App Monetization

IMG_4102One of the things that were a part of mobile apps since the early days is the REMOVE ADS button. The idea is simple – ads generate low amounts of revenue per user and getting $0.99 or $1.99 from them is better from the app publisher stand point.

Not showing ads to payers is the standard practice

Even in games that don’t have a specific purchase option around removing ads it became a standard practice to not show ads to depositors. This is based on the same approach that ads yield low amounts of revenue while purchases yield higher amounts.

Rethink what you know – ad whales exist

In recent posts we covered the existence of ad whales. Individuals who generate large amounts of ad revenues for their app publishers. Here is a user who generated $74 in ad revenue in November, and this user generated $52 in December. While these levels of revenue per user are quite rare for ad monetization, they are also quite rare when it comes to in-app purchases.

How many users generate enough ad revenue to level with payers

If we consider how much revenue is generated by a payer – the minimum is $0.7. The lowest purchase by a user is $0.99 and given that Apple and Google take a cut of 30% the publisher gets 70 cents.
Based on the data SOOMLA Traceback is collecting we can check how many users go over the point. How many monetize with ads at least to the same level as payers. The result is that in some games that relay heavily on ads it’s more than 10% of the user base. This is higher than a normal conversion rate to payers. We can also check how many users went over $3.5 which is the publisher share of a more $4.99 purchase by a user. The result is that it’s over 2% in some games.

Rewarded videos offer incentives to users

Let’s start thinking about a different approach. Should we allow any type of advertising to people who paid? One area to consider is the type of advertising in question. Ads that may annoy a paying user could be a bad choice from a user experience perspective but what about incentivized formats such as offer walls and rewarded videos. These formats are loved by users so the question becomes more about optimizing the revenues.

Option 1 – reversed approach

Let’s imagine for a second a complete mirror image of the “no ads for payers” approach. What this means is that we set a threshold of $0.7 and the users who have made at least $0.7 in ad revenue are considered ad-whales. Once we classified someone as an ad-whale, we don’t allow him to make purchases in the game. That would be the reversed approach to the “no ads for payers” approach. If it sounds silly to you – it’s because it is silly. Blocking someone from paying in a game is just nonsense but so is the “no ads for payers” approach. Why block someone from making revenue for you through watching ads?

Option 2 – balanced approach

A more reasonable approach to the problem is to simply allow users constent access to all methods of getting benefits. A user can get benefits by buying them, by watching video ads, or by taking on offers. Since the payout of a video view by a user is normally determined in retrospect, the publisher could apply a model where the rewards are dynamic based on the past payouts received for that user. If such a model is implemented, the publisher can guarentee that the price of getting the benefit is balanced across the different methods the user has for getting them. For example, if the eCPM of a user starts falling after a while, his rewards for watching videos will decrease and he will be more inclined to make purchases. If however, the eCPMs for a specific users are growing over time, the rewards he will get from watching videos will increase and he will have more motivation to keep watching them as opposed to buying something.

Ad measurement tools are becoming a must have

This type of innovative monetization strategies are becoming critical for the survival of game studios. We covered before the increase in CPI rates and how companies needs to adapt to stay relevant. Advanced segmentation and monetization measurement tools that can find the ad whales segment for you are becoming a must have in today’s mobile eco-system.

 

If you want to start measuring your monetization and find ad whales you should check out SOOMLA Traceback – Ad LTV as a Service.

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App Monetization

Kate Uptown is starring the Machine Zone (MZ) ads for their Game of War which has been advertised heavily in the last 24 monthsDemand diversity is a topic not many people discuss in the mobile game monetization forums. To understand it let’s think about the journey or a user through our app. The first time the user watches an ad, the mediation will check what is the ad-network that is on the top of the waterfall today and will have that network serve the ad. The network will normally try to serve the highest yielding campaign they have – why don’t we call the app in the ad Mobile Assault – it will help us refer to it later. In many cases, this user will see the same ad over and over again in the same day and more time in the next day. Having a user see an ad for 100 times these days is not uncommon. This is the demand diversity problem I’m referring to.

