Telegram automatic private chat batch adding skills: how to reduce the abnormality rate

Automatic private chat and batch adding of people are two things that many teams useCommonly used methods in Telegram’s customer acquisition phase. But as soon as the scale increases, the abnormality rate will increase. Failed to add people, prompt restrictions, and account functions are restricted. These problems are often not accidental, but structural problems.

If you want to reduce the exception rate, you need to optimize the process itself instead of simply slowing down. What really affects stability are data sources, account rhythm and operation structure.

1. Where does the abnormality rate come from?

Anomalies usually come from three directions.

First, add repeatedly. If the same account performs similar actions on similar people in a short period of time, the system can easily identify it as abnormal behavior.

Second, there is no filtering of data. The list contains a large number of unregistered users, silent accounts or abnormal accounts. The success rate of addition decreases and the failure rate increases.

Third, the account rhythm is consistent. Multiple accounts perform the same operations at the same time, and the behavior pattern is too concentrated.

When these three points are superimposed, the abnormality rate naturally increases.

2. Data filtering is the first line of defense

Many people ignore the quality of data sources and directly import externally obtained numbers or usernames into the system. But if these accounts are not opened at allIf Telegram has been inactive for a long time, adding the action itself will be invalid.

Before batch execution, it is recommended to complete the registration status and account activity detection. Through Digital Planet, you can batch identify whether the account is opened and whether it actually exists, and output the detection result label. Eliminating invalid data in advance can significantly reduce the failure rate.

Clean data is the first step to reduce the anomaly rate.

3. Account stratification is more important than deceleration

Many people will choose to slow down the overall speed after encountering an abnormality, but speed is only a superficial variable. The truly effective method is to establish an account tiering mechanism.

New accounts are responsible for low-frequency tasks, and mature accounts are responsible for high-frequency tasks. Execute at staggered times between different accounts, and do not add them together during the same time period.

After stratification, the behavior paths of each account are more dispersed, and the abnormality rate naturally decreases.

4. Batch adding people and automatic private chat should be designed separately

Although the two are often used together, the operating logic is different. Adding people in batches is to expand the user pool, and automatic private messaging is a conversion action. Don't add and send a large number of messages to the same users at the same time.

It is recommended to implement it in stages. The first stage is mainly about adding people, and in the second stage, private messaging will be launched. This can avoid the risks caused by behavioral superposition.

5. Stability testing before scale expansion

Test on a small scale before scaling up. Observe the addition success rate, abnormal ratio and account status changes. After the test is stable, the number of accounts will be gradually increased.

Many teams directly increase the volume without testing, and the abnormality rate will naturally soar.

The key to reducing the abnormality rate lies not in any one technique, but in the overall structure. Data is screened first, accounts are managed hierarchically, the rhythm is staggered and the operation phases are separated. As long as the process is clear, scaling can remain stable.

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