How to clean Korean Kakao user data: real accounts, age groups, and data tags are processed separately to make it more efficient

Made in Korean marketWhen Kakao acquires customers, many teams will encounter a problem: the amount of data is large, but the proportion that is actually usable is not high. The reason is often not that the channel is wrong, but that the data has not been effectively cleaned before entering the system.

especially inIn platforms with a more social nature like Kakao, the quality of users varies greatly. If "real account number, age group, information tag" are mixed together, it will be easy to filter out distortion, which will ultimately affect the reach efficiency and conversion results.

A more practical way is to separate these dimensions and process them in layers.

WhyKakao data cannot be “screened in one step”

Many teams are used to judging multiple conditions at the same time in one step, such as whether the account is authentic, the age group, and the completeness of the information. But the problem with this is:

l The judgment logic of different dimensions is completely different

l Easy to interfere with each other

l The screening criteria are unclear and the results cannot be reused

For example, an account profile may be complete, but if it is not available, the information is meaningless. Conversely, if a real account is not used for a long time, it is difficult to judge the value based on age alone.

Therefore, split dimension processing is more stable than one-time filtering.

The first level: first confirm whether the account is real and available

The first step in any cleaning action should be to confirm the basic status of the account.

This layer mainly solves several problems:

l Has it been activated?Kakao account

l Is it a real user?

l Whether there is an abnormality or risk status

The goal of this step is toAll "unusable data" are filtered out.

If this layer is not done well, all subsequent label judgments will be based on errors.

Second level: age group determines communication methods and conversion paths

In the Korean market, age group has a very obvious impact on communication methods.

A basic division can be made:

l 25-35 years old: More accepting of quick communication and new product trials

l 35-45 years old: Pay more attention to product value and stability

l Over 45 years old: Pay more attention to trust and brand background

The function of this layer is not to screen out users, but to determine"How to communicate".

If the age group is ignored in the screening stage, there will often be a problem of mismatch in speaking skills in subsequent contacts.

The third layer: Data tags are used to assist in judging account quality

Data tag belongs to"Supplementary dimensions" commonly include:

l Is there an avatar?

l Is the nickname normal?

l Is the information complete?

l Are there any traces of long-term use?

This information is of little significance when viewed alone, but when combined with the first two layers, it can improve the accuracy of judgment.

For example:

l real account+ Have avatar + Complete information → Closer to long-term users

l real account+ No avatar + Missing information → Careful judgment is required

The role of this layer is to further refine user quality.

Different industries have different label priorities.

Different businesses have different degrees of dependence on labels.

E-commerce projects

Pay more attention to activity and data completeness because continuous interaction is required

Financial projects

Focus more on age group and risk status as it relates to conversion quality

local service

More emphasis on reachability and region matching because of the need for quick response

Therefore, labels are not used in a fixed manner, but the order must be adjusted according to the business.

A more reasonable set of cleaning sequences

If the wholeKakao data cleaning process is standardized, you can refer to this sequence:

Step 1: Screen out unavailable accounts

Step 2: Make basic groupings by age group

Step 3: Use data tags for further filtering

Step 4: Sort by activity or usage traces

Step 5: Export data with different priorities

This order ensures that each level of judgment has a clear role, rather than repeated screening.

Common misunderstanding: over-reliance on a certain label

In actual operation, several errors often occur:

l Only look at age, not whether the account is available

l Only look at data completeness, not activity status

l Overlaying all tags at once reduces filtering efficiency

The essence of these problems is that there is no distinction between label levels.

Really effective screening is"Judgment in layers" rather than "overlaying everything".

How to integrate the cleaning process into daily operations

A more efficient way is to fix this set of cleaning logic instead of re-judging it every time.

You can do this:

l New data enters the cleaning process uniformly

l Each level of screening has fixed criteria

l After cleaning, export different data packages according to labels

l Sales or customer service follow up based on hierarchical data

This reduces human judgment and improves overall efficiency.

Use the filter tool to process the basic data first

In the data cleaning process, the most critical step is still"Basic filter number". If the account itself is not available, all subsequent analysis will be meaningless.

In actual operation, you can first use Digital Planet to do screen number detection, and thenThe real available accounts among Kakao users are screened out, and then the valid data is further hierarchized according to age group and data tags. This can significantly reduce the impact of invalid data on subsequent processes. Digital Planet supports free trial screening test.

The core of data cleaning is to separate and solve different problems

If you want to improve the efficiency of Kakao user data cleaning, the key is not to add tags, but to split the problem:

l First check whether the account is available

l Then determine whether the user matches

l Finally optimize the contact method

When each step is clear, you will find that the data can be used cleaner and more stably without complex models.

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