Why demand diversity is important

From a user perspective, seeing the same ad over and over is a poor user experience. The first time you are seeing an ad, it could be interesting, cool or even funny. If you have seen the new Clash Royal ads, they are quite amusing. However, nothing is interesting, cool or funny after you have watched it a dozen times already. At that point, it is just annoying.
From an ad effectiveness perspective, showing the same ad over and over is a bad choice as well. It leads to banner blindness so users stop noticing the ad. Most ads today are shown with the purpose of creating installs for tha advertised app and blindness leads to low click rates and conversion rates so less installs are generated.

The business models determine who takes the risk

One of axioms of online advertising is the chart below. Basically it says:

  • In a CPM model the risk is on the advertiser side while the publisher has guarnteed income
  • CPC is the middle ground
  • In a CPA/CPI model the risk is on the publisher side while the advertiser has guarnteed outcome

Illustrative chart showing the risk levels for publishers and advertisers based on the selected business model: CPM, CPC or CPI

The mobile advertising industry today is mostly driven by the CPI model which is a form of CPA meaning that the publishers assume most of the risk. They place ads in their apps hoping to get paid but their monetization is driven by whether or not the users ended up taking additional actions outside of their apps.
So now that we established who has the risk, we also know how is the one that gets heart from the situation. Users who watch the same ad over and over again become blind to it and the publishers’ monetization levels are getting hurt.

Risk and data are normally aligned

In most business situations, the party who is willing to takes the risks is the one with better tools to assess it and mitigate it. For example, in a CPM model, the advertiser assume the risk but they demand transparency about where their ads are being placed and have tools to measure the performance. In mobile app monetization however, the publishers are the one assuming the risks but they are doing so with complete lack of data or measurement tools. More specifically, the publishers are the ones that get hurt from the lack of demand diversity but they actually have no way to measure and manage it.

Mediation platforms are also left in the dark

The parties that are in the perfect position to be the police of demand diversity are the mediation platforms. Publishers are trusting the mediation companies to act as their agents and help them manage things of this sort using their ad-tech expertiese. The problem is that mediation companies are also in the dark about what ad is being shown to the user. They simply call the ad-network SDK as a black box that shows ads but they don’t get any information out.

Ad networks only see their own ads

The only type of company that has information about what ads are shown to the user are the ad-networks. The problem, however, is that each ad-network is only aware of what ads they show. Instead of collaborating and sharing this data between them and be part of the solution they are part of the problem since an ad-network that is not aware of what other ad-networks are showing is likely to show the same popular advertiser again to the user.

Choosing ad networks smartly

App companies often tend to choose ad-networks based on rumors of their projected CPMs or based on how well it worked for their friends. Often, one ad-network will seem better than another in the eyes of the publishers due to their presence in shows and their general brand perception. However, choosing 4 networks that are practically representing the same demand menas making the problem worse. It’s common to see a rewarded video stack that includes Supersonic/Ironsource as the mediation in addition to Vungle, Adcolony, UnityAds and Chartboost as the ad-networks. These networks are considered the best when it comes to rewarded videos for mobile games. The problem here is that thery are all bringing similar types of ads. The chances of a user seeing the same ad over and over again is much higher like that. A smarter strategy for selecting ad partners is to try and figure out how to diversify. SSPs can often bring more diversification through access to exchanges and there are also companies like Mediabrix who focus only on bringing brand advertising.

Diversifying through blacklists

Most ad-networks supports blacklists as a way for publishers to block certain advertisers from placing ads in their apps. This is mostly used for 2 things: 1) blocking competitors and 2) blocking inappropriate ad content. This feature however, can also be used to force ad-networks into skipping ads that are being shown too much. If you focus on the top 5 ads shown in your app and only allow one ad-network to serve them you will force the other ones to bring new ads and diversify the user experience.

