tg 30-year-old data 10 questions 10 answers: From detection principles to practical FAQs

When acquiring customers on Telegram, do you want to accurately screen out target users around the age of 30? Many people have heard of “tg 30-year-old data”, but don’t know what it is, where it comes from, and how to use it. This article uses 10 FAQ-style questions and answers to help you thoroughly understand the age field in Telegram gender detection - it is not an independent product, but a statistical age value in the gender detection results, which can be used to target people around 30 years old in batches. The following will cover data sources, accuracy descriptions, actual combat scenarios, operating procedures, costs, common misunderstandings, etc. You can get started after reading this.

1. What is tg 30-year-old data?

1.1 Data source: Where does the age field come from?

The age field does not come from official real-name authentication, but is inferred based on multiple signals:

• User public information (such as age information that may be hidden in Telegram nicknames, avatars, and signatures)
• Behavioral patterns (online time, frequency of interactions, group preferences, etc.)
• Associate social accounts (such as binding public data on other platforms corresponding to mobile phone numbers)

After these signals are integrated by the algorithm, an estimated age value (such as 30, 32, etc. integers) is output.

1.2 Accuracy description: Why is it not ID card level accuracy?

• No third party can identify the true age with 100% accuracy, and Telegram does not provide an official age query interface.
• The accuracy of the age field is usually between 60% and 80% (specifically varies by region and completeness of account information).
• For batch marketing or profiling analysis, statistical accuracy is sufficient to guide the delivery direction, but it cannot be used in scenarios that require absolute accuracy such as age verification and risk control review**.

2. What can tg 30-year-old data be used for?

2.1 Scenario 1: Acquiring customers through private messages targeting age groups

Suppose you promote a financial management tool aimed at men over 30 years old. You can filter for “gender = male” and “age ≈ 30 (for example, 28-32)” at the same time when filtering numbers, and then import this batch of numbers into the private message sequence. Coupled with recent activity detection, the conversion rate after contact can be greatly improved.

2.2 Scenario 2: Community hierarchical operation and content adaptation

After grouping users by age group, you can publish differentiated content in different groups:

• 20–25 years old: games, entertainment, job hunting
• Around 30 years old: career, financial management, childcare, garage
• Over 40 years old: health, investment, family

Pull the selected 30-year-old people into the exclusive community and push customized content to improve retention and interaction.

2.3 Scenario 3: Assisting in user portrait analysis

If you already have a batch of numbers (such as the mobile phone numbers of old customers), you can quickly understand the age distribution of the user group through the age field in gender detection. If it is found that people aged around 30 account for the highest proportion, subsequent promotion strategies can be developed around this group to reduce blind casting of the net.

3. How to obtain tg 30-year-old data?

To operate in the KK-DATA console, you only need three steps:

1. Prepare number list: Upload or use the global number generation module to create a batch of numbers. Supports CSV and TXT formats, up to about 1 million items.
2. Submit Screening Task: Select the detection type as “Telegram Gender Detection” (this type includes gender and age fields). The system will display the estimated cost (billed by item, please see the real-time price on the console for specific unit prices).
3. Wait for completion and export: After the task is completed, click Export in the “Task List”. The CSV file will contain the age column, which allows you to filter values ​​around 30 years old directly in Excel.

💡 If you only want to filter by age without gender, select “Gender Detection” and ignore the gender column when exporting.

4. Is the tg 30-year-old data accurate?

Frankly speaking: It is impossible to achieve ID card level accuracy. But please think about your actual needs:

• If you need “absolute accuracy in every detail” - no third-party service can currently do that.
• If you need to “select a group of people with a high probability of being around 30 years old among 100,000 numbers” - the age field is fully capable.

Effective vs Accurate:

• “Accurate” means that each value truly corresponds to the individual.
• “Effective” means that it can significantly improve the hit rate of the target population.

As long as the conversion rate increases significantly after using the age field filter in your A/B test, the data is “valid”. It is recommended to test with a small amount first (for example, 1,000 items), and then expand investment after verifying the effect.

5. How much does it cost to screen tg 30-year-old data?

• Billing Mode: Charge by item, no subscription package. Use it after depositing USDT (TRC20). The minimum deposit is about 50 USDT.
• Unit price: The unit price of Telegram gender detection (including age) is different from the separate activation detection. For specific figures, please see the real-time price of the console. The estimated deduction amount will be displayed before the task is submitted.
• Cost Example: Assume that 100,000 numbers are detected, the age field is an incidental field for gender detection, and only one detection fee is charged. Actual cost = 100,000 × unit price (yuan/item). Compared with manual verification one by one, automated screening is more efficient and the cost is controllable.

Note: If the data deduplication function is turned on, duplicate numbers will only be deducted once to avoid waste.

6. How to use tg 30-year-old data and tg active data together?

Most marketing scenarios not only require age appropriateness, but also want the number to be recently active. KK-DATA supports multiple detections of the same number list. The process is as follows:

1. Do gender detection first: Screen out numbers that are about 30 years old and whose gender meets the target.
2. Export the results and take the “valid and age-appropriate” number.
3. Resubmit active detection: For this batch of numbers, select “Telegram active detection” (the active window can be specified, such as the last 7 days, the last 30 days).
4. Merge results: After exporting CSV, use Excel to match the mobile phone number to get the age and active status at the same time.

