Guide to avoiding pitfalls in TG data in the United States: 5 common mistakes in number format, detection sequence and result misreading

When acquiring overseas customers, especially Telegram community operations or private message promotion for the US market, the screening quality of US tg data directly determines the conversion cost and effect. Many teams spent a lot of time sorting out numbers and submitting number screening tasks, only to get back a bunch of “fake and valid” data and even be deducted repeatedly. What’s the problem? This article focuses on five common misunderstandings such as number format, detection sequence, result misreading, deduplication mechanism and platform risk control, and provides steps to avoid pitfalls that can be followed directly. After reading this, you will be able to avoid 80% of pitfalls and turn your US TG screening data into a usable customer pool.

What is “US tg data”? Understand basic concepts at once

Before delving into the error, first unify the understanding. US tg data, simply put, is a collection of Telegram registration/activity/gender detection results for US area code (+1) numbers. It usually comes from two links:

• Number Generation: Use the [Global Number Generation] (https://kkdata.cc/) function to batch generate random or customized numbers based on US number segments (such as 212, 310, 415, etc.).
• Number screening: Submit the generated numbers to the number screening platform to detect whether they have opened Telegram, whether they have been active recently, and the gender/age of the associated account and other information.

The final output of US Telegram Data is a list of numbers with detection tags for subsequent precise contact.

Typical application scenarios of US TG data

• Telegram community drainage for cross-border e-commerce independent stations: filter active + male (or female) users and invite targeted users.
• Localized marketing: Conduct regional promotion based on US Telegram data in New York, Los Angeles and other city segments.
• Overseas agency operations team: Verify in batches for B2B customers whether potential customers are using Telegram, saving time in manually sending verification messages.

Core indicators of high-quality US tg data

A usable US tg data should at least contain the following fields:

Field	Meaning	Decision value
Activation	Whether the number is registered with Telegram	Filter invalid numbers
Active	Whether the number has any behavior within the specified time window (such as the last 7 days or 30 days)	Filter reachable users
Gender	Gender (male/female) inferred based on nickname/behavior	Used for crowd targeting
Age	Inferred age group (such as 25-34 years old), imprecise birthday	Coarse-grained population portrait

Note: The age field is an inferred value, not ID card level data, see the analysis of error three for details.

Error 1: The number format is wrong, causing valid data to be misjudged as invalid.

This is the most common pitfall, bar none. The numbers obtained by many users come from various sources - some are copied from Excel, with spaces or dashes (such as +1 212-555-1234); some are grabbed from the web and lack the country code (such as only 2125551234); and some are directly exported from the mobile address book and include parentheses (such as (212) 555-1234).

KK-DATA’s screening number system has strict requirements on the number format: must be pure digits and start with a country code. For US numbers, it is +1 followed by 10 digits, and there cannot be any spaces, dashes, brackets or letters in the middle.

Example of correct format:+12125551234 Common error format: 2125551234 (missing +1), +1 212 555 1234 (with spaces), (212)555-1234 (with brackets)

If the “invalid” rate in your test results is abnormally high (for example, more than 90%), prioritize checking whether the number format is correct. You can test it with a known and valid American Telegram number (such as your own) to confirm that the system can correctly identify it before submitting it in batches.

Mistake 2: The detection order is confusing, confusing “activated”, “active” and “gender”

Many users, trying to save trouble, submitted all numbers at once for “full check” or “activity check”, only to find that their balances were consumed quickly and the data quality was not improved. This is because there are logical dependencies between the three detection types:

Open vs active vs gender: the sequencing logic of detection

1. Activation Test: The most basic and cheapest. Determine whether a number is registered with Telegram. If not registered, all subsequent tests will be meaningless.
2. Activity Detection: Based on the activation results, it is further determined whether the number has been connected to the Internet, sent messages, etc. in the recent past (such as the last 7 days or 30 days). Note: Activity detection must first pass activation detection, unregistered numbers cannot be judged to be active.
3. Gender Test: The most expensive and most sophisticated. Only when the number is activated and the Telegram account has public information (nickname, avatar, etc.) can the gender be inferred (age is also supported in some countries). If the number is not activated or the privacy settings are strict, the gender detection will return blank or unknown.

The correct approach is: Open first → then active → last gender. The three steps are submitted separately. In each step, only the numbers with a “yes” result in the previous step are taken to the next step to avoid wasting money on invalid numbers.

How to submit the US TG data screening task according to the correct process

1. Create a task in Console, select United States + Telegram, and check Activate for the detection type.
2. After the task is completed, export the list of numbers whose “Activation” result is “Yes”.
3. Use this list to create a new task, and check Active for the detection type (it is recommended to specify a time window such as within 7 days).
4. Export the list with “Active” as “Yes”.
5. Create a new task using the active list, and select Gender for the detection type (the age field can be obtained at the same time).
6. The final result is high-quality US tg data that can be used for targeted marketing.

Mistake 3: Misinterpretation of results - the “gender” field is not equal to the true identity

This is a psychological trap that is easily overlooked. When you see a number labeled “female” in a US tg data, it’s easy to think of “this is a white woman around 30 years old” and design your words based on this. But in fact,** gender recognition is based on AI’s inference of public information such as nicknames, avatars, and introductions. The accuracy is about 70%-90%, and the accuracy varies greatly in different countries and language environments**. For example, the username “Alex” may be male or female; if the avatar is a cat, the gender may be “Unknown”. The age field is more of an age range (such as 25-34 years old), rather than an exact age.