Getting more visibility to what ads are being shown

While a solution to this problem might look far fetched at the moment, it’s actually feasible. The ad-networks are under a lot of pressure to be more transparent at the moment and this is one area where if each network gives up some transparency it can receive a lot in return. After all, ad-networks also loose from ad blindness. It will be a better world for everyone, publishers, advertisers, mediation platforms, ad-networks and users. However, someone needs to take the first step. Until then, feel free to contact SOOMLA if you want access to this kind of information. A side benefit of publishers gaining access to this info is that it will accelerate the path to full transparency by the ad-networks.

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Guest Post, Marketing

Baas offers a one stop shop but big app publisher often chose an architecture with internal DB and complementry point solutions

Disclaimer: There is no universal remedy for every single purpose: you have to see what aims you pursue and how exactly a push provider is going to boost your product.

Still, push notifications are not a supplement to a backend but a superior communication channel between you and your audience. If you feel this difference, you’ve already outgrown the “appendix” push notifications almost every BaaS platform offers. This article will help you realize you are all set to win the market using mature push campaign.

To make it more vivid, imagine your product is a car going through a race. Marketing strategy is a car engine aimed at helping you move forward and stand out. BaaS push notifications, as a part of marketing strategy, make it possible to start moving. However, it’s too slowly and you never foreknow when the engine runs too hot and gets broken. Point solution for push messaging is a dual fuel injection that empowers the engine and rushes you forward at full speed, so fast you just can’t back off.

Yet let’s go step by step.

To Each His Own

As stated in the disclaimer, it all depends on your goals, on what you are going to achieve with the help of your project. BaaS with push notifications is undisputedly the right choice for small apps with simple logic and basic push requirements. In other words, if your team consists of you and your idea only, it’s definitely a point in outsourcing a database for your project. The cloud backend lets you focus solely on the UX and business logic of your app, replacing your entire server infrastructure with an API-driven service. You’ll also get push notifications but in limited functionality mode. Anyway, it’s still enough to say meaningless “hello” to all your subscribers at once, but you won’t go beyond the simple push that way. No way you can guarantee to one million subscribers they will get notified the time and the way they like.

So, if you have a complex app and business goals to achieve with push notifications, BaaS is not the solution you are looking for. If you want to quickly prototype and run a proof of concept app – you’d better outsource backend.

4 Point Solution Perks BaaS Users Miss

Segmentation is a power feature not available through BaaS provided push

When you decide to use push notifications in marketing strategy, you want to have clear returns on your effort. And it’s impossible to make money out of push messages just by saying “Hello, Marianne”. It’s much better to say “We hope you are having a great weekend, Marianne! The red jeans you’ve added to your shopping cart are still trendy. Take a look at related items!”.

You will need advanced segmentation features to address the audience individually, the time when they are most responsive. That’s the right way to deliver them the reason and urgency to make a purchase.

Here come the marketing possibilities a point solution offers. Services like Pushwoosh has advanced segmentation that helps you precisely target your audience across different types of segments. Need to notify female users aged 25-35, who spent more than 35$ making in-app purchases last month? Not a big deal! Use Tags and Filters to collect additional data from your customers and send relevant messages to every single user, inviting them to visit your app.

Not all solutions deliver push notifications with the same speed

Delivery speed is a point of interest for every push provider. When your job is to keep notifications running fast and smoothly, you are interested in making guarantees for your users. The volumes may vary, but you are sure delivery speed is safeguarded when you sign an SLA with a push service.

Let’s say you are going to address 100000 users with a push message. If something goes wrong and notifications are being delayed – you can always rely on the previously signed SLA. As a result, both parties benefit from this mutual commitment.

