For the same batch of Telegram data, some people have not finished screening it for two days, while others can get a batch of available users in one or two hours. The difference basically lies in the screening method. Efficiency not only saves time, but also directly affects whether the data can be used. In actual use, it is not difficult to increase the screening efficiency to 30% to 70%. The key lies in whether the screening process is reasonable and whether the necessary steps are put in the front.
A simple comparison case
The same batch of about 50,000 Telegram data, two different processing methods, the results are very different:
Method 1: Manual + simple screening
- Determine account status one by one
- Just check if you are online
- There is no unified screening criteria
turn out:
The processing time is close to 2 days. After screening, the data is still mixed, and the actual usable ratio is not high.
Method 2: Tools + Structured Filtering
- Screen out abnormal accounts in batches first
- Then filter by activity
- Finally, further filter based on behavior
turn out:
The overall processing time is shortened to less than half a day, and the proportion of available data is significantly increased.
In this comparison, screening efficiency and data quality have been improved at the same time, basically reaching more than 30%, and in some scenarios it can even approach 70%.
Batch processing capability is the starting point of efficiency
Once the amount of Telegram data increases, manual screening is almost impossible.
If we still stay at the stage of judgment one by one, the time cost will be very high, and it will be difficult to unify standards.
Through batch processing of screening tools, large-scale data can be directly processed through a round of basic screening in a short time.
What this layer solves is not the accuracy problem, but the upper limit of efficiency.
Filtering by multiple conditions saves time compared to a single condition
Filtering with only one condition may seem simple, but it often leads to more rework.
For example, if you only look at the online status, the filtered data will still be mixed with a large number of invalid users, and you will need to filter it again later.
A more reasonable way is to combine the conditions at the beginning, for example:
- Available status + active behavior
- Activity + group participation
- Information integrity + usage records
After such a screening is completed, the data is already relatively close to the target and does not need to be processed repeatedly.
Data cleaning will directly affect subsequent efficiency
Many screening processes are slowed down not because of the screening itself, but because the data itself is not clean.
Common situations include:
- Duplicate data
- Abnormal account mixed in
- The proportion of invalid accounts is too high
If these issues are not dealt with in advance, every subsequent step will be slowed down.
Cleaning before screening and sorting out the data structure can significantly reduce the time for subsequent operations.
Using tools to combine the processes will make it more stable
If screening is carried out in multiple steps, it is easy to have unstable efficiency problems, such as repeated switching of tools in the middle, repeated processing of data, etc.
It will be smoother to handle basic screening, active screening, and behavioral screening together.
A one-stop screening platform like Amman can complete in the same process:
- Batch filter account status
- Filter low active users
- Filter by behavior and condition combinations
Processed in this way, it not only saves time, but also reduces repeated operations.
Once the screening method is fixed, the efficiency will continue to improve
The improvement of screening efficiency is not a one-time event, but can be accumulated continuously.
When the screening process is gradually fixed, the processing time of each batch of data will be shortened and the results will be more stable.
From the perspective of actual use, the efficiency improvement does not come from a single point, but from whether the entire screening process is clear. As long as the order is reasonable and the conditions are clear, data processing speed and availability will be improved simultaneously.
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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