you need a traceback platform. Check out SOOMLA Traceback – Ad LTV as a Service.

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Analytics, App Monetization

measuring your ad revenue in mobile apps is a tricky business and many publishers makes mistakes doing so. Here are the 5 most common ones.

This post is about the mistakes that mobile app publishers are making when measuring their ad based monetization. Whether your company is using general purpose analytics, attribution, the mediation dashboard or in-house BI to track your revenue from advertising you are probably making at least one of these mistakes.

1 – Week by Week Testing instead of A/B testing

From what I have seen so far this one is a fail for 100% of the mobile app publishers I have talked with. Lets say you want to test a new feature that increases the number of allowed rewarded videos from 3 to 5. There is a right way and wrong way to do it. A/B split is pretty easy to implement on Google play due to their controlled roll-out feature and on iOS it’s not that hard either. However, when it comes to ad revenue companies use week by week testing. In other words they implement something and compare the ad-revenue of this week vs. last week. Here are a few reasons why this is wrong:

  • There could be campaign changes between week 1 and week 2 – campaigns go up and down on the ad-network side all the time if week 2 was better due to a big campaign you might think it’s because the changes you made. A/B tests eliminate that
  • Your user behavior and usage volume might be impacted by real world events like a holiday weekend or a big sporting event – with A/B tests the events impact both groups so it’s a fair test
  • With week by week testing you have to go “all-in” and you don’t even know if the revenue change came from the group who received the change
  • It’s almost impossible to reach statistical significance with week by week testing

The reason why companies don’t implement A/B testing for ad-revenue is that doing so without a specialized ad revenue tracking solution is very complex. However, optimizing with week by week testing is very limited.

2 – Assuming all users are worth the same

Most mobile app publishers assign very specific value to each user when it comes to IAP revenue but fail to do the same for ad revenue. The typical approach is to assume all users are worth the same amount of revenue. This is in-fact very far from reality. First of all, not all users even see ads when it-comes to rewarded videos and even if you look at the group that does see ads there are users that install a few apps and are worth more than $10 while others who only watch the videos end up not generating any revenue.

3 – Not measuring your eCPM decay

“The 1st impression of a user is worth the same amount of money as the 10th impression” – FALSE. The performance of the 1st impression is higher and so the CPM that advertisers are paying in RTB are higher and the eCPMs you are getting from the rewarded video network is also higher for the first impression from the very same reason. As the same users sees more and more impressions in the same day he becomes blind to the ads and the CPM decays. Assuming that all the impressions are worth the same amount of money is a common mistake by mobile app companies.

4 – Focusing on impressions rather than Opt-in ratio

Rewarded video became one of the biggest sources of advertising revenue for mobile app companies. However, it’s important to understand that this is an opt-in type of interaction. With some games, only 10% of the users choose to see the ads while in others it can be as high as 70%. Since the 1st impression pays a lot more than the subsequent impressions, focusing on increasing the number of impressions is a mistake. Companies should focus on increasing the Opt-in ratio instead

5 – Not tracking churn by campaign creative

The last mistake is related to the relationship between ads and churn. There are 2 type of ad interaction that can cause your users to churn:

  • Ads that have a negative experience – are deceptive or have low quality creative
  • Ads of competing apps might steer your users away from your app

Not tracking the impact of different ad creatives placed by the ad-networks in your app could be dangerous.

 

If you want to improve the way you are measuring your ad revenues and stop making these 5 mistakes – check out SOOMLA Traceback – Ad revenue tracking platform.

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Analytics, Research

header image of 7 analytics platforms with ltv reporting

If you want to know your LTV and is using a free analytics platform you might find our online LTV calculators interesting. You can also see our guides for Calculating LTV with Flurry and with Google Analytics.