The full solution approach of BaaS limits your flexibility

It is really important to stay flexible when you have an application released. The market is changing and you may need to evolve to stay ahead. BaaS is not a one-stop-shop for app development, you may require custom code, 3rd party integrations or complicated business logic on your stored data. Bummer! As a BaaS user, you are bind to use features your provider offers, but no more. By the way, these features are most likely proprietary, so you can’t migrate from your backend provider to another BaaS. Moreover, if you are a Firebase user, you are vendor-locked, and we all remember what happened to Parse, no matter how good it was.

In that case, having an internal database infrastructure is much more flexible, since you are fully in control of your product. Push notifications and other point solutions complement your feature set when keeping database infrastructure internally.

Point solutions approach proves more cost effective than BaaS in the long run

It might be profitable to let someone host your backend and handle your pushes at the very beginning. This entire database headache doesn’t bother you and you are free to create a design of your dream that drives tons of conversion. Everything is great and you are happy…until you make it. Out of the blue, you realize your app needs functionality that requires your own backend code. When you get 100,000 users and hit storage limit, you’ll be surprised to see BaaS can’t manage your custom needs. On Firebase free plan you have 100 simultaneous connections. It means you can handle 100 users at the same time, making 1 call for each of them. Or even 50 users at once, if making 2 calls for each. And what if you decide to boost your marketing campaign using push notifications? You’ll get cropped functionality incapable of sending targeted push messages and addressing your audience effectively. Gosh, it all hurts!

You’ll be forced to find another backend solution or DIY your database. It will result in extra development efforts, extra costs and extra time spent, which are lethal for the product.

Why should you doom yourself to titanic efforts? It will be much more reasonable in long-term perspective to deploy and maintain your own backend and let professionals run your push campaign. For example, you can reach 500,000 customers with unlimited push messages, and that’s just the free plan Pushwoosh offers. When you have your own backend infrastructure, you are unaffected by provider limitations and free to try each and every feature a push notifications solution has. After all, isn’t it the genuine goal of a trial period?

Takeaway

Well, it’s surely no crime to use push notifications offered by a BaaS platform. Depending on the project size and complexity, this solution can help you bootstrap your product and get to the market as soon as possible. And that’s great for model-view-presenter applications.

However, if you are seeking for long-term benefits and push messaging is a major part of your marketing strategy, you are good to go. A point solution is your lucky ticket.

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App Monetization, Infographic

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This is the 3rd post in the series. The ad viewer of January is a user who made his app publisher very happy by generating the most amount of ad revenue for him when compared to other users. Our unique tech allows our customers to associate ad revenue to the user level and measure ad ltv. To check out previous months’ over achievers, follow this link for December and this one for November.

January Ad Viewer of the Month

This user alone generated 53.39 dollars for his app publisher in 20 activity days during January. What’s also interested is that he only recently started using the app – in mid December. The user contributed a bit over $20 in Dec. which makes his ARPU to date or his LTV to date $74. We are sure it will get even higher as he generates $2.66/day on average during January.

Attribue Ad Viewer of November
Country  United States
Device  iPad
Ad Types  Interstitial
Impresions  416
Active days 20
Revenue $53.39
eCPM $128.36
ARPDAU $2.66

IMG_4044

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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Marketing, Research

Singular ROI Index symbol with a banner saying best ad networks over a blue background

About a week ago Singular released a very interesting study ranking different traffic sources or user acquisition channels according to how much return on ad spend they bring for companies using them. Return on ad spend (ROAS) or marketing ROI has been a critical KPI for marketers in the mobile ecosystems. It allows decision makers to compare marketing activities not only by the amount of received installs but also by how much dollars were received from users who arrived through the channel and compare cost vs. return on each channel sperately.

The Singular ROI index

The study can be downloaded via this link – Singular ROI Index. It ranks the top 20 ad-networks in terms of ROAS for Android and the top 20 for iOS. It also draws some interesting insights about the differences between these two ecosystems. It finds that despite higher CPIs on iOS the ROI is 1.3x when compared to the ROI for the same app on Android running via the same ad-network. This is partially due to higher average payout on iOS.

