In the complex world of user acquisition, it is important to build an effective strategy that targets the right user and the right channels. If you want to walk the path to success with your mobile app’s user acquisition (UA) campaigns, everything depends on reading the data correctly and taking the right steps.
And it’s a continuous process that requires marketers and UA professionals to break down the data on a granular level and then tie it back to performance and other post-install activities.
High granularity has been crucial to marketers; it provides detailed and specific insights to the audience you should be targeting and as a result, lets you get more conversions, be it installs, or actions within the app. The rich data sets enable marketers to create and deliver personalized ads to potential customers that have a higher chance of catching their attention.
Why are we focusing so much on granularity?
Well, basically if you want to successfully operate your campaigns and increase your ROAS, you can’t just blindly advertise to everyone in the market. Sure, you’d get a return from some, but why would you lose money and time on the uninterested audience while you can target specific individuals that will have interest in your app, right?
The right level of granularity in user acquisition empowers us with the most accurate and actionable insights.
It provides information on not only the better performing channels but also the performance of dimensions within these channels, from ad set to creatives and even source-app.
Granular data reflects customer behavior and helps marketers with reducing the errors in targeted audience profiles. You can find out which country, gender, age group, or even which education level is performing better for your user acquisition campaigns, and therefore when you use the data to tailor your audience, you reach the right users and achieve a higher return on your marketing activities.
On the other hand, when you’re working with limited granularity, you’re ruling out the fact that not everyone in your audience behaves the same. For instance, let’s say that you’re targeting an audience of people between the ages of 18 - 54. It is highly possible that the age groups of 18 - 34 and 35 - 54 would be taking different paths while interacting with your app, they might not be interested in the same messages, creatives, or even the same call to action (CTA). It’s even possible for the ages between 18 - 34 to act differently when it comes to responding to your ads. So when you target the 18 - 54 age group without subdividing and do not optimize towards divisions, your campaigns become exposed to what we call value leakage.
I don’t get it, what is value leakage?
Value leakage occurs in two ways:
- when you spend your marketing budget on unqualified users or
- when you’re not spending enough on advertising to qualified user
In the first case, it’s relatively straightforward to see how value leakage occurs, because you’re basically losing money by advertising to users who will not bring you sufficient return to justify your costs.
How value leakage occurs in the second case is more subtle. If you find the right audience but limit how much you spend on them, you may be losing out on additional profitable users who are in the same audience group.
Both value leakage scenarios can occur at different levels, such as across ad networks, across campaigns within an ad network, and across ad sets and source apps within a campaign. In this post, we will demonstrate the concept of value leakage via examples across ad sets and source app level.
When you’re advertising, if you're not targeting the most granular level, as we explained before, you’re showing your ads to and spending money on literally everyone. Thus, you end up spending some of your budget on an unqualified audience. Even though you might catch your correct target audience in the bunch you’re advertising to, a percentage of people you spend your budget on will not be interested in becoming a user of your app. So though you may have high revenue, you end up with a higher cost per acquisition (CPA).
Let’s say that you understood the importance of having a high level of granularity in defining your target audiences and subdivided your audience carefully. Unfortunately, it doesn’t end there. Each of these divided target audiences, let’s call them ad sets, will behave differently. For example, some ad sets will generate a lifetime value of $10, while another one can go as high as $13. If you apply the same bid you used for the $10 bringing ad set on the $13 bringing ad set you will only be capturing part of the revenue you can generate from the $13 bringing ad set.
When you’re advertising, if you're not targeting the most granular level, as we explained before, you’re showing your ads to and spending money on literally everyone. Thus, you end up spending some of your budget on an unqualified audience. Even though you might catch your correct target audience in the bunch you’re advertising to, a percentage of people you spend your budget on will not be interested in becoming a user of your app. So though you may have high revenue, you end up with a higher cost per acquisition (CPA).
Let’s say that you understood the importance of having a high level of granularity in defining your target audiences and subdivided your audience carefully. Unfortunately, it doesn’t end there. Each of these divided target audiences, let’s call them ad sets, will behave differently. For example, some ad sets will generate a lifetime value of $10, while another one can go as high as $13. If you apply the same bid you used for the $10 bringing ad set on the $13 bringing ad set you will only be capturing part of the revenue you can generate from the $13 bringing ad set.
Therefore, to get the most out of your user acquisition campaigns for your game and avoid value leakage you need to allocate your bids and budget at the most granular level and treat each audience uniquely.
Can automation tools prevent value leakage?
As you might have multiple games you’re promoting on several ad networks, it might become more challenging to optimize towards the most granular level and evaluate the performance of campaigns one by one. This is when finding the right automation tool for optimizing your data and workflow becomes essential. Automation tools save you from the heavy manual work-load and let you and your team focus on analysis and decision making. Having said that, not all automation tools work in the same way, and it’s of grave importance to find the right tool to make sure that you minimize the handiwork and value leakage.
Most automation tools work with rule-based automation where the user defines the rules. Though this would help minimize the manual effort in managing your user acquisition campaigns, it doesn’t enable you to capture the uniqueness of each ad set or source app and define a specific rule for each of them. In essence these tools put all the burden of determining the right rule for each ad set or source app on you, which is nearly practically due to the large number of ad sets and source apps one needs to consider. However, when you work with an AI-based automation tool, such as UAhero, the algorithms work at ad set and source app level granularity, learn the unique characteristics of each one and recommend bid and budget values accordingly .
Optimizing with UAhero to prevent value leakage
UAhero has two options you can choose from to minimize value leakage while optimizing your campaigns. You can either choose to aim for the minimum cost per install (CPI) or for maximizing your return on ad spend (ROAS). While the objectives you set on the platform might change according to your goal, UAhero enables you to implement your strategies to all ad networks.
Optimizing towards Min CPI
Optimizing your campaigns for minimum cost per install means getting the highest number of installs with the budget and target audience set for your campaign. When you set your objective CPI-based, UAhero makes sure that you get the highest number of installs at the lowest cost.
Some ad networks such as Unity Ads, do not have the option to optimize towards CPI in their interface. UAhero resolves complications as this by allocating budget to the better performing, install bringing ad networks in a cross-channel working Optimizer. Algorithms work on the source-app level for Unity Ads to make sure that you’re bidding to get the highest amount of installs. The LTV of every source app might change, which is also taken into account by the algorithms while optimizing your campaign.
Optimizing for Max ROAS
ROAS optimization’s goal is to get you the highest revenue possible by giving bid recommendations more granularly in all ad networks in an Optimizer with this particular goal. All you need to do is to enter the ROAS percentage you’re aiming for and the estimated number of days to be considered for LTV prediction. UAhero will provide you with bid and budget recommendations in no time to hit your ROAS target.
