Is using custom audiences via CSV files a good strategy for user retention?
I work in the marketing department for a Fintech company. We are just setting up our performance marketing division and are setting up the pixel on our app. While that’s happening, there’s a little strategy I’m working on that’s very easy to do with the pixel in place, so I’m looking for use cases to see if this alternative idea will work or not.
The approach is to upload a segment of our users using CSV lists in custom audiences. Then, we only target that list with offes & deals via these ads. More specifically, we target users who are about to churn, so that they keep coming back.
I’m sharing this as I’ve struggled to find some use cases on this during my research. It would be awesome if you can share your experiences!
The short answer is:
Using custom audiences via CSV files for user retention, particularly for predicting and targeting users about to churn, is an excellent and common starting strategy in performance marketing, especially in a heavily-regulated sector like FinTech.
It works because it allows you to use your rich, first-party data (like transaction frequency or last login date) to deliver highly personalized offers to users who are actively flagged as “at risk” of churning.
However, its main limitation is that the data is only as fresh as your last manual upload, creating a delay that is fatal for real-time churn prevention.
The solution is to transition to an automated, server-side data stream using the Facebook Conversions API (CAPI) paired with Google Tag Manager (GTM) and a server solution like Stape or Google Cloud Platform, which provides real-time targeting accuracy cheaply and reliably.
The long answer is:
Your strategy of using segmented CSV lists to create custom audiences for retention offers is a smart move, and it’s absolutely a valid use case for performance marketing teams.
We’ve seen this work very well for clients, especially in FinTech, where you have excellent first-party data on user activity – things like deposit frequency, account balance trends, or features used – which you can use to create a “churn score.”
You then export the list of users with a high churn score and upload it to your ad platform.
This allows you to serve highly relevant, timely, and often personalized ads with exclusive offers, like a temporary boost to interest rates or a special cashback deal, to the exact audience segment that needs to be re-engaged.
This type of retention campaign is generally much more cost-effective than pure acquisition.
However, the major challenge you’ll run into is the issue of data latency and matching rate.
First, your list of “about to churn” users is only current up to the moment you manually exported the CSV.
If a user was on the list this morning and then came back and completed a key transaction this afternoon, you’re still wasting ad spend showing them a retention offer they no longer need.
Second, CSV uploads rely on Meta or Google matching the hashed identifiers in your list (like email, phone number, etc.) to their user base, and the match rate is often imperfect, meaning a significant percentage of your valuable list won’t be targetable.
To solve both the latency and matching rate issues, your best, most scalable, and cheapest long-term solution is to move to server-side event tracking using the Facebook Conversions API (CAPI).
Instead of relying on manual CSV uploads, you’d use a server-side instance of Google Tag Manager (GTM), hosted cheaply on a service like Stape or your own Google Cloud Platform environment.
The process works like this: your app’s server sends real-time user events, not just website events but custom, high-value FinTech events like money_transfer_completed, account_balance_check, or even a custom event you define as churn_risk_flag, directly to GTM Server-Side.
GTM then formats this event data, adds high-quality customer identifiers (like hashed email) in a privacy-safe way, and sends it directly to the CAPI endpoint.
This bypasses the browser entirely, dramatically improving data quality and matching accuracy, and most importantly, it’s real-time.
You can now build highly accurate custom audiences within the ad platform based on these live server events, like “Users with churn_risk_flag = true and last money_transfer_completed > 30 days ago.”
This automated, server-to-server data flow is significantly cheaper than relying on enterprise-level Customer Data Platforms (CDPs) and offers the most reliable foundation for a data-driven performance marketing division.
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