What about Ad Revenue

The report is lacking in one aspect – it only accounts for In App Purchase revenues for ROAS calculation. A more complete view on ROAS today would consider 3 elements for each channel:

  • Cost for that media channel
  • IAP revenue made by users who came through the channel
  • Ad revenue generated by users who came through that channel

Factoring in the ad-revenue generated from in-app ads in the ROAS calculation is becoming more and more important as the change in the mobile monetization landscape continues. This means that ad-networks who bring users who don’t convert to payers but do convert into ad-whales are under indexed in Singular’s report and networks who brings users who convert to payers but don’t contribute any ad-revenue are over indexed in the report.

Your own ROAS should also consider ad revenue

If you are using Singular for calculating your own ROAS, your decisions may be subject to the same measurement errors. Fortunately, there are already solutions for attributing ad revenue and completing your ROAS picture such as SOOMLA TRACEBACK consider using them and connecting them to Singular.

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App Monetization, Industry Forecasts

Latest report from App Annie supports the claim that the market is choosing View to Play as the business model of the future for the mobile ecosystem

Last November, while most of us were already preparing for the holidays, AppAnnie released a very interesting report that might have gone unnoticed by some of you. One of the Key Learnings is that Free-to-Play is giving way to View-to-Play. In other words, the fastest growing business model in the next 5 years in mobile apps will be in-app advertising and not in-app purchases.

In app advertising is growing at 24% CAGR and expected to surpass $110B by 2020 while Freemium is trailing behind

About App Annie and the report

The App Market data company needs no introduction from us and has become the source of data for most of the industry with regards to app store data. The company has over 600 employees in over 13, many of which are focused on researching data. From time to time, App Annie generates industry reports and forecasts and shares those through it’s blog and other content channels.
Company website – https://www.appannie.com/
Report Download Page – http://go.appannie.com/report-app-annie-app-monetization-2016-dg

What is View to Play?

If you haven’t heard the term View to Play, it’s probably because it’s new. When the app store just emerged, apps were sold and not given away for free. With the introduction of In-App Purchases, developer quickly started offering free apps to attract more users and find different ways to monetize them. This led to a new breed of game companies that specializes in conversion optimization, analytics, segmentation and performance marketing – the term Games as a Services was coined to reflect these new practices as well as Free to Play gaming. View to Play is similar in approach but instead of pushing users towards in-app purchases, the optimizations are focused around ad based monetization models – hence, “View to Play”. Users who want to advance in the game are often offered rewards and incentives for watching ads and a new breed of companies emerges with a toolset that includes special analytics capabilities around ad revenue measurement.

What is Driving the Change

In a recent article we covered how CPI is increasing and companies needs to adapt quickly. Well, some have already started and the App Annie report hints that more companies will be adopting the view to play model in the future.

These companies are realizing something that others have not. The CPI increase is highly correlated with the expected increase in ads LTV. They are both been driven by the same forces – the total mobile advertising spend is increasing twice as fast as the user growth. IAP revenues are actually increasing slower than the user growth and are becoming more and more concentrated in top grossing apps.

The cost per install is increasing over time as well as the average ad based revenue per user while In-App Purchase models are declining

This means that companies who transition quickly to view to play will be far better prepared for the future increase in CPIs. That is, as long as they can also adapt their measurement and optimization practices with a platform such as SOOMLA Traceback.

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App Monetization, Infographic

Ad viewer of December is the user who generated the most amount of ad revenue for his app publisher

We are continuing the series of Ad Viewer of the month that we started last month. This type of analysis is one of the things that sets SOOMLA apart. We are using the Traceback technology to provide publishers with reports that get as granular as a single user. The Ad Viewer of December is a single user who made the most amount of revenue for the publisher of the apps he was using. Here is the link for last month’s report – Ad Viewer of November

December Ad Viewer of the Month

The amount of ad revenue generated by this user is mind blowing – $52.92 generated for the app publishers. He registered 19 active days in the month of December and made an average of $2.78 in each one of them. Unlike the Ad Viewer of November, this user also received a lot of in-game rewards for his revenue contribution. His favorite ad-types were Offer Wall and Rewarded Video that surely gave him incentives for his ad interactions.

