How to obtain Telegram’s 30-year-old user data? — TG Age Data Acquisition and LLM Application Guide

In overseas marketing, accurately reaching target groups is the key to customer acquisition efficiency. Many teams hope to filter out tg 30-year-old data (that is, active users on Telegram who are approximately 30 years old) for community operations, private message promotion, or cross-platform marketing. However, many people have a misunderstanding of “tg 30-year-old data” - it is not an independent product, but the age field in Telegram’s gender detection results. This article will provide you with a set of practical operation guidelines from data acquisition and screening methods to crowd analysis combined with LLM.

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What is tg 30 year old data? — Understanding the age field in Telegram gender detection

When you submit a Telegram gender detection task on a screening platform such as KK-DATA, the returned results include an age field in addition to the “male/female” label. This field is not obtained from the official API (Telegram does not disclose age), but is an approximate value estimated through user profile, social behavior and other dimensions. It can be used to filter people who are about 30 years old, but cannot provide the precise date of birth.

Source and accuracy of age field

The age field comes from the platform algorithm’s analysis of Telegram’s public information, including:

• Nickname and introduction text in user profile (some users will disclose age information)
• Avatars and group participation rules (indirectly infer age range)
• Behavioral characteristics such as active time period, speaking frequency (assisted judgment)

Therefore, the accuracy of this field belongs to statistical level, not ID card level. This accuracy is sufficient for operational scenarios (such as pushing content that is of interest to 30-year-olds), but cannot be used for compliance scenarios that require proof of age.

tg The difference between 30-year-old data and real age

Dimension	tg 30-year-old data (estimated field)	True age at birth
How to obtain	Export through the TG gender detection task of the screening platform	Users need to actively provide or authoritative database
Accuracy	Age range ± 3–5 years	Accurate to the nearest day
Purpose	Operation layering, content targeting	Identity verification, financial risk control
Compliance risk	Low (no sensitive personal privacy involved)	High (regulatory restrictions such as GDPR)

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How to obtain tg 30-year-old data through the sieve platform?

Taking KK-DATA as an example, you can get the data set containing the age field in just three steps: Prepare number → Submit test → Export screening. The entire process does not require programming and can be completed by console operation.

Step 1: Prepare number list (generate global number segment or customize import)

You need to have a batch of Telegram numbers to be tested first. KK-DATA provides two methods:

• Global Number Generation: Select the target country (such as the United States, Brazil, Southeast Asian countries) in the console, and randomly generate numbers for 240+ regions. This step is free and will automatically import your task list after generation.
• Customized CSV import: If you already have a number library (such as numbers collected from other channels), you can upload a CSV file and the system will parse and remove duplicates.

Recommendation: If the target group is overseas users around 30 years old, give priority to generating number segments for target markets (such as Europe, America, and Southeast Asia) to improve efficiency.

Step 2: Submit Telegram gender detection task (including age field)

Create a new “Telegram Gender Detection” task in the console and upload the prepared number. Test types include:

• Activation detection: Determine whether the number is registered with Telegram
• Activity Detection: Check whether you are online in the last 7 days/30 days
• Gender Detection (including age field): Returns the user’s gender (male/female/unknown) and estimated age

You can estimate the cost before submitting (billed by item, for specific prices for each item** please see the real-time price on the console**). After confirmation, the task enters the queue, and upon completion, the balance will be deducted based on the result.

Step 3: Export and filter (use CSV/TXT to filter users around 30 years old by age field)

Once the task is completed, click the “Export” button and select CSV or TXT format. The exported file contains fields, where the “age” column is the age estimate. You can use the filter function of Excel or Google Sheets and set conditions such as age BETWEEN 25 AND 35 to extract users who are about 30 years old. If programming is required, Python’s pandas library can complete filtering with just a few lines of code.

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tg The practical application of 30-year-old data in acquiring customers overseas

Obtaining data is only the first step, how to use it is the core. In the following three scenarios, TG 30-year-old data can effectively improve reach efficiency.

Scenario 1: Telegram community operation for 30-year-olds

Users around the age of 30 usually have stable income and clear consumption needs (such as financial management, mother and baby, and workplace skills). Using the age field, you can:

• Screen out active users aged 28–35 and invite them to join Telegram groups
• Push content suitable for this age group within the group (such as cross-border e-commerce discounts, online courses, tool products)
• Further segment by combining gender fields, such as pushing electronic products to 30-year-old men and beauty and apparel to 30-year-old women

Scenario 2: Cross-platform (WhatsApp/Line) collaborative marketing

Overseas teams often operate multiple social media at the same time. KK-DATA supports detection of multiple platforms (Telegram, WhatsApp, Line, Zalo, etc.) in the same task. For example:

1. First generate a batch of global numbers and submit them to Telegram for gender detection (including age field)
2. Submit WhatsApp activation/activity check again for the same batch of numbers
3. After exporting the results, filter out users who have activated TG and WA at the same time, are about 30 years old, and have been active recently**
4. This group of users are typical “high-value leads” and can be reached through private messages or social groups on multiple platforms.

