Screening of abnormal accounts in Telegram: Why community customers are increasingly afraid of robot data

existIn the process of acquiring customers in the Telegram community, an increasingly obvious problem is: the data seems to be a lot, but the real users are getting fewer and fewer. There are many people joining the group, but no one responds to messages sent, and even some accounts don’t even have basic interactions.

The core behind this situation is thatThere are more and more "abnormal accounts", which are commonly referred to as robot data, batch registered accounts, and low active users. If not screened in advance, these data will seriously affect the quality of the community and conversion effects.

what isTelegram abnormal account

Abnormal accounts are not just"Bots" also include a variety of low-quality users.

For example, empty accounts registered in batches, silent accounts that have not been used for a long time, tool accounts that are only used to increase the volume, etc. On the surface, these accounts are"user" but actually has little real behavior.

In the community, such accounts usually do not participate in interactions or generate conversions, and will only lower the overall quality.

Typical performance of robot data in the community

When the proportion of abnormal accounts is high, the community will have some obvious characteristics.

For example, the number of people joining the group is growing rapidly, but the number of comments is very low; or after the message is sent, there is no feedback. There are also some groups that have a large number of people on the surface but very few active users.

These phenomena often indicate that a large number of robots or low-quality accounts are mixed in the data.

Why abnormal accounts affect conversion results

The core of community operation is interaction, not the number of people.

If users don't speak, don't participate, and don't respond, no matter how many people there are, they won't produce actual value. More importantly, abnormal accounts can affect judgment and make people mistakenly think that there is a content or strategy problem.

In fact, many times the problem lies with the data itself, not the way it is executed.

How to identify anomalies in batchesTelegram account

In the case of large amounts of data, it must be processed through batch filtering.

can passTelegram screening tools or API interfaces detect numbers to determine whether to register an account, and combine active screening to identify real users. At the same time, abnormal accounts can also be identified through behavioral characteristics, such as long-term inactivity or abnormal patterns.

In actual operation, it can be done through Digital PlanetTelegram screening, active user screening and abnormal account identification support batch processing, filtering out robot data in advance and optimizing the data structure from the source.

The relationship between active user filtering and exception filtering

Active filtering is one of the important means to filter abnormal accounts.

Because most robot accounts or low-quality accounts lack real usage behavior. By filtering active users, some abnormal data can be indirectly filtered out.

Therefore, registration testing is often combined with active screening in the screening process to improve overall data quality.

Why does community operation rely more and more on screening numbers?

along withThe scale of Telegram usage has expanded, and the proportion of abnormal accounts has also increased.

Without screening, it is difficult to guarantee the quality of the community. When the screening number becomes a pre-step, it can effectively reduce invalid users and increase the interaction rate and conversion rate.

For teams that need to operate communities for a long time, screening has evolved from“Optimization means” become “basic capabilities”.

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