TG How to accept the 30-year-old data? The studio delivers a 3-step dialogue technique and a platform to screen the truth for the “approximately 30 years old” list

In the practice of acquiring customers overseas, Telegram, as a highly active social platform, has always been the focus of community operations, private message promotion and B2B SaaS teams. When many teams purchase data, they will hear studios or suppliers provide so-called “tg 30-year-old data”. The term sounds intuitive - it seems to be a list of numbers that accurately targets users around 30 years old. But the actual situation is far more complicated than imagined. This article will break down for you the true meaning of tg’s 30-year-old data, common delivery tricks in studios, and a set of implementable acceptance methods.

What is tg 30-year-old data? An often misunderstood filter field

First, an important concept needs to be clarified: **tg 30-year-old data is not an independent “age-specific product”, nor is it an ID card-level age detection result. **

On a technical level, the Telegram platform does not directly disclose users’ precise birth dates. The so-called “age” field mainly comes from user profile analysis in Telelgram gender detection. When a user fills in birthday information (for example, “May 20, 1989”) in his or her profile, the Sieve platform reads the data through the interface and can calculate the approximate age of the user at the time of detection. This age value is usually an integer in the export file (such as 28, 32, 35).

Therefore, “tg 30-year-old data” is actually a semantic generalization. It refers to parsing the age field from the user profile through the gender detection function of the number screening platform, and then manually or scripting to filter out a list of numbers aged 25-35 (or around 30 years old). Studios often take advantage of users’ cognitive biases about “precise age” to exaggerate the accuracy of this data. In fact, it relies on the authenticity of users’ active filling.

When studios deliver tg 30-year-old data, the three most common “cheating” techniques are

After understanding the real situation, you will find that the “tg 30-year-old data” delivered by many studios on the market is flawed. Here are three of the most common techniques for reducing quality that you need to be especially vigilant about during acceptance.

Technique 1: Use “full number” to pretend to be “filtered data”

Some irresponsible studios may only do a simple “Telegram activation test” (that is, confirm whether the number is registered with Telegram), or even directly output a batch of numbers without doing any test at all and mark them as “about 30 years old”. They did not parse the gender and age fields of each number, resulting in a large number of unregistered numbers or empty user profiles (no age information) mixed in the list. This kind of “full data” seems to be large in quantity and good in price, but the actual availability rate is extremely low, causing many teams to waste their promotion costs.

Method 2: The age field is randomly assigned a value

Some studios simply do not have the ability or cost to do complete gender detection (this feature requires additional charges). In order to “satisfy” customer needs for ages, they may use a script to randomly generate an age for each number (e.g. randomly assigned between 25-35), or use completely inaccurate age data inferred from other sources. In this case, the “tg 30-year-old data” you get is actually falsely generated and cannot be verified by any review tool.

Technique 3: Ignore the activity level, resulting in a large number of zombie accounts on the list

This is the most concealed and most damaging method. The age field in “tg 30-year-old data” can filter out users whose account information shows that they are around 30 years old, but age is not the same as whether the user is currently active. A user may have signed up for Telegram five years ago and filled in their birthday, but never logged in again. If the studio only performs age screening without specifying an “active window” (such as active detection in the past 30 days, active detection in the past 7 days), the delivered list will contain a large number of zombie accounts that have not logged in for a long time. When you try to send a private message or invite to a group, the message is lost and the promotion effect is 0.

How to accept the tg 30-year-old data delivered by the studio? 3-step nuclear dialogue technique

In order not to be fooled by the above “cheating” tactics, you need a standardized acceptance process. Here are three ready-to-use nuclear dialogue techniques and steps.

Step 1: View the original exported fields and confirm that “telegram age” and “active date” are included

When communicating deliverables to studios, don’t settle for just a list of numbers. The other party is required to provide samples or complete data of the original export file (CSV or TXT format) of this screening task.

• You can explicitly write this in the acceptance requirements:

  “When delivering, please provide an export file containing the following fields: phone_number (mobile phone number), telegram_age (or similar fields, such as age, an integer representing the age value), last_active (last active time or date). We need to see the CSV file header.”

• **Why do this? ** If there is a telegram_age field in the delivery file, and the values ​​are all integers (for example, 28, 31, 33), it means that gender detection has indeed been parsed. If there is only a number, or the field name is not standardized (such as random_age), it is most likely to be forged. At the same time, the last_active field allows you to directly determine the other party’s “active window” setting (for example, if most of the most recent active times are in 2023, it means that the possibility of meeting “active in the past 30 days” is very low).

Step 2: Sampling retest and compare age hit rate

Just looking at the file header is not enough, you need to review the data itself. From the delivery list, randomly select 200 to 500 numbers and put them into a number screening platform that you trust for retesting.

• Set acceptable deviation criteria:
  • Age hit rate: After retesting, the proportion of these 200 numbers that can effectively parse the age field (for example, 70% of the numbers can return the telegram_age value). At the same time, check whether most of these age values ​​fall within the reasonable range of “about 30 years old” (for example, 25-35 years old). If the hit rate is lower than 60%, which is the norm in the industry, or the age values ​​are extremely evenly distributed (for example, all numbers are 30 years old), then the quality of this batch of data is questionable.
  • Activity compliance rate: If your requirement is “active in the past 30 days”, confirm whether the activity status is consistent with the delivery data after retesting.
• Action Words:

  “We decided to randomly check 200 numbers for retest. If the retest results show that more than 30% of the numbers do not provide an age field, or the age value is obviously inconsistent, we will refuse to accept this data.”

