When doing Telegram user screening, many teams started with manual operations, importing numbers, testing results, and then exporting the data. The process does not seem complicated, but once the data comes in, problems will quickly arise: the processing speed cannot keep up, there will be more repeated operations, and the results will be unstable.
This is why more and more teams are turning to API methods. Not for one more function, but to make the screening step a part of the automatic process rather than a separate operation.
Why did Sieve start to change from a tool to an interface?
When the amount of data is small, manual filtering with tools is acceptable. But when data sources increase, such as advertising leads, channel data, and historical users entering at the same time, manual processing becomes confusing.
Frequently asked questions will appear gradually:
The same batch of data needs to be imported and exported repeatedly
Different people have inconsistent handling standards
Filter results cannot be used in real time
The role of the API is to unify these scattered actions so that the data can be automatically filtered during the flow process instead of stopping for processing.
What exactly is the API filter checking?
Many people will understand the filter number as simply determining the registration status, but in actual use, the API can return much more than this.
The core will involve three levels:
Registration status, used to determine whether the number has been activated for Telegram
Activity status, used to determine whether the account has usage behavior
Account status, used to determine whether it is abnormal or unavailable
The combination of these three dimensions can truly determine whether a user is worth reaching, rather than just looking at whether it exists.
Can registration, activity, and status be checked together?
In the current screening method, multi-dimensional detection has become the mainstream. Although separate detection can be completed, it is inefficient and prone to data inconsistency.
A more practical approach is to return multiple dimension results in one request. In this way, registration identification, activity judgment and status screening can be completed simultaneously on the same batch of data, instead of processing in steps.
The advantage of this method is that the data results are unified and can be directly used in subsequent processes without the need for secondary integration.
What problems do these data solve?
Data of different dimensions correspond to the needs of different links.
The registration status solves the problem of whether it can be reached. Unregistered numbers can be directly eliminated.
Activity determines whether you will see the information. If the account is not used for a long time, it will be difficult to generate feedback even if it is accessed.
Account status is related to stability, and abnormal or risky accounts can be filtered in advance.
When these three layers are used together, the data moves one step further from "usable" to "closer to valid."
What will happen to the process after accessing the API?
Without API, screening is an independent step and needs to be triggered manually. After accessing the API, this step will be embedded in the data process.
Automatic detection when data enters the system
Return results directly with labels
Automatic stratification of data of different qualities
This way, screening is no longer an extra step, but a default part of the process. The data has been sorted since it was entered.
Using Amman to achieve multi-dimensional screening is more straightforward
In actual access, the key is not only whether it can be checked, but whether it can be checked stably and in batches, and the results can be used directly.
Through Amman, batch detection of Telegram numbers can be realized, and registration status, activity status and account status can be returned in one call. This method is more suitable for direct embedding into business systems, such as CRM, community management or automated processes.
The data is filtered when it enters the system, and no additional processing is required later, and can be directly used for reaching or stratifying.
The essence of API filtering is to make the data automatically clean
Many teams will treat the API as a performance improvement tool at first, but after using it, they will find that it is more like a basic capability. As long as the data is filtered as it enters the process, every subsequent step will be smoother.
When registration, activity, and status information can be obtained at once, screening is no longer an independent action, but a part of the entire data process. This change is where the real value of API lies.
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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