In addition to avatars, should Zalo real user testing also include gender and age

existIn Zalo user screening, avatar detection has always been a common dimension. When many people do Zalo real user testing, their first reaction is to check whether there is an avatar first, because the avatar is often used as one of the basic signals to judge the real account. But the discussion has begun to change recently. More and more people are not satisfied with just looking at the profile picture, but are asking whether, in addition to the profile picture, gender and age tests should be added.

This issue deserves discussion becauseZalo's real user detection itself is no longer just to determine whether the account looks like a real person, but to determine whether this batch of data is worthy of being used for subsequent customer acquisition, private domain operations and precise reach. If the goal changes, the filtering dimensions will naturally change.

Why is it increasingly not enough to just look at avatars?

Avatars do have reference value, because they can help filter out some low-quality accounts or obviously abnormal accounts. There is no problem with this. But the problem is that avatars can only solve part of the authenticity judgment, but cannot solve the problem of crowd matching.

For example, if an account has an avatar, it does not mean that it is the target user; having an avatar does not mean that the user is active; even having an avatar does not mean that it is suitable for the current business. In other words, the avatar is more of a basic signal, not a complete judgment.

If what you do is basicFor Zalo number detection, just looking at the avatar may be able to meet some needs. However, if you are doing Zalo precise user screening, Zalo real user identification or batch screening, relying only on avatars is often not accurate enough.

This is why more and more people now combine avatar detection with other tags.

Is it necessary to add gender and age tests?

Whether it is necessary or not depends on the scenario.

If the business itself is sensitive to the crowd structure, such as local customer acquisition, beauty, education, private domain conversion and other businesses, gender and age tests are usually worth adding. Because in these scenarios, crowd matching inherently affects the conversion results.

For example, if the goal is to screen out female users, simply looking at avatars is obviously not enough;For users over 25 years old or over 35 years old, just avatars cannot solve the problem.

At this time, gender tag screening and age testing are actually making up for the part of the judgment that the avatar detection cannot make.

But if you are just doing very basic data cleaning, sometimes you don’t necessarily need to add the dimensions very deeply. So it’s not necessary to add it, but it depends on whether the business needs it.

What is the value of avatar, gender, and age combination screening?

The greatest value is to reduce misjudgments.

If you only look at avatars, it is easy to retain data that looks like real accounts but does not belong to the target group.

If you add gender and age tests, it is equivalent to moving from authenticity judgment to crowd matching judgment.

The data filtered out in this way is not only more like real users, but also more like target users.

These two levels have completely different values.

especially doingWhen it comes to Zalo real user detection, Zalo user data cleaning, and precise user identification, the stability of this combination of screening is usually much higher.

This is why recentlyZalo's user screening logic starts from looking at avatars alone and moves towards combined tag screening.

How to build a more reasonable real user detection process

From a practical point of view, a more stable process usually does not start with complex layering directly, but handles it step by step.

First, check the account status to ensure that it is basically available.

Then do avatar detection and filter out obviously low-quality accounts first.

Then, according to business needs, add gender label screening and age testing to further identify the target group.

If it is more detailed, active user filtering can also be supplemented, because user activity status itself will also affect data quality.

This set of logic is more complete than looking at the avatar alone.

In this link, you can do it first through Digital PlanetZalo account identification and avatar detection, combined with gender testing, age stratification and active user screening, further separate real users and target users for subsequent customer acquisition or private domain operations.

This method is much more stable than just making a single-layer judgment.

Which businesses are suitable for this type of combination screening?

It is more suitable for businesses with high crowd matching requirements.

For example, local customer acquisition, because this type of business inherently relies on crowd accuracy.

Private domain operations are also suitable, because private domains essentially focus on data quality rather than pure quantity.

In the Vietnamese market promotion scenario, the value of this type of screening will also be more obvious, because many times the problem is not whether there are users, but whether the users are accurate.

In these scenarios, avatar plus gender age testing is usually worth doing.

WhyZalo real user detection begins to turn into multi-dimensional judgment

Essentially, it's because the definition of real users has changed.

In the past, the so-called real users were more like judging the authenticity of accounts.

Nowadays, it is more and more like the authenticity of the account plus the matching judgment of the target group.

These two levels are not the same thing.

Once the definition changes, the detection dimensions will naturally change.

So the current discussion about whether to add gender and age tests to avatar detection is essentially not a discussion about adding two more tags;What level does Zalo's real user detection go to?

Zalo’s filtering logic is also being upgraded

In the past, the focus was to determine whether the account looked like a real person.

The focus now is whether this account is real, whether it is a target user, and whether it is worth reaching.

This is a completely different level.

The combination of avatar detection, gender test, and age test is actually going in this direction.

So the question is no longer whether just looking at the profile picture is enough, but if the goal is to screen people more accurately, just looking at the profile picture is not enough in the first place.

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