When acquiring customers in foreign trade, many teams will obtain customer information through LinkedIn and then use email to reach them. However, in actual operation, we often encounter a problem: the email is sent, but there is no response, or even some emails are directly returned.
Many people will optimize email content, but the effect is still limited. The problem often lies in the previous step, where the mailbox data itself is not processed properly.
Why does mailbox data also need to be filtered?
Compared with social accounts, email is more likely to be ignored. Many teams use the email directly after getting it, but there are usually many problems with the original data.
Common situations include:
- Email address does not exist or is spelled incorrectly
- The mailbox cannot receive emails normally
- Email has not been used for a long time
If verification is not performed, the data will directly enter the sending process, resulting in a decrease in the overall reach effect.
LinkedIn Customer Data FAQ
As a data source, LinkedIn itself has uneven information quality. Even among customers in the same industry, there will be significant differences.
Frequently asked questions include:
- Incomplete information and lack of valid contact information
- The email source is unstable and inaccurate.
- Duplicate data exists for the same customer
If not cleaned, these problems will continue to amplify in subsequent use.
Where email verification fits in the process
A more reasonable approach is to put the email verification before the data is used, instead of dealing with the problem after sending it.
It can be understood as:
After obtaining customer data
Check the email validity first
Then enter the email contact process
This can filter out invalid data in advance and reduce sending waste.
What can email validity testing do?
Through verification, you can determine whether a mailbox meets the basic conditions for use.
Mainly can be identified:
- Does the email address actually exist?
- Is it capable of receiving
- Is it an abnormal or unavailable state?
Through this information, the original data can be divided into "available" and "unavailable" parts.
How to turn mailbox data into usable resources
After the verification is completed, it is not recommended to use them directly and uniformly, but to do simple layering.
Can be divided into:
- Valid email, priority access
- Suspicious email address as a supplementary test
- Invalid emails will be deleted directly.
This structure allows subsequent sending to be more targeted rather than blindly covering it.
Data cleaning takes priority over content optimization
Many teams will focus on the content of the email, but if the email itself is not available, no matter how good the content is, it will not be seen.
Instead of constantly optimizing email templates, it’s better to clean up the data first. Only when the mailbox meets the basic conditions for use, can the content have room to play.
Use Amman to optimize multi-channel data more efficiently
In actual operation, email data usually does not exist alone, but is used together with numbers and social accounts. If handled separately, not only the process will be complicated, but also inconsistencies will easily occur.
Through Amman, multi-channel data can be processed in a unified manner. It can not only detect numbers, but also assist in organizing the data structure obtained by LinkedIn to keep email addresses consistent with other data.
In this way, the data has been basically cleaned before entering the contact stage, instead of repeated adjustments later.
The data must be clean first before it can be meaningfully accessed
In foreign trade customer acquisition, email is only one of the ways to reach customers, but data quality is always the foundation. If the email is not verified, sending more will only increase the cost.
After the data has been cleaned and filtered, access will be more stable and feedback will be easier to judge. Instead of constantly adjusting the results, it is better to sort out the data clearly at the source.
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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