Acquisition of users for mobile applications: Post install optimization
Buying large volumes of cheap traffic, including motivated traffic, is no longer as effective as it used to be. What are the reasons? Firstly, this was influenced by increased competition in app stores. Secondly, ad networks begin to compete for publishers - accordingly, the cost of installation increases. And the third reason is the deterioration in the quality of mobile games due to the acceleration of development speed.
Among other trends in the mobile advertising market, it can be noted that the majority of advertisers and game developers stop buying a large amount of traffic, while buying less volume, but higher quality. It should be noted that CPI, the metric that was responsible for quality, ceases to be an important criterion when choosing an advertising network. In this regard, we are witnessing a transition from the CPI model to the CPA model.
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Developers are trying to look for those models of monetization, traffic purchases, which will be less risky, and large publishers are increasing their marketing staff. Recently, analytical services for automation and mediation have been greatly developed, because there is a lot of data and they need to be aggregated and analyzed.
The importance of ASO, reputation management (reviews, evaluations) and social integration is increasing. You need to do projects that become viral and acquire users for free.
On the one hand, this is the work of the development team or game designers to create and improve the funnel of engaging users in an application or game.
On the other hand, this is marketing, namely: the optimization or improvement of the sales funnel that occurs after installation. It includes tracking registration, login, tutorial passing, loyal users, customer retention rate, number of likes, reposts and purchases.
What is important for us now is that, knowing CTR and CR, you can predict how much you can earn.
Consider the example that Unilead provided us: a simple game where we made a test purchase to calculate an approximate ARPU. They took the day X, got 1000 installations for it. We looked at how much these users in 30 days will bring money - $ 250. ARPU in the end - $ 0.25. With such an ARPU, it is quite difficult to buy large volumes of traffic and go to the tops. We need to come up with a model in which the money gained from the built-in purchases would be enough to pay off marketing expenses and still earn something.
Soft launch results :
Unilead bought quite a few installations, got a lot of organics, which came through viral channels. But nevertheless ARPU was low, and also the short life of the user was noted.
Two tasks were set before marketers:
And although the viral effect was quite decent, ARPU was still low and developers were looking for new ways to increase in-app monetization.
It became clear that, taking into account the organic matter that the game received from viral channels, it was possible to afford to optimize advertisements by CPI in two monthly ARPUs, i.e. Start a new test UA campaign, with CPI = 2 * ARPU (30).
What they got : they realized that the model with patterns allowed us to sift out the wrong creatives quickly during the day.
This led to a clear and transparent KPI for media brands, they knew which banner worked well and could raise the bid. The important thing is that the network collected data for each creative ID - gender, age, geo. Advertising channels were divided into formats (video, native, interstitials) and publisher IDs.
In the process, Unilead was faced with the fact that CPI did not talk about efficiency, about how much the publisher earns.
This prompted to invent a new performance indicator K-margin = ARPU (30) - CPI. Naturally, it was negative due to installation prices, and the ultimate goal was to optimize K-Margin and reduce it to zero.
How to do it? There are three ways:
The network has noticed: the higher the CTR increases, the higher the CR becomes, while K-Margin decreases.
But not always high CTR and CR led to high ARPU. The reason for this was mislead-creatives, which did not reflect reality, were misleading or did not meet expectations.
To solve the problem with mislids, the patterns for which the highest average check and the largest number of paying users, and the banners by which they came, were analyzed. As a result, only those that brought the most valuable users remained in the banners and in the targeting. paying later in the game itself.
It is worth noting that most developers do not always pay attention to creatives. It seems to them that the banner was a success, but in fact it works poorly. The marketing team has no direct contact with the designers and cannot tell what is working and what is not. And designers do not always know what works well and what is bad. Therefore, they cannot learn to make the right creatives.
A narrow target audience for a banner leads to a faster CTR.
Among other trends in the mobile advertising market, it can be noted that the majority of advertisers and game developers stop buying a large amount of traffic, while buying less volume, but higher quality. It should be noted that CPI, the metric that was responsible for quality, ceases to be an important criterion when choosing an advertising network. In this regard, we are witnessing a transition from the CPI model to the CPA model.