Scenario 3: Combined with CRM to achieve hierarchical user reach

Import the exported tg 30-year-old data into a CRM or CDP platform and label it with “age tag” and “active tag”. When doing automated marketing later, you can set rules:

• When the user is 25–35 years old and active in the last 7 days → Send Category A coupons
• When the user’s age is < 25 → send Category B content (games, entertainment)
• When user age > 35 → Send category C content (education, health)

This stratification is more accurate than purely by gender or region, and typically improves conversion rates by 15%–30% (based on industry experience).

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How does LLM assist in analyzing tg 30-year-old data?

LLM (Large Language Model, such as GPT-4, Claude) can become an analysis assistant for tg 30-year-old data, but it cannot independently obtain real-time user data. You need to first obtain the data set through the screening platform, and then submit it to LLM for interpretation.

Use LLM for data interpretation and crowd insights

Suppose you export a dataset containing 100,000 TG gender test results. You can paste a brief statistical summary (such as age distribution, gender ratio, number of countries, activity rate) into the LLM conversation and ask:

“This data comes from TG users in Southeast Asia. The age range is concentrated between 25 and 40 years old, with users around 30 years old accounting for the highest proportion. Please help me analyze the overseas consumer categories that this group of people may pay attention to, and generate 5 private messages with a tone suitable for business cooperation.”

LLM will output an executable content strategy based on public industry knowledge. Note: Do not enter all original numbers directly to avoid data leakage. Only enter aggregated information at the statistical level.

Import tg 30 data into a CRM or analytics tool

LLM can also assist in writing SQL or Python scripts for automating the processing of age fields in CRM. For example:

“Write me a Python code that reads a CSV file containing the ‘age’ column, filters out rows with ages between 25–35 and ‘is_active’ is True, and exports it as a new CSV.”

LLM will generate code snippets that you can copy and run. However, it needs to be emphasized that data acquisition must rely on the screening platform. LLM is not a data source, but an analysis tool.

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Common misunderstandings and precautions when obtaining tg 30-year-old data

Misunderstanding 1: Thinking that tg’s 30-year-old data is an accurate age

Fact: The age field is an estimate and only reflects the approximate age range. Don’t use “just 30 years old” as a precise filter. It is recommended to use a range (such as 25–35) instead.

Misunderstanding 2: Only rely on the age field and ignore other dimensions

Suggestion: Combine age with gender, activity level, and multi-platform activation status. For example, a 30-year-old man who has both TG and WA is better suited to promote IT tools than a 30-year-old woman.

Misunderstanding 3: Thinking that LLM can directly obtain data

Reminder: LLM cannot access Telegram real-time data, nor can it generate real user lists. Data acquisition must go through the Sieve Number platform, and LLM is only used to analyze the exported results.

Notes

• Balance Management: KK-DATA provides data deduplication warehouse, which automatically deduplicates cross-task numbers to avoid repeated deductions.
• Compliance Boundary: Do not use the age field for discriminatory targeting (such as denying service to users of a certain age), comply with local laws.
• Verification method: If you doubt the accuracy of the age field, you can sample a small range (for example, select 50 numbers and manually verify the age information in the user profile) to evaluate the error.

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FAQ

**Q: Is the TG 30-year-old data accurate? ** A: This data comes from the age field in Telegram’s gender detection results. It is an estimate and can be used to filter people around 30 years old, but it cannot be used as the actual date of birth.

**Q: How much does it cost to obtain tg 30 year old data? ** Answer: Billing is per item, and the unit price for each detection type (activated, active, gender) is different. See the real-time price on the console for details. KK-DATA has no subscription package, you pay as you use, and the minimum recharge is 50 USDT.

**Q: Can LLM directly obtain tg 30-year-old data? ** Answer: No. LLM cannot obtain real-time user data independently. The data needs to be obtained through a screening platform (such as KK-DATA) and then handed over to LLM for analysis and interpretation.

**Q: Can I check only the age field separately? ** Answer: The age field is included in the Telegram gender detection results and does not support separate query. The specific fields are subject to the console export results.

**Q: How to avoid wasting balance through repeated testing? ** Answer: KK-DATA provides a data deduplication warehouse, which can automatically deduplicate cross-task numbers to avoid repeated deductions. The system will also prompt you with an estimated cost before submitting the task.

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The above is a complete guide from obtaining tg 30-year-old data to combining it with LLM application. If you are ready to start screening numbers, welcome to experience KK-DATA’s smooth console process:

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot

More documentation and billing instructions:

• Official website: https://kkdata.cc/
• Usage documentation: https://docs.kkdata.cc/

If you have any questions, please feel free to contact us through customer service.