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.

The Importance of "Clean" Data 

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.

Data Collection When Working with Multiple App Retargeting Partners

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.

Blocking Competitors During Testing

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.

Cleaning Data Post-Campaign

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.

Choosing the Right Data Collection Approach

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).

Retargeting Data Best Practices

To ensure your retargeting efforts are effective and yield meaningful insights, consider the following best practices.

Prioritize Data Cleaning

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:

  • In-House Logic: Determine what data processing logic your development team employs. Understanding this internal logic is crucial for aligning your data practices.
  • Source of Truth: Establish whether your internal data or the data provided by your Mobile Measurement Partner (MMP) is your primary source of truth. This decision shapes your data management approach.
  • Control Group Setup: Decide how you will configure your control group. Will you exclude a control group for all retargeting partners, or will you establish separate control groups for each partner?
  • Education: Educate your team on the importance of data cleaning. Address questions like: Why use real-time data instead of live data or static data? Why not remove 50% of data instead of just 20%?

Pass Real Time Revenue Data

Understanding the differences between passing back real-time versus static data is also crucial:

  • Real-Time Data: This is data that flows back from postbacks in real-time. It provides immediate insights into user interactions.
  • Developer-provided Data: App developers themselves can provide their partners with data in the form of static files or data that’s been refreshed on their end. You can choose to use data provided by your partners by refreshing segmentation or receiving fixed files.

At YouAppi, we recommend passing real time revenue data to your partner so they can make optimizations to your campaign immediately.

Ensure Data Quality for Incrementality Testing 

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”.

  • High-Level Approach: Whether you run the test in-house or with partners, keep data cleanliness in mind. Even if you opt for a realistic testing environment, clean data is vital.
  • Scrubbing: After the test, consider scrubbing users who were exposed to ads from multiple retargeting vendors. This ensures that you evaluate only untouched users.
  • Learning Environment: Decide whether you want to learn from a "clean" testing environment or a realistic one. In most cases, developers work with multiple retargeting vendors, making a realistic approach more representative of the actual scenario.

Expand Your Audience Potential

To maximize the effectiveness of your retargeting campaigns, consider these high-level data practices:

  • Audience Size: Expand your audience segmentation as much as possible. A larger audience size allows for a more comprehensive and competitive retargeting strategy.
  • Open Traffic: We recommend opening traffic to all retargeting vendors and allowing healthy competition. Limiting traffic to specific vendors can restrict your audience size, potentially affecting bidding and ROAS.
  • Noise Reduction: Assess the noise in your data based on factors like the number of partners, active users, and your internal logic. Tailor your data practices to minimize noise and align with your goals.

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.

A Note on Data Team Decision Makers

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.

  • "Clean" data refers to accurate, reliable, and well-organized information that is free from errors, inconsistencies, or biases. It is crucial for making informed decisions and drawing actionable insights from your campaigns.
  • When working with multiple mobile retargeting partners, there are two main data approaches:
    • Scrubbing Touched Users: Send bid logs of the control and treatment groups to partners after running campaigns. Partners can then remove users touched by competitors, allowing for a fresh analysis.
    • One Vendor at a Time: Run campaigns and incrementality tests with one retargeting partner at a time, temporarily blocking other vendors from running campaigns. This approach provides clarity on the impact of each partner.
  • Data collection methods should align with your app's audience size, the number of retargeting partners, and your internal logic. Ensuring a sufficient user base of at least 1,000 users/potential payers is crucial.