Why Telegram account screening is becoming more and more detailed, and the mix of real and fake users leads to rising traffic

DoPeople who attract traffic to Telegram have clearly experienced a change in the past two years: it is not that the traffic suddenly decreased, but that for the same amount of data, the proportion of truly valuable users is declining. On the surface, it seems that the number of group members is quite large and the size of the number pool is not small, but the traffic drainage effect is becoming increasingly unstable. Many times the reason is not at the traffic entrance, but at the data itself. There is a mix of real and fake users, a mix of abnormal accounts, and an increase in the proportion of low-active users. When these problems are superimposed, the cost of attracting traffic will be directly pushed up.

This is whyTelegram account screening is becoming more and more detailed. It’s not because everyone deliberately makes the process complicated, but if you still only look at the registration status and basic activation, many hidden costs cannot be suppressed at all.

Why is the cost of attracting traffic to Telegram getting higher and higher?

In essence, it is not because users have become more expensive, but because there are more invalid users.

If a batchTelegram data is mixed with fake users, abnormal accounts, and low-active users. It seems that the amount of data is huge, but in fact there are not many people who can actually be converted, so the traffic diversion itself will be diluted.

As a result, reach costs rise, group interaction quality declines, and subsequent conversion judgments become increasingly ugly.

This kind of cost is often not incurred during the launch stage, but has already been buried during the screening stage.

Therefore, the increase in traffic drainage costs is often not a front-end problem, but a data structure problem.

WhyTelegram account screening begins to become more and more detailed

Because just looking at the registration status is no longer enough.

The registration status can only determine the existence of the account and does not solve the problem of real and fake users.

And do it nowTelegram account screening usually at least separates it into active user screening, identification of true and false users, filtering of abnormal accounts, and identification of risky accounts.

In the past, many teams would not do all of these dimensions, but now they are becoming more and more like a basic layer.

Because if it is not disassembled, a lot of low-quality data will be mixed in.

This is whyTelegram's screening logic is becoming more and more detailed. It is not about taking more actions, but about taking apart the problems that were originally mixed together and dealing with them.

There is a mixture of real and fake users, which links are affected first?

The first impact is drainage efficiency.

Because the proportion of fake users is high, it seems that a lot of people have been reached, but there are no real responses.

The second impact is the quality of group interaction.

Many groups seem to have a large number of people, but have a high proportion of silence, which is essentially related to a low-quality user structure.

The third impact is subsequent transformation judgment.

Because there is a problem with the data pool itself, it is difficult to determine whether it is a content problem, a path problem, or a crowd problem.

This is why the mixture of real and fake users not only affects one link, but also affects the entire link.

What layers will Telegram account filtering be divided into now?

From a practical perspective, more stable screenings are now often layered.

The first level is to enable detection. First filter the basic unavailable data.

The second layer is filtering abnormal accounts. Eliminate obviously low-quality and abnormal data first.

The third level is active user screening. For example3-day active and 7-day active tiering.

The fourth layer is the stratification of high-value users. Further identify high-quality users.

The point of this structure is not that there are many layers, but that each layer solves different problems.

If they are all mixed together, the accuracy will usually be much lower.

If the screening logic is not refined, what hidden costs will it bring?

There are many hidden costs that are not visible at first, but will become more and more obvious later.

There will be more invalid contacts because the proportion of low-quality users is high.

Operational actions will be slowed down because many actions are spent on invalid users.

drainageROI will also become increasingly ugly, as the denominator becomes increasingly amplified by low-quality data.

These problems are often not completely remedied by subsequent optimization.

Because the fundamental problem was not screened out before.

Therefore, the essence of refining the screening logic is to reduce hidden costs.

Why more and more teams are picking up Digital Planet from the front

Because many teams have found that if they only do basic testing during the screening stage, the follow-up actions will become more and more difficult.

Therefore, more and more people will prioritize more detailed data screening.

At this stage, many teams will use Digital Planet toTelegram account detection, abnormal account filtering and active user hierarchical processing. Digital Planet supports free trial screen number detection, which is more suitable for screening real and fake users, abnormal accounts, and highly active users before entering the drainage process.

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