Step 3: Check the number of deducted test items to prevent “one order selling more than one”

This is the most easily overlooked step. If the task is submitted through the platform, each task has a clear “number of detections” and “amount consumed.” If the studio detects 100,000 pieces of data at a time but delivers it to 20 customers (each customer has a list of 50,000 pieces), this is a typical case of “one order, multiple sales.”

• Require the studio to provide task credentials: If you are using a platform such as KK-DATA, ask the studio to provide task consumption screenshots of the task or the total number of lines in the original export file. Compare the “number of items tested” claimed by the other party with the number of items on the list you received. If the difference is too large, ask for a reasonable explanation.

Why can choosing the right screening platform reduce “cutting corners” in the studio?

To eliminate the above problems, the most effective way is to control them from the source. When you choose a screening platform with complete functions and transparent data, the operating space of the studio’s “middlemen” will be greatly compressed.

A good screening platform should do the following:

• Direct export of original fields: The platform should allow users to directly export CSV files containing all metadata such as telegram_age, last_active, sex, etc. after the task is completed. This allows users to directly see the lowest-level detection results without relying on secondary processing in the studio.
• Task transparent and checkable: task status, number of detections, and consumption balance are all clearly displayed in the background. Users can create tasks with their own accounts and use the studio as an “operator”, but the data and balance are in their own hands.
• Accurate payment, pay-as-you-go: No need to subscribe to a package, recharge after use, and pay for what you use. Studios can’t “package” low-quality data.

When the studio and Party B connect TG 30-year-old data, how to write the contract terms to be more secure?

From a business perspective, it is recommended to clarify the following terms in the cooperation agreement to standardize delivery standards and avoid disputes:

• Deliverable Standard: Party B must provide a CSV export file containing original fields such as phone_number, telegram_age (integer), last_active (date), etc. Failure to provide original documents will be considered a failed delivery.
• Right of random inspection and retest: Party A has the right to randomly select 5% of the numbers from the delivery list for retesting. If the retest results show that the effective hit rate of the age field is less than 60% (or the standard agreed by both parties), Party B shall unconditionally re-screen or refund.
• Age field range definition: The definition range of “about 30 years old” must be clearly defined (for example, telegram_age field value is between 25 and 35).
• Activity Window: An activity window must be specified (e.g., the date in the last_active field is within 30 days of the task creation date). If the window is not specified, all opened accounts will be included by default, and Party A may reject it.
• Data Source Traceability: Party B needs to provide the task ID or consumption record of the screening platform used to generate data.

Complete workflow for obtaining tg 30-year-old data in batches (from generation to acceptance)

If you want to fully control data quality and avoid middlemen, you can establish a closed-loop workflow from “number source” to “acceptance”:

1. Generate target number segment: In the “Global Number Generation” module of KK-DATA, select the target country (eg: United States +1, Indonesia +62) and number segment, and generate a batch of original numbers to be tested. (This feature is free).
2. Submit Telegram screening task: Import the generated number into the screening task. In the detection type, be sure to check Gender Data Detection in “Telegram activation/active/Gender”. At the same time, set the active window according to the promotion needs (such as the past 7 days or the past 30 days).
3. Wait and Export: After the task is completed, download the CSV file with fields in the console. You will get a multi-dimensional data table containing all numbers, active status, gender, age, etc.
4. Filter the “about 30 years old” people: In Excel or Google Sheets, use the filter function to perform conditional filtering on the telegram_age column (for example: greater than or equal to 25, less than or equal to 35).
5. Form delivery standard document: Output the filtered list. It is recommended to attach a complete original CSV description file for verification.

FAQ

**Q: How accurate is the age accuracy of tg’s 30-year-old data? **

Answer: The age comes from the birthday field filled in by the user in their Telegram profile. The accuracy depends on whether the user fills it in truthfully. Generally speaking, the filling ratio of active users is relatively high, and the overall hit rate is about 60%-80% (depending on the region and cultural habits), which cannot reach the ID card level.

**Q: Why are the ages of the same person found different on different screening platforms? **

Answer: Different platforms have different strategies for obtaining the age field. Some may be based on avatar estimation or third-party data inference rather than directly reading the birthday in the data. It is recommended to use a platform that supports directly reading the birthday field of telegram data, and pay attention to its data source description.

**Q: The studio only provides a list of numbers and no age field. How can I tell if it has really been screened? **

A: The studio is required to provide the original export file (CSV) of the sieve number task, which should contain the “telegram_age” column. If the other party cannot provide it, it is most likely that the age is randomly generated by a script, or only the activation test is performed without gender detection. It is recommended to conduct random inspections by yourself.

**Q: In the tg 30-year-old data, how long is the activity level usually set? **

Answer: It is generally recommended to set it to be active in the past 30 days or the past 7 days. If the list delivered by the studio does not mark the active window, the default may be “as long as the account is opened”, which contains a large number of zombie accounts that have not logged in for a long time. The number of active days should be specified upon acceptance.

**Q: Can one KK-DATA account be used by multiple studios at the same time? **

Answer: Yes. You can assign subaccounts or API keys to different studios to avoid confusion with your main account balances. Each task is billed independently, and the export results can be viewed separately. It is recommended that the master account regularly audits the consumption and output of each sub-account.

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If you need to personally verify the tg 30-year-old data delivered by the studio, or want to generate and screen the active age groups in the target country yourself, it is recommended to use the KK-DATA platform. No subscription required, fees are deducted on a per-item basis, and the entire process is transparent. 👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot Official website details: https://kkdata.cc/ Usage documentation: https://docs.kkdata.cc/