The “age + activity” double screening formed in this way can significantly increase the response rate after private messages are reached.

7. Common misunderstandings and precautions

Here are the 5 most common misconceptions:

Misunderstanding	Correct understanding
It is believed that the age field can be accurate to the birthday	The age is an estimate (such as 30), without month and day
Thought “tg 30-year-old data” is an independent product	It is an incidental field in gender detection and is not sold separately
Ignore data deduplication, resulting in repeated deductions	Submitting the same number multiple times will result in repeated billing, deduplicating the warehouse can save costs
Use statistical-level data for identity verification	Cannot be used in strict scenarios such as finance, real-name, etc.
It is believed that the recognition rate is the same in all countries	English-speaking countries have richer data, and the recognition rate in some small language areas may be low

8. The correct process for batch screening tg 30-year-old data

8.1 Step 1: Prepare number list

• Generate numbers: Using KK-DATA’s global number generation module, you can specify the country and number segment and generate a large number of numbers for free.
• Upload your own number: Import CSV/TXT file.
• Quantity Recommendation: It is recommended that 10,000-100,000 items be sent for each task, with a maximum of 1 million items at a time.

8.2 Step 2: Data deduplication (the key to saving money)

Import numbers in the “Duplication Warehouse” of the console, and the system will mark the detected numbers. When submitting subsequent number screening tasks, select “Skip the detected numbers” to avoid repeated deductions. For long-term marketing teams, this step can save up to 30%–50%.

8.3 Step 3: Submit the screening task and export the results

1. Click “New Task” → select “Telegram Gender Detection”.
2. Upload the number list (or directly select the list in the deduplication warehouse).
3. Submit the task after confirming the estimated cost.
4. After the task is completed, export the CSV. Filter the age column in Excel and set the interval (for example, 28–32) to obtain “tg 30-year-old data”.

If multiple rounds of screening (age → active) are needed, just repeat the above steps.

9. What if we need tg data for other age groups?

The age field is a continuous value (such as integers such as 30, 25, 38, etc.). Once exported, you can use Excel’s custom filtering capabilities to handle any age group:

• 20–25 years old: filter age ≥ 20 and age ≤ 25
• 35–40: filter age ≥ 35 and age ≤ 40
• 50+: filter age ≥ 50

KK-DATA does not limit how you use it after exporting, nor does it charge extra. You can even group them by age and use them separately for different marketing campaigns.

10. Which overseas scenarios require tg 30-year-old data the most?

The 30-year-old user group usually has the following characteristics and is therefore most valuable in the following industries:

Industry	Demand	Characteristics of the 30-year-old group
Cross-border e-commerce (independent website)	Men’s clothing/electronics/home furnishings	Rising consumption power and family demand
Game promotion (medium to severe)	SLG, MMO	Have stable income and be willing to pay for interest
Fintech	Loans, financial management, insurance	Facing pressures such as buying a house, educating children, etc.
Dating and social networking	Marriage and love platform	Marriageable age, high matching degree
Online education	Vocational skills, MBA	Workplace improvement period

If your target user profile happens to include “25-35 years old, with medium to high spending power, and uses Telegram”, then the tg age field is your accurate amplifier.

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FAQ

**Q: Can tg’s 30-year-old data be accurate to the specific birthday? ** Answer: No. The age field in tg gender detection provides an estimated age (for example, “30”, “28-32”, etc.), which cannot be accurate to the month and day. Recommended for crowd targeting, not authentication.

**Q: How do you know if the TG 30-year-olds are active after being screened? ** Answer: You can first filter out numbers with appropriate age, then initiate a “tg activity detection” task (select the same number list), and merge the activity status field in the export results. KK-DATA supports multiple detections of the same list.

**Q: I only have a few thousand numbers, can I still use tg 30-year-old data to filter? ** Answer: Yes. A single task supports at least dozens of numbers (no lower limit), and is billed based on the number of detected numbers. It is also applicable to small scales.

**Q: What do the fields look like after exporting tg 30-year-old data? ** Answer: The exported CSV usually contains columns such as “phone”, “tg_on”, “active”, “gender”, and “age”. The “age” column is the age value and can be filtered directly in Excel.

**Q: Does the age field of tg 30-year-old data apply to all countries? ** Answer: The recognition rate of the age field may vary in different countries/regions. It is recommended to test with a small number of numbers in the target country first, and then use it in batches after confirming that it is effective.

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If you are looking for a tool to efficiently obtain tg 30-year-old data, KK-DATA provides you with one-click detection, automatic deduplication, and multi-format export. No subscription required, pay per item, recharge as much as you use.

👉Log in to the console to start screening numbers 👉 Two-way contact customer service: https://t.me/kkdata_robot 👉 Reference document: https://docs.kkdata.cc/ 👉 Official website: https://kkdata.cc/