Do not equate the screening number result with the real identity filled in by the user. The correct usage is: use gender and age as reference dimensions for population portraits, such as “screening groups with a high proportion of males” or “exclude numbers that are obviously inactive during office hours”, rather than using them to make one-on-one accurate portraits. For scenarios that require identity verification (such as loan approval), look for other compliance methods.

Mistake 4: Ignoring data deduplication and repeating detection wastes balance

Many operators will submit the same number list multiple times in batches and across time periods. If duplication is not removed, fees will be deducted every time the same number is submitted. For example, if you screen 10,000 numbers today and use the same number to test the activity tomorrow, if the duplicates are not removed, the activation + activity fee will be deducted again for these 10,000 numbers.

KK-DATA provides a data deduplication warehouse function that can automatically detect and exclude already detected numbers across tasks. The usage steps are as follows:

1. In the “Data Deduplication” module of the console, upload the list of numbers you need to filter.
2. The system will compare it with the historical task results and mark the numbers that have been detected.
3. You can choose to only submit untested numbers, or force retest (but fees will be deducted).

In addition, if the number source is fixed (for example, obtained from the same crawler interface), it is recommended to check “Automatic deduplication” when setting up scheduled tasks, so that the system will skip the detected numbers and only process the new ones.

Mistake 5: Not understanding the account number and risk control mechanism, resulting in screen interruption or abnormal results.

Telegram itself has risk control for high-frequency detection, but from the platform side, KK-DATA has dispersed requests by cooperating with multiple proxy nodes. In most cases, users do not need to worry. However, some operations will trigger the platform’s own current limit:

• Excessive number of single task numbers: Although the console supports a maximum of about 1 million at a time, if multiple million-level tasks are submitted at the same time, it is likely to cause the detection to slow down or even the IP of some nodes to be temporarily restricted. It is recommended to split extremely large tasks into multiple subtasks of hundreds of thousands and submit them in batches.
• Submitting a task when the balance is insufficient: The fee cannot be deducted in the background, and the task will stay in the “queue” until the balance is received. It is recommended to always maintain sufficient account balance (minimum of about 50 USDT), or check the [Billing Page] (https://kkdata.cc/billing/) to confirm the unit price before submitting the task.
• Too many invalid numbers mixed: If the proportion of valid numbers is too low (for example, only 5% are activated), the system will still detect all numbers, but it will consume more balance. It is recommended to do an activation test first and filter out invalid numbers before continuing.

Summary: The correct process and checklist for TG data screening in the United States

Reverse the above five mistakes and you will find a reliable US TG data screening process:

Operation steps

1. Data preparation: Check that all numbers are in the format of +1 followed by 10 digits, without spaces or symbols.
2. Duplication Cleaning: Use the data deduplication warehouse to filter out historically detected numbers.
3. Step One Detection (Activation): Submit the task and only select “Activate” detection.
4. Export valid numbers: Download the list whose activation result is “Yes”.
5. Second Step Detection (Active): Submit with a list of valid numbers, select “Active” and specify a time window (such as 7 days).
6. Export Active Numbers: Download the list whose active result is “Yes”.
7. Step 3 Detection (Gender): Submit with active list, select “Gender” detection, and obtain the age field at the same time.
8. Result export and classification: According to gender, age group, city number and other tags, store them in your CRM or mass mailing tool.

Checklist (can be copied and used)

• All numbers have been converted to +1xxxxxxxxxx format
• Historical data has been filtered using deduplication warehouse
• Task sequence: activation → active → gender
• The number of single tasks does not exceed 500,000 (safer)
• The account balance is sufficient to pay for this task (check the estimated fee)
• Maintain reasonable expectations for gender/age field results and do not make absolute interpretations

FAQ

**Q: Can the American TG data filter be operated in the country? ** Answer: Yes. KK-DATA is a B2B SaaS platform that can be used globally by logging into the [Application Console] (https://app.kkdata.cc/). No local deployment is required, only an Internet connection and a Telegram account are required. Access is normal under the domestic network environment and there are no special restrictions.

**Q: How many numbers can be detected at one time using the screened US Telegram data? ** Answer: A maximum of approximately 1 million numbers can be submitted in a single task. It is recommended to submit in batches according to the detection type, activate first and then activate the last gender to optimize the balance. If the total number exceeds 1 million, it can be split into multiple subtasks.

**Q: Why do some numbers in the US tg data I got show “activated” but cannot send messages? ** Answer: Possible reasons: the number has only been registered but has been inactive for a long time (Telegram will limit the sending and receiving capabilities of inactive accounts); or the other party has blocked you and set privacy permissions. “Activity” detection can filter out numbers with recent behavior, and is recommended to be used in conjunction with “activation” detection.

**Q: Can the gender field in the US TG data be used for precision marketing? ** Answer: It can be used for crowd profiling and coarse-grained targeting (such as screening men/women), but please understand that it is based on behavior/name inference and is not an ID card-level accuracy. It is recommended to combine the age field for comprehensive screening to avoid one-to-one over-interpretation.

**Q: When filtering US TG data, are there any “pitfalls” suggestions? ** Answer: Three key points: 1) The number format contains +1 without spaces; 2) Activate first and then activate the last gender; 3) Be sure to use a data deduplication warehouse to avoid repeated deductions. In addition, for first-time users, it is recommended to verify the process with hundreds of test numbers, and then operate in batches after becoming familiar with it.

---

If you are acquiring customers in the US market and need to obtain high-quality US TG data and filter it efficiently, you may wish to experience the complete process for yourself. 👉 Log in to the console to start filtering numbers or use the two-way contact customer service https://t.me/kkdata_robot to get instant codes and practical answers. More documentation tutorials can be found at https://docs.kkdata.cc/.