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22

2026-04

In addition to screening T-cards, US number testing also depends on the operator, equipment, and blue label status.

In the scenario of US number detection, many people will initially focus on T card identification. There is no problem in this direction, because T card can indeed provide part of the user structure judgment. But if you only focus on T cards, the screening results are often incomplete. Especially now when doing WhatsApp US number detection, batch screening, and precise user screening, more and more teams are starting to look at the operator, device identification, and blue label status at the same time, instead of relying on one dimension alone.

21

2026-04

How to unify standards for multi-region number detection? Data from South America, North America, and Southeast Asia cannot be one size fits all.

When doing global number detection, an issue that is easily overlooked is not the detection tool itself, but how to set the standard. Many teams are accustomed to using a set of number detection logic to run all regions at first. They think this is more efficient and the process is unified. However, after running for a long time, they often find that the results of South American data, North American data, and Southeast Asian data are often skewed if they use the same set of standard filters. The problem is not that the number is detected incorrectly, but that the standard should not be one-size-fits-all.

21

2026-04

How to find high-spending people over 45 years old? It is more stable to use device identification and active status together.

In the screening of high-value users, high-spending people over 45 years old have always been an underestimated category of data. Many people do user screening and tend to focus on users over 25 years old and users over 35 years old, but ignore that people over 45 years old have more stable value in many industries. Especially in high-customer single products, long-term services, and local high-net-worth consumption scenarios, this group of people is often not the largest in number, but they are likely to be of higher quality.

21

2026-04

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 prone to problems. 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.

21

2026-04

What dimensions are supported by the global screening platform? Gender, age, and avatar have become standard.

Nowadays, choosing a global screening platform is no longer as simple as whether you can check the registration status. In the past, many teams did number detection, and as long as the platform could determine the activation status of the number, it was considered sufficient. But now if a global screening platform only supports basic registration testing, it is often difficult to meet actual business needs. Because the data usage logic has changed, the screen number is no longer just to determine whether the account exists, but to determine the user quality, crowd structure, and whether it is worth reaching.

20

2026-04

US Android user screening or iOS user screening, which one is more suitable for low-cost conversion recently?

To achieve low-price conversion in the US market, the dimension of device identification has recently been re-emphasized. In the past, many people used to screen numbers, paying more attention to user registration status, activity, or gender and age. But recently, when screening American users, the differences between Android users and iOS users have begun to be discussed separately more and more.

17

2026-04

How to screen user data in 2026 so as not to fall behind, and how to match activity, gender, age, equipment, and authentication

In the current data environment, user filtering is no longer a simple conditional filtering, but a set of combination logic. With changes in platform rules, user behavior and delivery costs, single-dimensional screening methods gradually become ineffective and are replaced by multi-dimensional combined screening.

17

2026-04

When doing cross-border advertising, what is more worthy of screening recently are high-active users or high-quality equipment users

In cross-border advertising, an increasingly common disagreement is whether to prioritize high-active users or high-quality equipment users. Both approaches have their supporters, and the results vary significantly in different projects.

16

2026-04

Device identification, age stratification, and gender tags are becoming new combinations for global number screening

In the number screening system, single-dimensional judgment has become increasingly difficult to meet actual needs. A common approach in the past was to only determine whether to register or only look at a certain tag. However, after the data scale increases, this method is prone to deviation.

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