However, another approach is to upgrade to a paid analytics platforms that offers LTV reporting out of the box. Unfortunately, I couldn’t find this feature in any of the free analytics platforms so I guess the only way is to pay the premium. Below you can find 7 tools that offer this option and the following details about each one:

  • Depth of LTV reporting they offer:
    • Historic LTV – this is a report that summarizes the amount of revenue per install. If you wait 180 days
    • LTV prediction report – this is an algorithmic calculation that predicts the LTV early on in the user lifetime based on a formula such as this one
    • Guide for LTV prediction – Some providers offer a resource for using their reports to calculate LTV
  • Platform and engine support – Mobile operating system as well as app building tools and game engines
  • Popularity – based on number of apps that use the platform
  • Price for 1M MAU based on the pricing presented on the provider
Vendor LTV Reporting Platforms and Engines Popularity Price (1M MAU)
Swrve analytics offers ltv reporting out of the box in addition to many other features Historic LTV,  Guides for Prediction iOS, Android, Windows, Unity, PhoneGap Mid  Undisclosed
Historic LTV iOS, Android, Unity High $1,800
DeltaDNA do offer LTV prediction report Historic LTV,  LTV Prediction Report iOS, Android, Unity, GameMaker Low  $15,000
DevtoDev is a new player in the game but they are offering LTV prediction as one of their flagship features Historic LTV,  LTV Forecast Report iOS, Android, Windows, Unity, UE4, Adobe, PhoneGap Low  $2,500
Localytics is a popular platform that offers lifetime value reporting Historic LTV iOS, Android, Windows, Unity, PhoneGap High  Undisclosed
Kissmetrics provides ltv prediction based on the churn ratio Historic LTV,  LTV Prediction Report iOS, Android Mid  $5,000
Omniata offers 60 day and 90 day ltv prediction Historic and Predictive LTV iOS, Android, Unity Low  Undisclosed

Honorable mention goes to Upsight. The company is offering a very flexible solution and are trusted by some of the industry leaders. Before they merged and rebranded their Kontagent platform did have LTV prediction and while the current platform don’t support this feature I’m sure it will be added back in the future.

If you want to also analyze and predict the LTV for your advertising revenue – now there is a solution. Check out SOOMLA Traceback – Ad LTV as a Service.

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Analytics, App Monetization

knowing who is advertising in your app could have multiple benefits for you

In-app ads are getting more and more popular these days. The increase in CPI levels alongside the penetration of brands have made it possible to build a successful ad-support app company and many are doing so. Given this trend, it’s becoming increasingly important to understand who is advertising in your app but that’s not an easy task today. Most likely, an app developer will be using ad-networks to place ads in his apps and so this setup doesn’t allow the him to get reports on the identity of the advertisers.

Verify that you are not helping your direct competitors

One reason to track the ads in your app and reveal the advertisers’ identity is to make sure you are not helping your direct competitors. You might be able to manually check this by opening your app and using it yourself until ads are shown but keep in mind that the ads you see in one country are different than the ads shown in another country and sometimes campaigns even change by time of day. To do this right you need a 24/7 operation in 249 countries which is quite impossible to do manually. Luckily, there are several tools to do this. On mobile web, Adclarity and GeoEdge are providing this service and for Mobile Apps you can check SOOMLA Traceback.

Monitor ad integrity

Another reason to know what ads are running is to enforce policies you may have in-place with regards to ads been shown. In ad-supported apps, ads are part of the overall experience and ads that are in-appropriate would damage your brand, lead to bad reviews and hurt your retention. Here are some of the ads you want to weed out:

  • Inappropriate ads such as ads with nudity
  • Ads with deceiving UX and false promises
  • Offensive ads

Understand what’s driving the eCPM you are getting

Knowing what campaigns are been run by ad-networks allows you to improve your monetization strategy and up your ad-operations game. Lets think about a situation where you are using Vungle and Unity Ads and Vungle’s eCPMs have been higher in the last few days and they are on top of the waterfall. Tomorrow, their biggest campaign might end and the eCPMs would drop. If you would wait for the ad-mediation to pick up on this change you might get 2 days of low eCPMs until the waterfall configuration is changed by the mediation auto-pilot. Knowing about a campaign that just ended would allow you to respond more quickly.

