tg How to evaluate data quality at the age of 30: A practical inspection guide for overseas customer acquisitions

In acquiring customers overseas, “tg 30-year-old data” is regarded by many operation teams as a “gold mine” for accurate reach - if a group of users aged about 30 years old who are active on Telegram are found, they can promote products or services in a targeted manner. However, during the actual launch, many teams found that the obtained lists either had a low activation rate, or the age field was largely missing, or even some numbers did not exist at all. How to evaluate tg 30-year-old data quality? What kind of list is worth investing in private message budget? This article provides a set of practical inspection guidelines from the aspects of detection logic, activity verification, gender and age field interpretation, number deduplication, etc. to help you avoid invalid contacts and waste of balances.

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What is “tg 30-year-old data”? Let’s start with detection logic

First of all, it is necessary to clarify: tg 30-year-old data is not an independent product, but the age field in the Telegram gender detection results. When we use a screening tool (such as KK-DATA) to perform “Telegram gender detection” on a batch of mobile phone numbers, the detection results will return multiple fields, including:

• tg activated/unactivated
• Number of active days (last 7 days/30 days/customized)
• Gender (Male/Female/Unknown)
• Age field (about 20 years old, about 30 years old, about 40 years old, etc., presented in range values)

The age field comes from the comprehensive inference of the user’s public information and behavioral data by the algorithm, and refers to the crowd portrait model. It is not the precise year of birth at the ID card level, but a probability estimate based on an interval of 5-10 years. Therefore, the “tg 30-year-old data” represents the algorithm’s inference that the user of this number has a high probability of being between the ages of approximately 25-35 years old, not the user’s actual year of birth. Understanding this is a prerequisite for using this field correctly.

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Why do you need to care about the quality of “tg 30-year-old data”?

If your goal is to accurately reach male/female users around the age of 30, then a low-quality list will bring three serious consequences:

Typical problems with low-quality data

• Invalid number: The number is not registered in Telegram, causing the private message to fail to be sent.
• Age field is missing: The algorithm cannot infer the age, and the field is empty in the exported results.
• Low activity: Although the number has opened Telegram, it has not logged in in the past 30 days, and the private message opening rate is extremely low.
• Number reuse: The same number is detected multiple times by multiple people, resulting in repeated deductions, and a large amount of invalid data is included in the list.

What practical benefits can a high-quality list bring?

• Increase the open rate of private messages: For numbers that have been active in the past 7 days, the open rate is usually 2-3 times higher than for fully activated accounts.
• Reduce Harassment Complaints: Accurately target 30-year-old male users who are interested and reduce blocking/reporting caused by irrelevant push.
• Increase ROI: Every penny is spent on real potential customers, and the conversion rate will naturally increase.

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How to evaluate the quality of your “tg 30 year old data” list?

The following is a set of executable evaluation checklists. It is recommended to test with a small sample (such as 500-2000 items) before official launch.

Step 1: Check the number activation rate and activity

Screening platforms usually provide two basic tests:

Detection type	Meaning	Purpose
tg activation	Check whether the number is registered with Telegram	Exclude empty numbers
tg active	Check whether the number has logged into Telegram within the specified time window	Filter real active users

Operation Suggestions:

1. First, select 10% of the samples for “tg activation” testing and calculate the activation rate. If it is lower than 50%, it means that the quality of the original list is extremely poor, and it should be screened after full testing.
2. Then perform a “tg active” test on the activated number, and select the active in the last 30 days window (can be customized as 7 days, 14 days, or 30 days). Priority will be given to numbers that have been active in the past 7 days for private messages.
3. Note: Activity detection is generally slightly more expensive than activation detection, but it is worth it in exchange for a higher conversion rate.

Step 2: Verify the readability of the gender and age fields

Check Gender (including age field) in the “Detection Type” of the screening task, and export the CSV after the task is completed. Focus on two columns:

• gender：male/female/unknown
• age_group: For example, 30-39, 20-29, or about30 and other identifiers defined by specific platforms (subject to the KK-DATA console export field).

Check Points:

• Age field filling rate: If the age field of more than 80% of the numbers is not empty, it means there are more targetable people in the list; if it is less than 30%, the age field has poor usability and it is not appropriate to use this as the main filtering condition.
• Reasonable age range: observe whether the age distribution is concentrated around 30 years old. If there are a large number of outliers such as “0-10 years old” or “70-80 years old”, it may be a misjudgment by the algorithm or the number segment itself is impure.

Please keep in mind: the age field is an algorithmic inference and cannot be regarded as an exact age. It is recommended to send content to a small number of test numbers first and observe whether the feedback is consistent with the target group.

Step 3: Confirm data deduplication status

Many operation teams obtain numbers from multiple channels (such as self-purchase, crawling, and customer consultation via ad clicks) and directly submit mixed number screening tasks, which will result in the same number being detected multiple times and repeated deductions. Using the Data Deduplication Warehouse function (provided by KK-DATA), you can automatically compare historical detected numbers and eliminate duplicates before submitting a task.

Operation Suggestions:

1. Import all existing number lists in the “Duplicate Warehouse” of the KK-DATA console.
2. Every time a new number is added, it is first cleaned through the deduplication interface, and then the number screening task is submitted.
3. After the task is completed, merge the export results with the historical database to avoid repeated detection next time.

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A practical process from number generation to screening

Taking the KK-DATA platform as an example, the complete “tg 30-year-old data” acquisition process is divided into three steps, which is suitable for teams that need to mass-produce lists from scratch.

