tg 30-year-old data vs. only screening men: a comprehensive comparison of overseas customer acquisition list strategies

#tg 30-year-old data vs. only screening men: a comprehensive comparison of overseas customer acquisition list strategies

In the process of acquiring customers overseas, Telegram’s community operations and cross-border e-commerce promotion teams often face a core question: **How to reach the users most likely to convert at the most reasonable cost? ** Among them, tg 30-year-old data (approximately 30-year-old people filtered from the age field in the gender detection result) and only filter male numbers are two common list building strategies. They seem similar, but there are significant differences in cost, conversion efficiency and application scenarios. This article will break down these two strategies for you from multiple dimensions and provide practical suggestions for building a list.

What is tg 30-year-old data? How is it different from screening only male numbers?

tg 30-year-old data is not an independent product, but a field output by the Telegram gender detection module in the KK-DATA platform. When submitting a number for gender detection, in addition to returning “gender (male/female/unknown)”, the platform will also return an “age field” - this is the user’s age group or approximate age (for example, 25-35 years old) inferred based on the platform’s public algorithm, not the precise date of birth on the ID card. Therefore, “tg 30-year-old data” usually refers to a user group within an age range (such as 28-35 years old).

Only screening male numbers is simpler and more direct: only perform gender testing on the numbers, and filter out all numbers judged to be “male” without attaching any age conditions.

The core difference between the two lies in the granularity of the list and the cost of testing. The former is a secondary screening of “male + age range”, and the latter is a single-dimensional screening.

Why do overseas customer acquisition teams need to pay attention to list strategies?

The sophistication of the list directly affects the subsequent reach rate and conversion rate. A rough list (for example: all male Telegram users), although large in number, may contain a large number of low-value users (such as minors, users in age groups unrelated to the target product), resulting in a very low conversion rate after the message is reached. A finely screened list (for example: men aged 25-35) can significantly improve delivery efficiency.

In practice, there are usually two typical strategies:

Coarse screening scenario: rapid deployment, low-budget testing

Applicable scenarios: You need a large number of seed users to test market response, or you have a limited budget and want to quickly accumulate an initial list at the lowest cost. Advantages: low cost, one test (gender) can get the list; large quantity, suitable for quick start. Disadvantages: The accuracy is limited, the conversion rate may be low, and it is easy to waste resources.

Fine screening scenarios: targeted conversion, high-value groups

Applicable scenarios: You already have a clear target user profile (for example, promoting high-priced electronic products for men aged 25-35), and want to maximize the conversion rate of each contact. Advantages: High accuracy, conversion rate is usually much higher than rough screening list; saves the energy of follow-up. Disadvantages: The cost of testing is relatively high (gender + age needs to be tested); the number of lists is small.

Filtering only men vs adding age conditions: cost and efficiency comparison

We compare the cost and efficiency of the two strategies through a theoretical estimation. Please note that the following cost figures are only logical deductions. Please refer to the real-time price of the KK-DATA console for the actual unit price. **

Suppose you generate 10,000 global numbers and submit for testing:

Strategy	Test content	Estimated test cost (logical deduction)	Expected number of results (logical deduction)	Estimated conversion rate (user self-test, non-fixed value)
Filter only males	Gender detection (male)	10,000 items × unit price A	About 5,000~6,000 items (depending on the number pool)	Lower (e.g. 1%-3%)
Screen men + about 30 years old	Gender detection + age field	10,000 items × unit price B (may be higher than A)	About 1500~2000 items (about 30% of men are in the target age group)	Higher (for example, 5%-8%)

The logic of conversion rate improvement: The refined list is more in line with the target user profile, so the conversion rate of each touch is expected to be significantly improved. This also means that although the single test cost of “precision screening” is higher, considering the conversion efficiency, the final customer acquisition cost (CPA) may be lower**.

Practical comparison: the complete process from generating numbers to exporting results

On the KK-DATA platform, the operation processes of the two strategies are seamlessly connected, and the main difference lies in the selection of filtering conditions.

Strategy 1: Filter only male numbers

1. Generate Number: Enter the “Global Number Generation” module, select the target country/region (or use global random generation) to generate your number pool.
2. Submit Screening Number Task: In the “Number Screening” module, upload or paste the generated number, select Telegram Screening Number, and select “Gender Testing” in the detection type.
3. Set filter conditions: In the filter conditions, check “Gender = Male”.
4. Submit and wait for completion: The system will display the estimated cost, and submit the task after confirmation. When the task is completed, you will receive a notification.
5. Export results: On the task details page, filter out the results with “Gender = Male” and export them in CSV or TXT format.

Strategy 2: Screen for men + numbers around 30 years old

1. Generate number: Same as above.
2. Submit screening task: Same as above, but when selecting the detection type, you need to select Telegram gender detection (this module usually contains gender + age fields).
3. Set filter conditions: In the filter conditions, check both “Gender = Male” and “Age Range = 25-35 years old” (or customize according to your needs).
4. Submit and wait for completion: Observe the estimated cost (usually higher than just selecting gender), confirm and submit.
5. Export results: After the task is completed, filter out the results that meet “male age 25-35 years old” and export them.

