existIn the construction of Telegram's private domain, mass marketing and precise user contact, "screening" is no longer just about determining whether an account exists, but has evolved into a comprehensive strategy of "tag recognition + behavioral profiling + operational status". Especially in the two key dimensions of gender and age,Telegram tag detectionIt has become the core technology that determines screening efficiency and conversion potential.
This article will start from actual combat and share how to achieve dual improvements in efficiency and accuracy in gender and age identification through tag detection technology.
Why are gender and age labels so critical?
1. Match product crowd portraits
Brands selling beauty products do not want to waste their budget on marketing to men; when promoting digital products, they also hope to focus onYoung men aged 18-35. Behind these needs, it isThe value of gender and age screening.
2. Refined operations and hierarchical management
After having tags, users can be managed hierarchically. For example:
lWomen aged 18-25: Content focuses on beauty, makeup and outfits
lMen over 30 years old: Prefer financial management and investment
lUsers with unknown gender: included in the default pool and waiting for subsequent identification
3. Avoid waste of resources
If you are using mass messaging, inviting groups, placing robots, etc. to operate accounts in batches, then determine in advance which accounts aretarget group, which can greatly reduce the risks of being blocked, complained, and low response rate.
The whole process of tag detection: How to obtain gender and age information?
Step 1: Prepare account data to be tested
You can get it in the following waysTelegram account ID:
lList of mobile phone numbers (if fromWhatsApp sync users)
lGroup member export information (TG group member extraction tool)
lusername(@username) or user ID (purely numeric)
Step 2: Import the detection platform and start tag analysis
With the help of e.g.Digital Planet PlatformWaiting for supportTG detection tool, after importing the account, the system will automatically analyze the user's gender and age.
Analysis methods include:
lAI avatar recognition: Identify gender and age range through user avatar (such as20-25, 30-40, etc.)
lNick name+Semantic analysis: Identify the English name and age number in the nickname (such asjenny18, john1992)
lVerbal behavior judgment: The system will also analyze whether the account prefers to use English, Arabic, Indonesian, etc. to assist the country.+Age judgment
The output will contain the following fields:
lIs it enabled?Telegram
lIs the avatar a real person?
lAI determines gender: male/female/uncertain
lAge tag:<20 years old, 20-25 years old, 25-35 years old, 35 years old and above
lActive status: whether it is an active account and whether to set a username
lCredit rating and risk mark (supported by some platforms)
Actual test results: What improvements can gender and age recognition bring?
According to the sample data feedback after batch testing on the platform:
lrecognizedThe click-through rate of female accounts has increased on average32%
lAge isConversion rate of users between 25-35 years oldhigher than average48%
lAfter using tag grouping operationsComplaint rates dropped by more than60%
It can be said that not doing tag screening is like casting a blind net. After adding gender and age tags, accurate placement can be achieved and efficiency can be improved.
How to avoid common misunderstandings in label recognition?
1.Avatar recognition≠100% accurate
If some users use cartoon avatars, animal pictures, etc., the system may judge them as"uncertain". It is recommended to set a default response strategy in subsequent operations.
2.Nickname misjudgment requires cross-validation
likeNicknames such as "babyboy" and "mr.snow" can easily mislead the system. Relying solely on nicknames may lead to inaccurate labels and must be used together with avatar recognition.
3.Failure to recognize is low value?
Not so. Although some users cannot determine gender and age, they may still beAccounts with high interaction potential, stratification can continue through follow-up behavioral monitoring.
Who should useTelegram gender and age tag detection?
lForeign trade and cross-border e-commerce sellers:According to age+Gender-matching products to increase private chat transaction rates
ladvertising agency: Accurately screen advertising audiences and optimizeCTR and CVR
lcommunity operator: Constructing women’s theme communities, youth technology groups, male investment groups and other group management groups
lEducation and training/course promoter: Users of different age groups have huge differences in interests, so hierarchical design of course content is more efficient.
Recommended tool to use: Digital PlanetTG label detection system
lImport mobile phone numbers in batches/username
lSupports detection of activationTG account
lSupport gender+Age+Activity+Avatar and other dimension recognition
lSupport exportUse Excel and connect it to CRM
lThe background comes with filters and supportsCombination operations such as "Only view women aged 25-35"
How to start?
Just prepare yourUpload the TG user list (mobile phone number or user name) to the Digital Planet platform, select the "tag detection" function, and the system will generate a complete detection report within a few minutes.
You can set filter conditions based on report content, such as:
lAs long as it is openedTelegram account
lSelect onlyUsers under 30 years old
lThe avatar is a real person+ A high-quality account with a nickname + a username
thereby building a real"Accurate, efficient, low-complaint" high-quality user pool.
For more informationWith the complete function of Telegram tag detection, welcome to experience the multi-dimensional filtering service supported by [Digital Planet] to help you use data to drive private domain growth and global user conversion.
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