Many teams get stuck in a misunderstanding when working on data: they think that "having a number" means "having a customer." So we generate a large number of mobile phone numbers, import them into the system, and start mass sending. However, the result is often a lot of sending, few replies, or even no feedback at all.
The problem is not generation, but filtering. Generation is just the entrance, and filtering determines whether the data is valuable.
Let’s talk about reality first: most of the generated numbers cannot be used.
Whether it is rule generation, number segment calculation, or batch tool generation, you will eventually face a problem:
- Some numbers don’t exist at all
- Some of them don’t have WhatsApp enabled
- Some parts will not be used for a long time
- Some are abnormal or risky accounts
If you use it directly without filtering, it is basically a waste of time and resources on invalid data.
So the first step is not "how to use", but how to sift it clean .
Step 1: Empty number filtering (must be done)
This is the most basic but most critical step.
Through detection, you can filter out:
- non-existent number
- error number
- Unreachable data
This layer usually directly filters out 20%–50% of the data. If this step is not done, all subsequent actions will be delayed.
When used, Amman can directly complete this layer of batch testing, support large-scale number screening at one time, and eliminate invalid data in advance to avoid entering the subsequent process.
Step 2: WhatsApp activation detection
After empty number filtering, the next step is to determine whether this number has WhatsApp enabled.
The meaning of this layer is:
- Confirm whether access conditions are met
- Avoid sending to "people who can't see the message"
Through Amman, you can batch identify whether numbers have been activated for WhatsApp and output clear results. You don’t need to test one by one, the system will directly give you the available data.
After this layer is completed, the data is considered "usable".
Step Three: Activity Screening (Core Step)
Many teams stop at this point, but this is where the real impact is.
because:
- Although some users have activated the service, they do not use it for a long time.
- Some accounts occasionally go online and the response is unstable
- Only some users are truly active
If not distinguished, the reach effect will be very unstable.
Through Amman, the user's active status can be further identified and the data can be divided into:
- Highly active (priority used)
- Medium active (test use)
- Low activity (reduce investment)
This step directly determines the subsequent response rate.
Step 4: Filtering exceptions and risk numbers
In actual use, there is another type of data that must be processed separately, that is, abnormal accounts.
Such numbers may:
- Unstable status
- There are risks
- Abnormal usage behavior
If not filtered in advance, it may affect the sending success rate and even drag down the overall account performance.
During the screening process, Amman can assist in identifying abnormal conditions, so that such data can be isolated before being accessed, instead of being mixed into normal data.
Step 5: Data layering and reuse
After the screening is completed, it is not recommended to use all the data uniformly, but to do simple stratification.
Can be used in the following ways:
- Highly active users → focus on reaching and giving priority to conversion
- Medium active users → small batch testing
- Low active users → postpone or reduce usage
When Amman outputs results, it can directly add labels so that the data has a natural hierarchical structure instead of manually sorting it out later.
API turns screening into an "automatic process"
If you only occasionally filter a batch of data, manual operation is still acceptable. But once you enter the continuous customer acquisition phase, you must automate it.
Through the Amman API, you can achieve:
- Data import automatic detection
- Automatically return to the activated + active + status label
- Automatically shunt to different data pools
In this way your process will become:
Generate numbers→automatic filtering→automatic stratification→use directly
Instead of doing it manually over and over again.
Why do so many people fail to achieve results?
Not because there is no data, but because:
- No empty number filtering
- No active filtering
- No layering used
- No process is formed
If any link is missing, the results will be unstable.
Generation is only 1%, screening determines 90%
Many people focus on how to generate more numbers, but what really determines the effect is how many "usable people" are left after screening.
When passing through, Amman handles the layers of empty numbers, activated numbers, active numbers, and exceptions at once. What you get is not a bunch of numbers, but a group of users who can truly be reached and have the opportunity to respond .
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.
Editor abcheck has a lot of experience, welcome to communicate with me, click to contact @Tg8189