When they are new to WhatsApp customer acquisition, many people will focus on the number of people added, but they will soon discover a situation: there are a lot of numbers added, but not many people actually respond. The problem is often not the communication behind the scenes, but the lack of screening when selecting people up front.
If no distinction is made, the original data will be mixed with many low-active or even invalid users. Even if these numbers can be added, it will be difficult to generate interaction. Compared with complex screening methods, avatars are actually a very intuitive entry point for judgment, especially suitable for novices to get started quickly.
From the perspective of actual use, doing a round of avatar screening first can significantly reduce ineffective communication and focus on people who are more likely to respond.
Why do avatars have reference value?
In WhatsApp, avatars usually reflect whether the user is using the account normally. Although it cannot be completely equated with activity, in most cases, accounts with avatars are closer to real users.
It can be briefly divided into several situations:
Accounts with real-person avatars usually mean that the user has made personalized settings during use. Such accounts are more likely to generate interactions and are suitable for priority processing.
Accounts that use default avatars often have no further information after registration. Some users may still use them, but the overall activity is not high.
Accounts with blurred or obviously abnormal avatars have a certain probability of being low-quality data. For example, accounts that have not been used for a long time or have been used abnormally should reduce investment accordingly.
Through the filtering of avatars, a batch of data can be quickly divided into hierarchies, without complicated judgments, and a round of filtering can be done first.
How to combine avatar screening with number screening
Just looking at the profile picture is not enough. It is recommended to combine it with the basic screen number, so that the effect will be more stable.
A smoother process could be:
First, perform number detection to remove obviously invalid numbers to ensure that the data is usable.
Then filter the avatars, and filter out some of the accounts that have no avatars or obviously low-quality avatars.
Finally, look at the basic information, such as whether the nickname is normal and whether the account looks like a real user.
After processing this way, the data will be cleaner from the beginning instead of trying again later.
In actual operation, you can directly use Amman to do this set of processes. First run the number for detection, and then process it together with the avatar filter. There is no need to operate in multiple steps, and the overall process will be much simpler.
What practical problems can be solved by using avatar screening?
After adding avatar screening to the process, the most direct change is the reduction of ineffective communication.
For example, if a batch of numbers is not filtered, there will be many accounts without avatars or with low activity mixed in. These users will basically have no feedback, but it is difficult to tell at a glance during actual use.
After filtering through avatars, these users can be eliminated first. The remaining data will be more concentrated, and subsequent communications will be more likely to respond.
Another change is that the rhythm is clearer. Filtered data does not require repeated trial and error, and you can quickly find the part with feedback and gradually adjust the direction.
Different avatar types, corresponding usage suggestions
When actually screening, you can simply make a distinction, which will make it easier to get started.
Accounts with real-person avatars can be prioritized, and these users are usually more likely to interact.
The account with the default avatar can be used as the second layer and used appropriately, but it is not recommended to invest heavily.
Accounts with unusual avatars or no avatars can be put away first to avoid wasting time on this part of the data.
This simple layering does not require complex analysis, but it will be more effective in actual use.
Start filtering with avatars to make it easier to find target groups
For those who are new to WhatsApp, the filtering method does not need to be complicated at the beginning. The avatar is a relatively intuitive entrance that can help quickly judge the quality of some users.
When this layer gradually stabilizes, combined with other filtering conditions, such as region, activity, etc., the data will become closer and closer to the target group.
Screening itself is a gradual optimization process. Starting from simple judgments is easier to implement than pursuing complex rules from the beginning.
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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