Beyond the basics — like segmentation and programmatic technology — there’s a lot that goes into crafting a successful mobile retargeting campaign. The effectiveness of your retargeting efforts often hinges on the quality of your data. Implementing strategies to maintain “clean data,” especially when working with multiple mobile retargeting partners can make or break your campaigns.
In this blog, learn the data best practices for maximizing your mobile app retargeting campaigns. These strategies are also key for creating a clean apples-to-apples testing environment to incrementally improve your campaigns with uplift testing.
Before we dive into our best practices, let's clarify what we mean by "clean" data. Clean data refers to accurate, reliable, and well-organized information that is free from errors, inconsistencies, or biases. In the context of retargeting, clean data is crucial for making informed decisions and drawing actionable insights from your campaigns.
Setting up practices that ensure clean data is especially important when working with multiple mobile retargeting partners. Realistically, most app developers work with two or three to half a dozen or more retargeting DSPs. This can drastically affect the size of their potential audience depending on their data collection and testing methods. In our experience, there are two basic data approaches app developers can take when working with multiple retargeting DSPs.
One approach is to run campaigns and incrementality tests with one vendor at a time. This ensures that only the behavior of users that have not been touched by ads other than those served by the unrestricted vendor are included in the campaign results. One potential risk of this approach is the possibility of changing your bid strategy based on a restricted audience size.
For example, if you restrict your campaign to only users that have not been “touched” by any vendor except for the one you are isolating for, you might restrict your audience size and have to bid higher to win users and stay competitive. Bidding higher might, in turn, affect your budget and ROAS down the line.
An alternative approach to testing vendors one at a time is allowing all of your partners to run campaigns at the same time and isolating the performance of one vendor by scrubbing the results post-campaign. With this option, you would run a campaign for four weeks or more and scrub the bid logs of any users that were served ads by competitor partners.
Whether you decide to run your campaigns with one partner at a time or scrub your results post-campaign to isolate for different partners’ performance should depend on your app’s needs and goals. There can be a lot of “noise” that biases your campaign results. Controlling for this “noisy data” is different for every app. For example, if your app has a high number of active users and is working with just a few retargeting partners, then running campaigns with one partner at a time might be an appropriate approach. On the other hand, if your app has a low number of MAU (monthly active users) and works with many retargeting DSPs, then the best strategy might be scrubbing bid logs of users after the fact.
Overall, when deciding your data methodology, it’s important to choose a data logic that ensures you have a sufficient amount of users (an audience size of at least 1,000 users/potential payers).
To ensure your retargeting efforts are effective and yield meaningful insights, consider the following best practices.
Data cleaning is a foundational step in the process and arguably the most critical. It involves ensuring that your data is accurate, consistent, and free from errors. Given its complexity, here are some key considerations:
Understanding the differences between passing back real-time versus static data is also crucial:
At YouAppi, we recommend passing real time revenue data to your partner so they can make optimizations to your campaign immediately.
Incrementality testing is at the core of evaluating retargeting campaigns. It’s crucial to make sure the data in your control group is cleaned as much as possible prior to running your uplift test. This means fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within the dataset. As we’ve written in the past, undertaking a ghost bid incrementality testing methodology is one way to ensure your data is not biased by “noise”.
To maximize the effectiveness of your retargeting campaigns, consider these high-level data practices:
For meaningful results, as mentioned above, ensure you have a sufficient user base of at least 1,000 potential payers. Smaller sample sizes, such as 500-600 users, can yield less reliable insights. Expanding your demand to reach more potential users is often the key to successful retargeting.
Oftentimes your data team, even in cases when they are not a part of the marketing team, play a pivotal role in making data strategy-related decisions. Be prepared to make inroads with your data team if their goals restrict your audience potential and inhibit overall marketing KPIs.
Crafting an effective data strategy is key to a successful mobile retargeting campaign. The quality of your data plays a pivotal role in the effectiveness of your retargeting efforts.