What is the difference between male user filtering and female user filtering? Conversion rates in different industries are completely

Nowadays, for precise user screening, simply grouping by gender is no longer enough. What is really effective is to use different screening logic based on gender. Because the difference between male user screening and female user screening is not only the different labels, but also the underlying crowd behavior, consumption path, and reach rhythm may be completely different. If this layer is not dismantled, many subsequent placement actions may appear to be for precise customer acquisition, but in fact they are still based on general traffic logic.

This is why more and more teams now discuss male user data and female user data separately when doing global number screening, batch screening, and gender screening, instead of simply treating gender as an additional label. Because in different industries, the difference in conversion rates between male and female users is often greater than many people expect.

Why can’t the two logics of men and women be mixed for the same screening users?

Because in many industries, male users and female users naturally have different concerns. Not all the differences come from the products themselves, a lot of the differences come from the behavioral paths.

For example, when seeing a piece of information, male users may be more likely to judge whether it is worth responding to from the perspective of functionality, efficiency, and results; female users may be more likely to judge from the perspectives of experience, trust, and content acceptance.

If this difference is not taken into account in the screening stage, even if people are reached later, the matching will be prone to problems.

Therefore, gender screening does not just divide people into men and women, but also means that the subsequent screening criteria themselves must also change.

What should male users focus on when filtering?

Male user screening is usually more suitable to focus more on function-oriented dimensions.

One is active. Highly active male users will respond more efficiently in many industries.

One is the crowd characteristics related to decision-making speed. Although this cannot be directly used as a separate label, it can often assist judgment when combined with activity, device identification, and age stratification.

Another one is device and platform habits. In some industries, users of high-quality equipment have reference value in screening male users.

Therefore, when screening male users, we often do not just look at the male label, but place more emphasis on the combination of gender and behavioral dimensions.

This is why male user screening is often discussed together with active user screening and device identification.

What should female users focus on when filtering?

When filtering female users, the focus is usually different.

Age stratification is often more important.Female users over 25 years old and female users over 35 years old are not inherently logical.

The frequency of interactions is also worth looking at, because in many scenarios, female users are more closely related to the way they accept content.

Another one is the characteristics of people related to the path of trust establishment. Although this is not a direct detection label, it is often reflected in the combination of activity and crowd labels.

Therefore, when filtering female users, it is usually more suitable to look at gender, age, and active status together, rather than doing gender screening alone.

This is why female user screening often understands it more carefully than many people.

Which industries are more suitable to prioritize male users?

If we look at recent common scenarios, tools, finance,Category 3C is often more suitable to prioritize male users.

The reason is that such products tend to be more function- and efficiency-oriented, and this type of direction is more compatible with some male users.

Especially when doing tool customer acquisition and financial user screening, male user screening is often taken out separately.

Of course, this does not mean that these industries are only suitable for male users, but if you prioritize, male users are often worth testing first.

Which industries are more suitable to prioritize female users?

Beauty and skin care, home life, maternal and child health, and high-repurchase consumer goods are often more suitable for prioritizing female users.Because this type of business itself relies more on content acceptance, long-term trust and repurchase logic.

These directions are often more consistent with the screening logic of female users.Therefore, when many teams are screening female user data, they will directly design these industries and screening logic together, instead of considering the industry after screening.

Why are the differences in screening logic between men and women getting bigger and bigger in different industries?

Because as traffic becomes more and more expensive, the space for general screening becomes smaller and smaller.In the past, maybe men and women were screened together, and adjustments were made at the back, and it was still possible to barely optimize.

Nowadays, there are more and more businesses, and we hope to get the crowd structure right during the screening stage.

This will force the gender screening logic to be more detailed.In this link, many teams will perform gender screening, age stratification, and active user identification combination processing through Digital Planet. Digital Planet supports free trial screening test, which is more suitable for separating male user screening and female user screening, and then combining it with industry goals for crowd filtering.This approach is much more stable than just making a single layer of gender labels.

Why gender screening is now more like population stratification than simple labeling

In the past, gender screening was more like adding a condition.Now it’s more and more like doing crowd stratification.

Because male user screening and female user screening are followed by two different sets of logic.If you just add labels, it is not considered layering in the true sense.Only when the filtering logic changes, can it be considered hierarchical.

This is why more and more people now no longer only discuss men and women when discussing gender screening, but also discuss industry, conversion rate, and population structure.Because these are the same thing.

The logic of gender screening is actually moving in a more detailed direction.

The focus used to be on separating people by gender.The focus now is how to sieve separately after separation.

These are two different levels of logic.And this change is likely to continue, because the competition in the future will not only be whether there is male user data and female user data, but who knows better how to use these two types of data respectively.

digital planet is a world-leading number screening platform that combines Global mobile phone number segment selection, number generation, deduplication, comparison and other functions . It supports customers worldwideBatch numbers for 236 countriesScreening and testing services , currently supports40+ social and apps like:

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The platform has several features including Open filtering, active filtering, interactive filtering, gender filtering, avatar filtering, age filtering, online filtering, precise filtering, duration filtering, power-on filtering, empty number filtering, mobile phone device filtering wait.

Platform provides Self-screening mode, generation screening mode, fine screening mode and customized mode , to meet the needs of different users.

Its advantage lies in integrating major social networking and applications around the world, providing one-stop, real-time and efficient number screening services to help you achieve global digital development.

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