Attribue Ad Viewer of November
Country  United States
Device iPhone
Ad Types  Offer Wall, Rewarded Video
Impresions  398
Active days 19
Revenue $52.92
eCPM $132.98
ARPDAU $2.78

Infographic featuring the ad viewer of December and different attributes about him. How much revenue was generated and at what eCPM

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Analytics, Marketing

Reality can prove very different than the statistics that represent it

There is a simple idea at the core of most mobile marketing campaigns these days – if you spend $x on some marketing activity and received $y in return you want y to be grater than x. This is often referred to as ROAS or campaign ROI. We have trained mobile marketers to break down their activities to small units: ad groups, ad sets, ad creatives, audiences, … and find the ones that show ROAS. Doubling down on the positive ROAS units while shutting down the negetive ROAS units is the leading campaign optimization strategy today.

Here is the problem – it only works under certain conditions.

There is a famous saying by Mark Twain – “There are lies, damned lies and Statistics”. It comes to warn people about using statistics in a wrong way. One such way is using statistics when small numbers are involved. Another way in which statistics are deceiving is called Multiplicity or Multiple comparisons. Let’s see how those come into play when calculating returns.

Beware of the small numbers

Most companies base their ROAS calculations only on revenues from In-App Purchases. This is a result of 2 things:

  • Up until recently, ad based monetization and ad spend were mutually exclusive
  • Until SOOMLA TRACEBACK there was no way to attribute ad monetization

The problem with In-App Purchases revenue is that it’s highly concentrated. Studies have shown that purchases are less than 2% of the users and among those 2%, the top 10% generate half of the revenue. Let’s say that you spent $5,000 to acquire 1,000 users and you are trying to figure out the return. Most likely you have 20 purchases but there are 2 whale users who generated $1,500 each (this is aligned with the studies – yes). Now, suppose you had 2 ad-groups in that campaign and you are trying to figure out which one was better. Here are the options:

  • Group A had both whales
  • Group A had one whale and B had one whale
  • Group B had both whales

Since we are talking about 2 users here – the scenario that actually happened would be completely random. Even if one ad-group is better than the other it is still very likely for that group to outperform the other group when we are talking about only 2 users who can flip the outcome completely. The danger here is that our UA teams would double down on the ad-group that yielded the 2 whales without understanding that it’s not better than the other. If we look at sample sizes here n=1000 is normally considered a good sample size. Has the monetization been less concentrated a sample size of 1,000 should have been enough to make decisions. However, for the purpose of acquiring whales the actual sample size is n=2 in this case. We should try to get at least n=500 before we start making decisions on media buying. The problem of course is that attracting 500 whales could be a very expensive test – more than $100,000 based on the numbers in the example above.
On the other hand, companies who monetize with ads enjoy the fact that more users participate in generating revenue and can make decisions based on smaller sample sizes and smaller test budgets.

Multiplicity – the bias of multiple shots

Another bias we normally see in mobile marketing is Multiplicity. The easiest way to explain this is with the game of basketball. Let’s imagine you are through from the 3-pt line and you have 50% chance to score. What happense if you try twice, the chances of scoring at least once becomes 75%. With 3 shots, it’s 87.5% and so forth. The more times you try the better your chances to score.This is what happens when you try to hard to find positive ROAS in a campaigns that has a lot of parameter. You compare ad-groups – that’s 1 shot, you compare ad creatives – that’s a 2nd shot, you compare audiences – that’s a 3rd shot and so forth. The more you try to find a segments with positive ROAS by slicing and dicing the more likely you are to find a false positive one.

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Industry Forecasts, Marketing

IMG_3787

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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SOOMLA - An In-app Purchase Store and Virtual Goods Economy Solution for Mobile Game Developers of Free to Play Games