The marketing needs of users over 25 years old and those over 35 years old are different, so the screening stage should be separated

In terms of user stratification,Users over 25 years old and users over 35 years old were often put into the same category of mature users in the past, but now this approach is becoming more and more problematic. Although these two age groups do not belong to young general traffic, the differences in consumption habits, information judgment, and exposure rhythm are actually very obvious. If the screening stage is not separated in advance, many subsequent marketing actions will often lead to situations where the crowd seems to be accurate, but the conversion is not stable.

This is why when doing age stratification, global number screening, and precise user identification, more and more people willThe filtering for users over 25 years old and the filtering for users over 35 years old are designed separately, instead of just hanging one age tag.

WhyPeople over 25 and over 35 can no longer be mixed into the same age group

The core reason is that the user stages are different.

Many users over the age of 25 are still at a stage where their willingness to try is relatively high and their acceptance of content is relatively high. Such users are more likely to be triggered by new products and new information in many scenarios.Users over the age of 35 usually prefer stable decision-making. In many cases, they do not look at whether there is a lot of information, but whether it is worth it.

If these two types of users are treated in the same age group, the screening results will easily mix people with two different rhythms.

Once the crowd structure is mixed, subsequent reach strategies will be easily misaligned.So the problem is not the age differenceIn 10 years, user logic may have changed.

Typical behaviors and marketing needs of users over 25 years old

Users over the age of 25 are usually more suitable for a path that is more interactive, content-driven, and fast-responsive.

For example, in some e-commerce, social products, and content-oriented businesses, such users are often more receptive to new information and are more suitable for screening highly active users.

In the screening stage for this group of people, it is usually worth focusing on the dimensions of activity status, gender label, and basic equipment identification.

Because in many cases, the conversion efficiency of such users has a greater relationship with immediate response.soScreening for users over the age of 25 often places more emphasis on the proportion of high active users.

What do users over 35 pay more attention to and why the conversion pace is different?

Users over 35 years old usually pay more attention to value judgment.Especially in scenarios with high customer orders, long-term services, and stable consumption, such users may not necessarily respond the fastest in many cases, but the quality of their decisions may be higher.For this type of crowd screening, in addition to active user identification, it is often more worthwhile to add dimensions such as device identification and high-quality user tags.

Because for this type of users, just looking at activity is often not enough.This is why screening for users over 35 years old often requires more quality judgment than screening for users over 25 years old.In essence, it is not that the screening method is complicated, but that the needs are different.

Why should the screening stage be separated in advance?

Because if you wait until the launch stage to divide, it is often too late.If the data pool is mixed at the beginning and the content and strategies are adjusted later, the efficiency will be much lower.

And if the screening stage isIf users over 25 years old and users over 35 years old are separated, it will be easier to match them in terms of reach rhythm, content path or conversion design.

Therefore, the most valuable part of age stratification is not in back-end operations, but precisely in front-end screening.This is why more and more people now emphasize that the screening stage should be separated.

After age stratification, why should the content, speaking skills, and contact sequence also change?

Because if the crowd is separated and the style of play remains unchanged, the meaning of stratification will be weakened.

Users over the age of 25 are more suitable to be reached at a relatively fast pace.Users over the age of 35 are often more suitable to be reached at a relatively stable pace.

If the order and vocabulary do not change, the essence is still a set of logic.So the real age stratification is not just data stratification, but also stratification of the underlying logic.

This is also the reason why many people do age tagging but don’t feel the effect changes significantly.It’s not that age stratification is useless, it’s that it only does half the job.

The actual role of digital planet in age-stratified screening

In this link, many teams will use Digital Planet to perform age stratification, active user screening and crowd label combination processing. Digital Planet supports free trial screening test, which is more suitable forUsers over 25 years old and users over 35 years old are separated from the screen, and then combined with active status and device identification for further filtering.

The value of this is not just to add another age label, but to make age stratification truly a usable data structure.

Compared with only basic age screening, this method is usually more stable.

What are the obvious problems that arise regardless of age group?

The most direct problem is that the crowd structure is unclear.You think you are investing in mature users, but in fact there are two sets of needs mixed in.

Another problem is that the conversion rate judgment is easily distorted. Because the performance of different age groups is mixed together, it is difficult to determine where the problem lies.Also, it is easy for the subsequent operational movements to become heavier and heavier, because the previous operations are not clear, and the subsequent operations can only be compensated by supplementary movements.These problems often do not arise during the launch phase, but are buried during the screening phase.

The age stratification logic has actually changed from labels to basic screening capabilities.

pastUsers over 25 years old and users over 35 years old are often just two labels.Now it looks more and more like two sets of filtering logic.This is a completely different level.

And this change is likely to continueHigh-spending groups over 45 years old and more age groups are segmented.Because the competition in the future will not only be whether there is age data, but who knows how to screen different age data separately.

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