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How does this affect the developers?
Developers are trying to look for those models of monetization, traffic purchases, which will be less risky, and large publishers are increasing their marketing staff. Recently, analytical services for automation and mediation have been greatly developed, because there is a lot of data and they need to be aggregated and analyzed.
The importance of ASO, reputation management (reviews, evaluations) and social integration is increasing. You need to do projects that become viral and acquire users for free.
Post install optimization
On the one hand, this is the work of the development team or game designers to create and improve the funnel of engaging users in an application or game.
On the other hand, this is marketing, namely: the optimization or improvement of the sales funnel that occurs after installation. It includes tracking registration, login, tutorial passing, loyal users, customer retention rate, number of likes, reposts and purchases.
What is important for us now is that, knowing CTR and CR, you can predict how much you can earn.
Consider the example that Unilead provided us: a simple game where we made a test purchase to calculate an approximate ARPU. They took the day X, got 1000 installations for it. We looked at how much these users in 30 days will bring money - $ 250. ARPU in the end - $ 0.25. With such an ARPU, it is quite difficult to buy large volumes of traffic and go to the tops. We need to come up with a model in which the money gained from the built-in purchases would be enough to pay off marketing expenses and still earn something.
Soft launch results :
Unilead bought quite a few installations, got a lot of organics, which came through viral channels. But nevertheless ARPU was low, and also the short life of the user was noted.
Two tasks were set before marketers:
- Think of how to increase the ARPU
- Sell the game.
And although the viral effect was quite decent, ARPU was still low and developers were looking for new ways to increase in-app monetization.
Marketing at PIO
It became clear that, taking into account the organic matter that the game received from viral channels, it was possible to afford to optimize advertisements by CPI in two monthly ARPUs, i.e. Start a new test UA campaign, with CPI = 2 * ARPU (30).
What they got : they realized that the model with patterns allowed us to sift out the wrong creatives quickly during the day.
This led to a clear and transparent KPI for media brands, they knew which banner worked well and could raise the bid. The important thing is that the network collected data for each creative ID - gender, age, geo. Advertising channels were divided into formats (video, native, interstitials) and publisher IDs.
In the process, Unilead was faced with the fact that CPI did not talk about efficiency, about how much the publisher earns.
This prompted to invent a new performance indicator K-margin = ARPU (30) - CPI. Naturally, it was negative due to installation prices, and the ultimate goal was to optimize K-Margin and reduce it to zero.
How to do it? There are three ways:
- Reduce CPI (CPM) rates, this is reflected in the amount of traffic.
- Increase CTR
- Increase CR
The network has noticed: the higher the CTR increases, the higher the CR becomes, while K-Margin decreases.
But not always high CTR and CR led to high ARPU. The reason for this was mislead-creatives, which did not reflect reality, were misleading or did not meet expectations.
To solve the problem with mislids, the patterns for which the highest average check and the largest number of paying users, and the banners by which they came, were analyzed. As a result, only those that brought the most valuable users remained in the banners and in the targeting. paying later in the game itself.
It is worth noting that most developers do not always pay attention to creatives. It seems to them that the banner was a success, but in fact it works poorly. The marketing team has no direct contact with the designers and cannot tell what is working and what is not. And designers do not always know what works well and what is bad. Therefore, they cannot learn to make the right creatives.
A narrow target audience for a banner leads to a faster CTR.
findings
- With proper targeting, you can achieve an increase in ARPU.
- Different audiences perceive different creatives. Find the right creative and make a small test, target the small group. If you see that the CTR is falling, make the targeting wider and the stakes higher. Create your own patterns, count K-margin. When it seems that you have tried everything - change the creative.
- If you target a small group, such an audience burns out - change the banner more often.
- A CTR of 2% (even if you pay for the installation) will bring you twice as much money as a CTR of 1%.
- You need to constantly optimize the store page. The user decides (to put the application or not) for 3 seconds, do not forget about it.
- And be sure to collect data from networks and aggregate them at home, and remember that patterns need to be shared.
Source: https://habr.com/ru/post/297578/
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