How to screen users over 25 years old? Can e-commerce and social products use the same set of data?

During the user screening process,People over 25 are often analyzed separately. The reason is that this age group usually has more stable spending power and clearer usage habits. But in practical applications, a common misunderstanding is: using the same batch of data for both e-commerce and social products.

It all seemsUsers over 25 years old, but the needs of the two business scenarios are completely different. If no distinction is made, it will easily lead to a decrease in conversion efficiency.

WhyUsers over 25 years old are often screened separately

From a behavioral perspective,Users over the age of 25 have usually formed a relatively stable pace of life and consumption habits.

In e-commerce scenarios, such users are more likely to make actual purchase decisions; in social products, such users pay more attention to communication efficiency and user experience. Therefore, this age group has both commercial and operational value.

This is why this group of people is often prioritized during screening.

What is the core difference between e-commerce users and social users?

Although the sameUsers over 25 years old, but the behavioral logic of different products is obviously different.

E-commerce users pay more attention to the product itself, such as price, quality and cost-effectiveness; while social product users pay more attention to interactive experience, such as communication efficiency and social relationships. This difference determines the focus of screening.

If the same set of standards is used, matching deviations can easily occur.

Why can’t the same set of data be used directly?

The same group of users have different values ​​in different scenarios.

For example, a user may be a high-frequency consumer on an e-commerce platform, but not active on social products; conversely, some users may be very active on social platforms, but have weak consumption behavior. If usage scenarios are not differentiated, data will be misused.

Therefore, the data needs to be re-screened according to the business rather than reused directly.

Which screening dimensions should we pay more attention to in e-commerce scenarios?

In e-commerce screening, the focus is usually on spending power and purchasing behavior.

For example, user activity, device type, age group, and past behavior tags, etc. These dimensions can help determine whether a user is willing to purchase. At the same time, low activity and abnormal accounts need to be filtered to ensure data quality.

Through these screenings, the conversion efficiency can be improved.

Which user characteristics are social products more suitable to focus on?

In social products, the focus shifts to usage frequency and interaction behavior.

For example, whether the user has recent active status, whether he has continuous usage habits, whether he is easy to participate in interaction, etc. These dimensions determine whether users can integrate into the product environment.

Compared with e-commerce, social products rely more on activity rather than consumption power.

How to adjust more reasonable screening methods

In actual operation, you can first perform basic screening, such as account status detection and activity judgment, and then perform secondary screening based on different businesses.

For e-commerce, users with high consumption potential can be further screened; for social products, high active users can be screened first. In this way, the same batch of raw data can be adapted to different scenarios.

In this process, basic screening, active user identification and data cleaning can be completed through Digital Planet, and then segmented according to business needs to improve the overall screening efficiency.

Changes from unified data to scenario-based use

User filtering logic is shifting from unified standards to splitting based on scenarios.

same batchUsers over the age of 25 require different processing methods in different businesses. Only by completing the differentiation in the screening stage can subsequent operations and conversions be more stable.

There is nothing wrong with the data itself, the key lies in whether it is used in the right place.

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