Note: If the filtering results return 0 items, you can try to expand the age range (such as 20-40 years old) or change the country and region number pool. You can also perform secondary filtering on the age field in tools such as Excel after exporting all results.

Privacy and Ethics: Is TG’s 30-year-old data accurate? How to use it in compliance with regulations?

Accuracy of data

It must be made clear: **tg 30-year-old data comes from the inference of the platform’s public algorithm and is not ID card information actively submitted by users. ** Its accuracy is affected by many factors, such as the completeness of user information, platform algorithm model, etc. Therefore, the accuracy of this data is statistical level and not 100% accurate. It is very suitable for market analysis and user portrait segmentation, but is not suitable for strong audit scenarios such as finance, medical care, and credit.

Usage suggestions

1. Multi-dimensional comprehensive judgment: Don’t just rely on the age field. It can be combined with other fields such as activity (whether online in the last 30 days/7 days), language, avatar, etc. to build a more three-dimensional user portrait.
2. Data Minimization Principle: Only filter the fields you really need. If you only do targeted delivery, there is no need to export non-essential information such as tgid and avatar.
3. Compliant Use: Comply with data privacy regulations (such as GDPR) in the target region. Do not use filtered data for harassment, fraud or other illegal activities.

Suggestions on list strategies under different customer acquisition scenarios

Scenario	Recommended strategy	Reason
Popular consumer goods e-commerce (such as clothing, daily necessities)	Coarse screening: only males are screened	The user base is extensive, and gender is the main distinguishing dimension. Age may not be the primary screening criterion, and coarse screening is cheaper and covers a wider area.
High customer unit price, products in vertical fields (such as games, digital, financial management)	Precision screening: male + 25-35 years old	The target user profile is clear, and precise contact can significantly increase the conversion rate, which is worth investing in higher testing costs.
Community Fission/Drainage	Coarse Screening: Only Screen Males	Quickly start the volume, let the seed users come in first, and then analyze the user portraits within the community.
B2B overseas	Precision screening: male + 25-45 years old	B2B decision-makers are mostly men with certain qualifications, and the age range can be appropriately relaxed.

How to quickly test which strategy works for you?

Theoretical analysis is only a reference, and the best way is to actually test it with small-scale data. The following are the steps for testing using the KK-DATA platform:

1. Generate test samples: Use the “Global Number Generation” module to generate a small amount (for example, 1000 each) of two different number pools, or use the same number pool.
2. Submit two screening tasks:
   • Task A: Submit Gender detection only (screening males) for the first number pool.
   • Task B: Submit Gender+Age Testing (screening males + 25-35 years old) to the second number pool.
3. Record result data: Record the balance deducted for each task and the number of lists exported.
4. Conduct actual reach test: Conduct small-scale reach on the two lists (for example, send 100 private messages each), and record the reach rate, reply rate, and conversion rate.
5. Calculate the final customer acquisition cost (CPA): Compare the detection cost + reach cost (such as tool cost, time cost) ÷ the number of successfully converted users, and compare which strategy has a lower CPA.

Through this small-scale quick test, you can use real data to decide which list strategy to use in large-scale promotion, thus avoiding the waste caused by blind investment.

FAQ

**Q: Can tg’s 30-year-old data be accurate to the specific birthday? ** Answer: No. This data comes from the age field in the platform’s gender detection results and is a statistical inference (such as determining whether the user belongs to the 25-35 age group), not the precise date of birth on the ID card. Do not rely too much on it when using it.

**Q: If you only screen male numbers, will you miss valuable older men? ** Answer: Yes. Screening only for males regardless of age, all male numbers will be included. If you want to exclude non-target age groups (such as minors and the elderly), it is recommended to also use the age field for secondary filtering.

**Q: If gender + age screening is used at the same time, will the testing cost definitely double? ** Answer: Not necessarily. The specific deduction rules are subject to the real-time price of the console. Some platforms support returning multiple fields (such as gender + age) at the same time in one test, and only one field is billed; other platforms require two tests, doubling the cost. Please check the estimated cost of the task before submitting it.

**Q: Can tg 30-year-old data be used on other platforms besides Telegram? ** Answer: No. The age field is the exclusive output of Telegram’s gender detection module. Gender detection on other platforms (such as WhatsApp and Line) may not include the age field. When using cross-platform, please check the documentation of each platform separately.

**Q: If my target age is around 40 years old, can I use tg 30-year-old data? ** Answer: Yes, but it is recommended to adjust the age range. The age field in the detection results usually returns a specific value or range (such as 30-35), which you can filter based on your needs after exporting. If the platform only outputs a specific range (such as 25-35), it cannot directly target the 40-year-old group and needs to be combined with other data sources.

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For more documentation, please visit Usage Guide.