How do male Telegram users over 25 years old use AI recognition to screen high-quality accounts

In the customer acquisition scenario of the currency circle, user quality often directly determines the conversion results. especiallyTelegram is a channel with a huge number of users, but only a portion of the population actually has the ability to invest and make decisions. Compared with general users, men over the age of 25 usually have better financial strength, risk awareness and long-term participation willingness, so they have become a key screening target.

The problem is that such users are not directly marked in the data and need to be identified through a set of filtering logic.It is in this link that AI technology plays a key role.

1. Why does the currency circle rely more on user portrait screening?

The currency circle is different from ordinary e-commerce. The user decision-making cycle is shorter, but the threshold is higher. If the user does not have a certain cognitive foundation or financial ability, even if the contact is successful, it will be difficult to complete the conversion.

maleUsers over the age of 25 usually have several characteristics: they are sensitive to investment information, have a certain source of income, have a higher probability of accepting emerging assets, and have a basic understanding of risks. This type of group is also relatively active in Telegram and is often distributed in trading groups, analysis groups, project groups and other scenarios.

Therefore, screening user portraits is essentially narrowing the target range and focusing limited resources on people who are easier to convert.

two,What is the core logic of AI identifying users?

Without labels,AI mainly makes judgments based on multi-dimensional information rather than a single condition.

The first is gender recognition, which can be inferred through user name characteristics, behavior patterns, language habits, etc. The second step is age determination, which is analyzed through account usage habits, active time period, type of content participated in, etc. The other is behavioral characteristics, such as whether to participate in investment discussions, whether to pay attention to project information, etc.

After these dimensions are superimposed, a relatively clear user portrait can be formed, thereby filtering out accounts that are more in line with the target.

3. How to putAI recognition combined with Telegram data

In actual operation,AI recognition is not performed independently, but is based on basic data screening.

The first step is to obtain the number or account data and thenTelegram enables testing to ensure that these numbers correspond to real accounts. The second step is to perform active filtering to filter out accounts that have not been used for a long time. The third step is to use the AI ​​model to identify tags, such as gender, age group, interest direction, etc.

Through such a process, high-quality users can be gradually screened out from the original data instead of directly facing a batch of disordered data.

In this process, it can be completed with the help of Digital PlanetTelegram enables detection and active screening, combined with AI identification tags, to achieve more accurate user screening results.

4. Several misunderstandings that easily occur during the screening process

In practical applications, there are several issues that will affect the screening effect.

One is over-reliance on a single label, such as only looking at gender or only looking at age, while ignoring behavioral characteristics. The users screened out in this way may not necessarily have investment attributes. Another is to ignore activity, even if the user matches the age and gender, but if the user does not use it for a long timeTelegram, still has no value.

Another situation is that the filtering is too loose, resulting in a large number of edge users being retained, which ultimately affects the overall conversion rate.

Therefore, filtering must be multi-dimensional superposition rather than simple conditional filtering.

5. How to further improve screening accuracy

After the basic screening is completed, the model can be continuously optimized through data feedback.

For example, record which users have interacted and which users have completed conversions, then analyze the common characteristics of these users, and adjust the filtering conditions in reverse. This allowsAI models gradually adapt to actual business needs, rather than staying in theoretical judgments.

In addition, the screening criteria can be adjusted according to different project needs, such as short-term trading users or long-term investment users. Different directions correspond to different crowd characteristics.

6. From screening to conversion, the key lies in crowd matching

When a group of men are screened outAfter high-quality users are over 25 years old, subsequent operations also need to match the characteristics of this group of people.

This type of user pays more attention to information value and revenue logic, so the communication content needs to be more professional rather than simple marketing. At the same time, the cadence can also be more direct, because this type of user decision-making is relatively faster.

whenWhen AI screening matches the actual operation strategy, the overall conversion efficiency will be significantly improved, instead of relying solely on quantity to reduce the effect.

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