Before overseas promotion, many teams will focus on advertising materials, delivery structure and budget allocation, but a more basic link is often ignored: whether the user data entering the delivery link is normal.
If there are abnormalities in the account itself, no matter how detailed the delivery is, it will be difficult to stabilize the back-end conversion. Platforms such as Facebook have a large amount of data, but the differences in user status are also obvious. Screening for abnormal accounts in advance is a step to make the delivery more controllable.
What is an abnormal account?
In actual data, abnormal accounts are not a single type, but a collection of multiple states.
Common ones include:
- Accounts that are not actually used or have been inactive for a long time
- Accounts with incomplete information or abnormal behavior
- Accounts with unstable status and abnormal usage environment
These accounts "exist" in the system, but in actual marketing, it is difficult to generate effective interactions.
Why screening is necessary before release
If the abnormal account directly enters the delivery link, it will have several direct impacts.
The first is data feedback distortion. After the advertisement is placed, the system will optimize based on user behavior, but if the users themselves do not have real behavior, the feedback data will lose its reference value.
The second is the decrease in conversion efficiency. Even if the click or interaction exists, it cannot enter the real communication or purchase process.
The third is that resources are dispersed. The budget will be consumed on low-quality users, while high-quality users will not be covered intensively.
Therefore, the purpose of screening is not to reduce data, but to make the data closer to real users.
A screening process that can be implemented
In actual operation, screening can be broken down into several steps instead of making a one-time judgment.
First import the original data and enter it into the data pool uniformly.
Then do a basic test to confirm whether the account exists normally.
Then filter the activity to distinguish users who are using it from those who are not.
Then identify abnormal status and filter out unstable accounts
Through this layering process, the original data can be gradually compressed into a more stable part.
How to use the data after screening
After the screening is completed, it is not recommended to use all remaining data uniformly, but to perform simple stratification.
High-quality accounts will be prioritized for advertising or key exposure
Medium quality accounts can be used as tests or supplements
Low-quality or marginal accounts reduce investment
This allows resources to be concentrated rather than spread evenly.
Amman’s role in the screening process
In large-scale data processing, manual screening is difficult to ensure efficiency and consistency. Through Amman, batch inspection can be completed before the data is put into release.
Can be achieved:
- Determine account status in batches
- Identify activity and screen out real users
- Filter abnormal or unstable accounts
- Output structured tagged data
In this way, screening is no longer an independent action, but becomes part of data processing.
In actual use, you can first use Amman to process the data, and then import the filtered results into the delivery system, so that the advertisement can face a cleaner user group from the beginning.
API access makes screening a fixed process
When data sources continue to increase, it is easy for standards inconsistencies to arise if each process is handled manually. Through Amman API, screening capabilities can be directly connected to the system.
Implementation methods include:
- Data import automatic detection
- Automatically mark account status
- Automatically shunt data of different quality
In this way, each batch of data has completed basic processing before being released, instead of re-screening later.
Data processing comes first, then delivery is meaningful
In Facebook promotion, many optimization actions are concentrated in the delivery stage, but if there are problems with the data itself, it is difficult for these optimizations to produce stable effects.
Putting abnormal account screening at the front will make it easier to judge subsequent placements and increase the volume. The cleaner the data, the more controllable the delivery.
Amman is the world's leading number screening platform, providing global customers with batch number screening and testing services covering 236 countries. The platform currently supports more than 40 mainstream social networking and applications, including WhatsApp, Line, Twitter, Facebook, Instagram, LinkedIn, Viber, Zalo, Binance, Signal, etc., adapting to the needs of multiple scenarios.
The main core functions cover multi-dimensional precise screening such as activation, activity, interaction, gender, avatar, age, online, accuracy, empty account, mobile phone device, etc., and can flexibly meet the needs of different users. Its core advantage is to integrate global mainstream social and application resources to provide users with one-stop, real-time and efficient number precision screening services, helping customers achieve global digital layout.
It is a common choice for all professional teams to complete rational screening before actually reaching users.
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