How to filter out real active users from Kakao account

Kakao has a high penetration rate in the Korean market, especially in the local community, retail, finance and education fields. After many teams have accumulated a certain number of account lists, they often directly enter the group sending or private chat stage, but ignore the differences in account active structures. Whether the account is actually used and whether it has been active recently will directly affect communication efficiency and conversion results.

If the goal is to screen out real active users, rather than simply expanding the reach, then the screening logic must be more refined.

1. Understand what a real active account is

A real active account is not as simple as successful registration. The registration status can only indicate the existence of the account, but cannot indicate the frequency of use.

To judge authenticity and activity, we can start from three dimensions.

Recent login behavior of the account.

Is the frequency of interaction stable?

Whether there is an abnormal or silent state.

If an account has not been logged in for a long time, it will be difficult to form effective communication even if the registration is successful. Active identification should be used as a core dimension when screening, rather than as additional conditions.

2. Why is manual screening inefficient?

Many teams use small-scale testing methods, such as manually adding or sending test messages. However, the efficiency of this method will decrease rapidly after the data scale increases.

When the list reaches thousands or even tens of thousands, manual testing takes a long time and is difficult to fully cover. More importantly, manual methods cannot form structured tags, and subsequent hierarchical management will also become confusing.

Therefore, batch identification becomes a necessary step.

3. Process design of batch active identification

To screen real active users, you can follow the following process.

The first step is to unify the number format and remove duplicates.

The second step is to identify the registration status in batches.

The third step is to further detect the account activity.

The fourth step is to perform label classification based on the detection results.

Through Digital Planet's activity cycle detection capability, account activity tags can be output in batches and high-activity and low-activity accounts can be distinguished. After screening, only highly active accounts are imported into the private chat or marketing system, which can significantly improve the success rate of communication.

In an actual project, a local service team in South Korea reduced the effective list to approximately30%, but the overall response rate more than doubled. This shows that structural optimization is more important than quantitative expansion.

4. Changes in operating methods after screening

After the list is screened, the operational rhythm should also be adjusted simultaneously.

Highly active users can directly engage in focused private chats.

Moderately active users are suitable for batch testing.

Low active accounts will be suspended or re-evaluated.

This hierarchical management method can avoid resource waste and allow customer service time to be more concentrated.

5. Long-term maintenance mechanism

Active status is not fixed. The frequency of account usage will change over time. Therefore, it is recommended to review the historical list regularly.

Digital Planet supports periodic batch testing, which can help teams continuously update account status labels. Through regular updates, you can keep the data pool healthy and avoid the gradual increase in the proportion of silent accounts.

6. The relationship between screening accuracy and conversion

existIn Kakao marketing, account quality determines communication efficiency. Real, active accounts are easier to respond to and easier to build a trusting relationship with. The more precise the filtering, the smoother the conversion path.

Screening is not an extra step but a pre-marketing structure. Only when the data foundation is stable can the promotion rhythm truly improve efficiency.

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