How to keep TG 30-year-old data fresh? ——A complete guide to preserving and rescreening age-related results

#tg How to keep 30-year-old data fresh? ——A complete guide to preserving and rescreening age-related results

When acquiring customers overseas, using tg 30-year-old data for targeted promotion has become the core strategy of many teams. But have you ever encountered this situation: The number filtered out last month was marked as “30-year-old male”, but when you sent a message this month, you found that the account has been canceled, or the other party’s active window has long been closed? What this reflects is the “freshness” problem of tg 30-year-old data - the age field is not permanently valid, and the number and user status are changing dynamically. This article will provide a practical guide from the three dimensions of data preservation principles, expiration judgment standards, and re-screening strategies to help your tg 30-year-old data continue to maintain high accuracy.

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What is tg 30 year old data?

tg 30-year-old data does not refer to an independent product, but refers to the age field** in the gender results obtained through Telegram number detection in screening platforms such as KK-DATA. When you check “tg gender” in the filtering conditions, the platform will provide detection results including gender, age group (such as about 30 years old) and other fields, and you can filter out the target group accordingly.

Common usage scenarios of tg 30-year-old data

Scene	Description
Targeted promotion to 30-year-old men	Combine the gender + age fields to screen out men around 30 years old for private domain fission or product launch
Private domain operation for 30-year-old women	Screening of 30-year-old women, suitable for promotion of beauty, parenting, and life services
Activity Test	After screening the 30-year-old group, superimpose the activity test and only retain users who have been active in the past 7 days
A/B Group Test	Segment the 30-year-old group into 25-30-year-old and 30-35-year-old groups by age group, and compare the conversion effects

Accuracy and Limitations of Age Field

• Accuracy: The age field is inferred by the algorithm based on the user’s public information (such as personal profile and birthday field when not hidden) and platform behavior signals. It is an estimate and is not an exact number of the user’s true age.
• Limitations: Some users may not fill in their birthdays or modify their information, resulting in inaccurate age inference; therefore, the age field is suitable for coarse screening and targeting, but is not suitable for compliance scenarios that require accurate age (such as financial authentication).
• Best Practice: It is recommended to use the age field in combination with activity and gender to form a fuller user portrait, rather than relying on age alone.

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Why does tg’s 30-year-old data “deteriorate”?

The fundamental reason for the “deterioration” of data is: Telegram user status is dynamic. Test results taken one month ago will be invalid due to the following three major factors:

Three major factors affecting the freshness of tg 30-year-old data

1. User cancellation or account freezing: After a user cancels their Telegram account, the number immediately becomes invalid; the monthly natural attrition rate is about 3-5%.
2. Modification of user age information: Users may modify their personal information (such as setting their birthday to a different year), causing the platform to re-infer age, and the original “30 years old” mark is no longer accurate.
3. Activity window is too narrow: Even if the number is still a Telegram user and the age field has not changed, if the activity window during detection is set to “last 7 days”, the user may no longer be active after one month, affecting subsequent contacts.

Example: A number was detected as “30 years old, Telegram activated, active in the past 7 days” on June 1, but the user had canceled his account on July 1. If you directly use June’s data for promotion, it will result in invalid sending.

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How to determine whether the tg 30-year-old data has expired?

Although there is no fixed “shelf life”, you can refer to the following expiration judgment logic based on business scenarios:

Promotion intensity	Recommended rescreening cycle	Data validity performance
High frequency (daily reach)	Every 7 days	Activity indicators decline rapidly and need to be updated frequently
Medium frequency (reached every week)	Every 15 days	The age field is relatively stable, and the activity begins to be discounted
Low frequency (reached monthly)	Every 30 days	The age field and activation status are still available, but the activity needs to be reconfirmed
Data pool reserve	Every 45 days	It is recommended to re-screen all fields for more than 45 days

Judgment method: Based on the task completion timestamp, plus the preceding churn rate estimate. It is recommended to combine revalidation with Telegram activity detection instead of relying solely on the age field.

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Three strategies to keep tg’s 30-year-old data “fresh”

Strategy 1: Regular batch re-screening (weekly/monthly plan)

Applicable scenarios: The existing tg 30-year-old data is large in scale and requires systematic maintenance.

