On a communication-oriented platform like Line, whether an account exists does not mean it can be used. Many numbers seem normal, but coupled with the lack of response afterwards, the problem is usually not with the communication method, but rather with the failure to handle it in the previous screening stage.
Clarifying the filter number step can significantly reduce invalid additions and make the data closer to real and communicable users.
The first step is to do number detection and sort out the basic data.
The original number is usually mixed with some unusable data, such as deactivated numbers, abnormal numbers, or numbers that cannot receive messages normally. If this part is not handled in advance, there will be basically no feedback for subsequent operations.
Doing a round of number detection first to filter out obviously unavailable data can make subsequent screening more effective.
The focus of this layer is not on refinement, but on transforming the data from a mixed state into a usable state.
The second step is to filter out the numbers that are using Line
If the number detection passes, it does not mean that the user is using Line. Although some numbers have been registered, they are no longer active. This type of data has very limited effect in actual use.
When filtering, you can focus on whether there are traces of use, such as whether the account information is complete, whether there are normal settings, etc. This type of account is closer to real users.
Filtering out numbers that have not been used for a long time can reduce a lot of invalid additions.
The third step is to judge user quality through basic information.
In Line, avatar and nickname can be used as a simple criterion.
Accounts with avatars and normal information are usually closer to real users; accounts without avatars or with abnormal information have relatively low value.
This layer does not require complex analysis, only simple distinctions are needed, and the data can be screened again.
Regional unification makes data easier to use
Line’s users also have obvious geographical distribution. If the data sources are complex, there will be problems with communication in subsequent uses.
For example, if the target is in a certain country, but the filtered data is distributed in multiple regions, language and custom differences will be encountered when using it, which will affect the overall effect.
Try to unify the regions during the screening stage to make the data more centralized and easier to use.
It is recommended that the sieve number sequence be fixed
In actual operation, it will be more stable if the screening process is fixed.
It can be processed in this order:
First do the number detection, then screen the usage status, then look at the basic information, and finally unify the region.
After the order is stabilized, the quality of each batch of data will be closer and no repeated adjustments will be needed.
Using tools to handle them together will save time.
If data is processed manually, efficiency problems are likely to occur as the volume increases, and standards are not easily unified.
Through Amman, the steps of number detection, usage screening, and information screening can be completed together. The data can be sorted into a relatively clean state at the front end, reducing subsequent repeated screening.
The clearer the screen number is, the easier it will be later
The essence of screening is to reduce invalid operations. If the data is cleaner at the beginning, subsequent additions and communication will be smoother.
For those who have just started doing Line screening, there is no need to do complex screening at the beginning. Most of the problems can be solved by doing the basic steps well.
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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