Step A: Generate the number of the target area/number range

Enter the “Global Number Generation” module, select the target country (such as the United States, Indonesia, Vietnam), enter the number segment prefix (such as +1 202), set the number (10,000-1 million), and click Generate. You can also upload a custom CSV file in the format 国家代码,号码.

Note: Number generation is free, fees will be deducted only when submitting the number screening task. See the real-time price on the console for details.

Step B: Submit Telegram screening task (with gender and age detection)

1. On the “Screen Number Task” page, select “Telegram Screen Number”.
2. Upload the number list generated in step A (or directly reference it from the generated number pool).
3. Check the detection type:
   • Required: tg activation (exclude invalid numbers)
   • Optional: tg active (7 days or 30 days recommended)
   • Required: Gender (including age field) to obtain age data
4. Set the task name and submit. You will be notified automatically after the task is completed (Telegram notification needs to be bound in advance).

Step C: Use the data deduplication warehouse to clean up duplicate numbers

After the screening task is completed, export the CSV results. Enter the “Data Deduplication Warehouse” and import this export file. The system will automatically identify and mark duplicate records with historical data. After removing duplicates, a “pure list” is obtained. At this time, you can filter the rows in Excel that age_group is “30-39” or “about30”, and then sort by active_days to give priority to numbers that have been active in the past 30 days.

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Common misunderstandings: Several misunderstandings about tg’s 30-year-old data

• **Misunderstanding 1: tg’s 30-year-old data is the exact age. **In fact, it is the age range inferred by the algorithm, and the error may be ±5 years.
• **Myth 2: All numbers have an age field. ** Only some users can infer age, and the age field will be empty for a large number of numbers.
• **Misunderstanding 3: The accuracy of the age field is 100%. ** The accuracy of the algorithm is usually 70-90%, and there are misjudgments (such as judging a 40-year-old as a 30-year-old).
• **Misunderstanding 4: You can send it directly after getting the list. ** Even if the age is accurate, activity level, target region language, content relevance, etc. need to be considered.

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How to avoid account risks caused by data quality?

A low-quality list not only wastes budget, but may also cause your private message account to be blocked by Telegram. Here are some best practices:

1. Prioritize the use of high activity windows: Select numbers that are “active in the past 7 days”. These users read messages more often and have a low complaint rate.
2. Cooperate with the deduplication warehouse: Avoid sending to the same number repeatedly, otherwise it will easily be marked as harassment.
3. Abide by platform rules: Telegram has certain restrictions on batch private messages. It is recommended to control the amount sent every day and send them in different time periods.
4. Small sample test first: Send 200-500 items to each new list first, and observe the open rate, response rate, and complaint rate. If the complaint rate exceeds 2%, stop optimizing the list immediately.
5. Dispersed sending accounts: Use multiple Telegram accounts to send in rotation to reduce the risk of a single account.

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Summary: Incorporate quality assessment into your customer acquisition process

The quality of tg 30-year-old data will not automatically appear in front of you - it needs to be confirmed through a series of steps such as activation detection, activity screening, age field verification, deduplication and cleaning. Embedding this evaluation process into your daily customer acquisition SOP can significantly increase the conversion rate of private messages and reduce operating costs.

If you are looking for a tool that integrates number generation, multi-platform screening, gender and age detection, and data deduplication, you can consider KK-DATA. It supports the screen numbers of mainstream platforms such as Telegram, WhatsApp, Line, and Zalo, and is billed on a per-item basis without bundled packages.

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FAQ

Question: Can the detected “tg 30-year-old data” be accurate to the specific year of birth?

Answer: No. This age field comes from the algorithm’s inference and profiling analysis of public data. It provides a reference range for people around 30 years old, not precise date of birth or ID card level data. It is recommended to use it as an auxiliary filtering condition for crowd targeting, rather than the sole basis for decision-making.

Question: Why do many of the numbers I filtered out have no age field?

Answer: Not all Telegram numbers will return a value for the age field. This field may be empty when some users have not disclosed relevant data, or when the algorithm cannot infer a reliable age range. In the exported CSV file after the detection task is completed, it is normal for the age field to be empty.

Question: How to judge whether a “tg 30-year-old data” list is worth publishing?

Answer: It is recommended to evaluate from three dimensions: 1) number activation rate (can be verified by TG activation test); 2) activity level (can be used for active detection in the past 7 days, and the conversion rate is higher than full number activation); 3) coverage and consistency of gender/age fields. It is recommended to take a small amount of samples for testing first, and then decide whether to use the full amount after observing the actual conversion results.

Question: I have a list of hundreds of thousands of numbers. Can I use it directly to test people over 30 years old?

Answer: Yes. However, it is recommended to remove duplicate numbers first (to avoid repeated deductions), and then submit the Telegram number screening task with gender and age fields. After the task is completed, just filter the export results for people around 30 years old corresponding to the age field. A single submission supports a maximum of about 1 million numbers, and more than that must be submitted in batches.

Question: How much does KK-DATA’s tg 30-year-old data test cost?

Answer: Testing fees are billed on a per-item basis, and the unit prices are different for different platforms and test types. For specific prices, please log in to the console to view real-time prices, or visit the official website billing page. Use USDT (TRC20) for deposit, the minimum is about 50 USDT.

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Want to test the quality of your list right away? 👉Log in to the console to start screening numbers Have a problem or need help? Two-way contact customer service https://t.me/kkdata_robot For detailed operating documents, please visit https://docs.kkdata.cc/