It is not difficult to obtain registered YouTube users. What is difficult is filtering out the ones that can be used. Raw data is often mixed with a large number of empty shell accounts, low-activity accounts and non-target groups. If no screening is done, subsequent use will basically depend on luck.
After the screening is done solidly, the data differences will be very obvious. In actual comparisons, for the same batch of registered users, the difference in effective feedback before and after screening is 2 to 3 times a common result. Let’s talk directly about how to screen, focusing on the steps that can be implemented.
Filter empty shell accounts first to avoid invalid consumption
Among registered YouTube users, some accounts exist but have almost no traces of use, such as:
- No avatar
- No subscription record
- No viewing behavior
This type of account has essentially no use value.
The first step is to filter this part of the data to remove obvious "empty accounts" to reduce subsequent invalid contacts.
Looking at behavior records is more important than looking at registrations
Registration is just the starting point, the real value is the usage behavior.
Prioritize attention when filtering:
- Is there any viewing record?
- Whether there is a like or comment behavior
- Do you have a subscription channel?
Accounts with good behavior indicate that users are actually using the platform, and such users are more likely to generate feedback.
Accounts without any behavior, no matter how many there are, are difficult to convert.
The region must match the content direction
YouTube content is highly bound to users. If the region does not match, usage efficiency will be significantly reduced.
For example:
- Create English content and prioritize users from English-speaking areas.
- To produce local content, you need to screen local users.
- When marketing in a certain country, avoid mixing data from other regions.
If the region does not match, even if the account is active, it will be difficult to generate effective interaction.
When filtering, it is recommended to directly bring the regional conditions instead of splitting them later.
Activity filtering determines reach effects
Whether the user is active will directly affect whether the information is seen.
It can be judged by the frequency of behavior:
- Have you had any recent viewing behavior?
- Is there any continuous interaction record?
- Whether it will not be used for a long time
Screening out low-active users can significantly improve overall feedback.
It is recommended to fix the data screening process
Filtering can be performed in a fixed order:
- Filter out empty shell accounts first
- Rescreen behavior records
- Do regional matching again
- Last screen activity
After the order is stabilized, the quality of each batch of data will be closer and the use will be more controllable.
When using Amman, unified screening will be more efficient.
In actual operation, it will save more time to complete these steps in one process.
Shi'anman can directly complete multi-layer screening:
- Batch filter empty shell accounts and abnormal data
- Filter real users by behavior records
- Support regional filtering to ensure data matching
- Combined with activity screening to increase the effective ratio
Through one round of screening, the original data can be compressed into a more usable batch, reducing subsequent repeated processing.
After filtering the data, the difference will be very intuitive.
The data before filtering is usually large in quantity but mixed; the data after filtering may be reduced in quantity but more concentrated in structure.
The actual performance is:
- Reach is more stable
- Interaction is more focused
- Effective feedback increased significantly
The same usage method, different data quality, the results will directly widen the gap. Once the screening step is done well, there will be a foundation for subsequent operations.
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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