Telegram age screening practical guide: How to find the precise distribution of target customer groups?
existDuring the operation of Telegram's private domain, age screening is often ignored, but it often determines the "hit rate" of reaching content. Whether a promotional content is aimed at college students or 35+ professionals, the age group of the user behind the account will directly affect the conversion rate. The actual situation is that most of the accounts obtained in batches lack age tags, making it difficult to operate in layers.
This article will focus onThe age screening needs of Telegram users are expanded from common application scenarios, identification methods, and screening paths, and an automated detection method that can be used without building a self-built tool is introduced.
What is the value of age screening?
not allTelegram users are all suitable for the same promotion context. In the following types of private domain operation scenarios, clear age tags are the prerequisite for accurate communication:
lEducational project placement: It is necessary to distinguish between teenagers and professionals, and match the course type and language style
lBeauty/Maternal and infant community drainage: Age determines whether the user has consumption needs and decision-making rights
lfinance/investment conversion: Age can be regarded as a basic evaluation indicator of financial management awareness and purchasing power.
lContent recommendation system construction: Age affects interest preferences, thereby affecting push strategies
If there are a large number of minors or elderly users in the account, it will cause the community atmosphere to be out of focus, the private chat reply rate to be low, and even lead to platform risk control.
How to judgeWhat is the age group of Telegram users?
Telegram does not force birth date collection like Facebook or LinkedIn, so other paths are needed to determine user age:
1. Nickname, username and information field analysis
Some users marked in their nicknamesInformation such as "post-95s", "born in 2000" and "1988" can be used to initially determine the age group if it can be parsed in batches. However, this method is only applicable to those with clear self-report information, and its adaptability is small.
2. Avatar image AI recognition
The current mainstream approach is to useThe AI visual recognition algorithm analyzes the user's avatar and estimates facial age. This method can quickly obtain classification labels such as "under 18 years old", "20-30 years old" and "30-45 years old" from larger volumes of data, and can be combined with confidence scores to assist in screening.
3. Behavioral characteristic model inference
By analyzing account behavior (such as frequently followed channel types, message interaction patterns, usage time distribution, etc.), a behavioral model of age group tendencies can be constructed. For example, users who frequently join campus groups and are more active late at night may be students.
4. Integration of third-party data sources
Some platforms can provide age tag data of historically verified accounts, and assign age attributes to new accounts through mapping and matching.
How to obtain in batchesAge filtering results for Telegram accounts?
For most users, building avatar recognition by themselves+Behavioral model systems are laborious and difficult to maintain. Currently, a more efficient way is to use the batch inspection capabilities provided by professional platforms.
digital planetprovidedTelegram’s age screening service has the following features:
lSupport mobile phone number/Batch upload in username and other formats, adapted to domestic and foreign collection channels
lThe system is based on avatar recognition+Natural language annotation analysis+Comprehensive judgment of behavioral model
lCan automatically outputFive categories of labels: under 18 years old, 18-24, 25-34, 35-44, 45+
lcombined gender/Activity/Registration status and other dimensions joint filtering, to achieve accurate crowd positioning
lTest results support structured export, to facilitate the docking of private domain tag systems and community diversion deployment
Currently, this service has been widely used in industry scenarios such as education and training, consumer goods, cross-border finance, and robot delivery, helping companies shorten customer identification time and improve the accuracy and efficiency of first contact.
Through age screening intervention,Telegram accounts are no longer cold data collections, but have become user profile assets that can be classified, activated, and operated. The true realization of hierarchical operations is the underlying driving force for private domain growth.
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