Operating steps:

1. Export the existing tg 30-year-old number to CSV.
2. Submit Telegram screening tasks to the KK-DATA platform at a fixed period (such as every Monday).
3. Check “tg activated + tg active + tg gender” in the detection type (the age field will be automatically included).
4. Set the activity window (such as “active in the past 7 days”).
5. After the task is completed, export the results and filter the 30-year-old group again based on the latest age field.

Strategy 2: Prioritize re-screening of high-value numbers

Applicable scenarios: The budget is limited and you want to prioritize maintaining core groups.

Operating steps:

1. Filter out the confirmed active tg 30-year-old numbers from historical tasks (such as those with interactions in the past 15 days).
2. Mark these numbers with “key tags” and prioritize re-screening.
3. Mark inactive numbers as “to be verified” or abandon them directly to save costs.

Cost Control: Avoid duplicate billing through deduplicated warehouses. See KK-DATA usage documentation for details.

Strategy 3: Use the global number generation module to replenish fresh seeds

Applicable scenarios: You want to replace old data with new numbers to keep the data vital.

Operating steps:

1. Use the “Global Number Generation” function on the KK-DATA platform to generate new numbers in small batches (such as 1,000) every day.
2. Immediately submit to the Telegram screen and detect tg activation + tg gender (including age).
3. Use fresh numbers and existing numbers alternately to avoid over-reliance on old data.

Advantages: Generating numbers is completely free, and you are only charged based on the number of screening numbers. See Billing Instructions for details.

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How to perform tg 30 year old data refiltering on KK-DATA

Step 1: Export old data and remove duplicates

• Export existing tg 30 number CSV from historical tasks.
• Upload to KK-DATA’s Deduplication Warehouse to remove known invalid numbers (such as canceled or unsubscribed).

Step 2: Submit the rescreening task

1. Log in to Application Console.
2. Click “New Task” → select “Telegram Screen Number”.
3. Import the number list after deduplication.
4. Check the detection type: tg activated (required) + tg active (recommended) + tg gender (including age field).
5. Set active window: Select according to promotion needs (such as “active in the past 7 days”).
6. Click Submit, and the system will display the estimated cost (see Console Real-time Price for details).
7. Wait for the task to be completed, and you can get the completion reminder through Telegram Notification.

Step 3: Export and apply fresh data

• Once the task is completed, export the CSV results.
• Filter out people around 30 years old based on the age field.
• Combine activity fields for hierarchical labeling (such as high activity/medium activity/low activity).

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FAQ

**Q: Is the age field of tg 30-year-old data accurate? ** Answer: The age field is inferred by the platform algorithm based on user information and public signals. It is an estimate and is not accurate to the number on the ID card. It is suitable for rough screening in targeted marketing, but not suitable for compliance scenarios that require precise age (such as financial certification).

**Q: If a number is re-screened for tg 30 years old data, will there be repeated deductions? ** Answer: Yes, every time you submit a screening task (regardless of whether it is re-screened), the balance will be deducted based on the final number of tests. However, KK-DATA’s deduplication warehouse function can help you avoid repeated submissions of the same number and reduce waste.

**Q: How often is it best to rescreen tg 30-year-old data? ** Answer: It is recommended to rescreen the active status every 7-15 days for high-frequency promotions (reaching every day); for low-frequency or reserve data, the age + active combination can be rescreened once a month. For specific intervals, please refer to this article’s preservation plan.

**Q: If I only do activity testing and no age testing, can I maintain the “30 years old” mark? ** Answer: No. Activity detection only verifies whether the number is currently an active Telegram user and does not update the age field. To confirm whether the age is still around 30 years old, you must rerun the task including “tg gender” detection.

**Q: When re-screening, can I only update the age field and not detect the activity level? ** Answer: The Telegram filter on the KK-DATA platform supports checking “tg gender” (including age) separately. You can also uncheck “tg active” to save costs. But it is recommended to at least check “tg activation” to verify whether the number still exists.

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👉 Log in to the Console now to start preserving your tg 30-year-old data, or get one-on-one guidance through Two-way Contact Customer Service. For more features, please check Official Website and Documentation.