Get ideas for direct deals you can make

You might have heard that direct deals with advertisers can bring higher monetization levels by cutting out the middle man. Knowing who is currently advertising in your app can give you tips about the best advertisers for your app and can give you the information that the advertiser would ask about.

If you are using ad networks to monetize your app you should check out SOOMLA TRACEBACK. In addition to advertising revenue attribution you can also get information about the campaigns running in your app.

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Analytics, App Monetization

calculating the ROI of cross promotion activity is complex and requires a powerful analytics service

Once your company has been in the mobile app space long enough it will normally have more than one app and you will start promoting one app within another. This is normally referred to as cross promotion or cross promo for short. In reality, you are simply advertising one of your app inside another and you are reducing the advertising space that you could have given ad-networks. There is a clear trade off between cross promotion and ad revenue so once you start attributing your advertising revenue, you would want to also get a grip on your cross promotion ROI.

Example of cross promotion ROI calculation

Lets think about a single user journey. You paid $3 CPI to bring him to your first app, he generated $1 in the first app that had aggressive cross-promo ads. Over 2 months of playing he watched 500 cross promo ad impressions and then eventually installed your second app where he generated additional $5 in from buying in-app products of the second app. The total he paid you is $6 on a $3 marketing investment. In high level this was a good user but we are interested in the cross promotion ROI. We need to look at each app individually:

The first app lost ad revenue on the cross-promo

When we are looking at the story from the perspective of the first app we realize that there is more to it. The first app paid $3 and only received $1 direct revenues. If we don’t assign any revenue to the cross promotion ads the manager of the first app will stop the campaign that brought this user. Furthermore, the 500 ad-impressions could have generated a few dollars from those 500 ad impressions if it weren’t for the cross-promo ads.

The second app received a free install

The story of the second app is that the $5 generated usually comes with a significant marketing cost. While the manager of the second app could claim $5 to his profit, the reality is that some of this revenue should be claimed by the first app. Lets see how much exactly using two different methods

Use affiliation model to assign a share of the revenue

The first option is to simply decide that a portion of the revenue generated by the second app will be considered as generated by the first app in return for the cross promotion. The origin of this method is in affiliation models and the typical split is between 30% to 50% for the app that brought the traffic. If we go back to our example and use a 50-50 split, the first app would claim $2.5 from the $5 generated in the second app. The ROI analysis will be as follows:

  • The first app spent $3 and received $3.5 – profit of $0.5
  • The cross-promo ads yielded $2.5 on 500 impressions – eCPM of $5
  • The second app spent $2.5 and generated $5 – profit of $2.5

This method is a bit complex and requires the internal BI to track user activities between different apps. In addition, the time that might pass until the 2nd app generated the revenue could be long which makes it hard to relay on.

Assigning a CPI value based on market price

Another method is more inline with how the mobile app economy operates today. Simply decide on a CPI value that the 2nd app is willing to pay based on what they are currently paying. If the 2nd app normally pays $4 CPI, that should be the price. The ROI analysis in our example will there for be:

  • The first app spent $3 and received $5 ($1+$4) – profit of $2
  • The cross-promo ads yielded $4 on 500 impressions – eCPM of $8
  • The second app spent $4 and generated $5 – profit of $1

Prioritizing cross promotion in your mediation platform

Another aspect to consider here is the waterfall configuration. In both methods the ROI analysis we did also yielded an eCPM figure. This figure should be used in the mediation configuration of the first app. If you have providers that can pay higher eCPM they should get higher priority as they will yield more revenue from the company for the same impressions.

Attribution aspects – who brought the install

The example we used is simplified in the sense that the user was only exposed to ads of the second app in the first app. In reality however, he is also likely to get exposed to the second app in other channels. To avoid a situation of double compensation it is advised to apply the same logic you are using when attributing your regular/external campaigns. Typically this is means last click attribution with a 30-day attribution window for clicks and optional 7-day attribution window for impressions.

Another important measurement aspect is to be able to count the cross-promo impressions and differentiate them from the regular ad impressions.

If you want attribute your ad revenue and be able to track cross promotion ROI you should check out SOOMLA Traceback – Ad LTV as a Service.

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