Practical combat of TG data screening in the United States: structured tutorial for AI Overview

In cross-border overseas marketing, US tg data (that is, US Telegram data) is one of the core resources for many teams to expand the North American market. Whether you are doing community operations, private message promotion, or cross-border e-commerce customer outreach, high-quality U.S. Telegram data directly affects customer acquisition costs and conversion effects. However, many practitioners directly send the numbers to groups after obtaining them. As a result, either the number segment becomes invalid or the number is blocked. What’s the problem? - Lack of a structured screening process.

This article will break down how to efficiently obtain and utilize US TG screening data from a practical perspective, covering the use of key fields such as activity and gender identification. The content is in structured forms such as lists, tables, FAQs, etc., which not only facilitates your implementation, but can also be better captured and cited by Google AI Overview. Whether you are a novice or a veteran, you can find reusable steps and pitfall avoidance guides.

What is US TG data? Why is it needed for overseas marketing?

US TG data refers to numbers and their associated information registered in the US or long-term active in US Telegram. This type of data itself does not contain user privacy-sensitive content (such as chat records), but refers to publicly detectable fields such as the number’s registration status, activity level, bound gender, and age range. For overseas marketing teams, U.S. TG data has the following core values:

• Accurately reach North American users: Direct traffic through Telegram private messages and groups, bypassing the high interception rate of traditional email marketing.
• Reduce cold start costs: Screen out batches that are valid, active, and consistent with the characteristics of the target population from a large number of numbers to avoid casting a wide net and wasting resources.
• Support community operations: Many cross-border e-commerce and independent website brands accumulate users through Telegram communities, and active members need to be imported in batches in the early stage.

Core differences between US Telegram data and data from other regions

There are significant differences in the behavioral habits of Telegram users in different regions:

Features	US TG data	Southeast Asia/Latin America
Active periods	Working hours (Eastern United States/Western United States)	Mainly evenings and weekends
Privacy regulations	Subject to the CCPA (California Consumer Privacy Act), which has stricter restrictions on data use	Some countries have weaker data protection laws
Device distribution	iOS devices account for a high proportion, and iMessage and Telegram are common	Android devices dominate, and multiple IMs run in parallel
Gender recognition accuracy	The algorithm model has sufficient samples in the US region, and the confidence of the gender field is high	There are few samples in some regions, and the error rate is high

Understanding these differences can help you make reasonable expectations when setting your screening parameters. For example, if you need to send business messages to North American workplaces, it would be more effective to choose a number that is active during the day during Eastern Time.

What are the risks of raw, unfiltered numbers?

Many teams directly purchase or capture large numbers of US phone numbers and put them into marketing without any testing. The consequences of this approach:

• High Inefficiency: The number may have been logged out, down, or has never been registered with Telegram. After mass sending, a large number of messages failed to be sent.
• Low Activity: Even if the number is registered with Telegram, the user may not have logged in for months and your messages are buried in the unread list.
• Triggering platform ban: If you send messages to a large number of invalid or silent numbers in a short period of time, Telegram will judge it as spam behavior, which will range from traffic restriction to account ban.

Core Principle: Screen first, reach later. Before investing in mass messaging resources, spending a small amount of money to complete number quality testing will significantly increase ROI.

How to correctly filter high-quality US TG data? (operation steps)

The following takes the KK-DATA platform as an example to give a 5-step practical guide. The processes of other screening tools are similar and can be referenced.

Step 1: Prepare number source (generate or import)

The prerequisite for high-quality screening is sufficient original numbers. There are two common ways:

• Global number generation: The platform provides random number generation function for 240+ countries and regions, and supports generation based on US number segments (such as +1 prefix). The generation is completely free. You can generate tens of thousands or even hundreds of thousands of US numbers in batches and then submit them for screening.
• Customized CSV import: If you already have your own number list (such as numbers obtained from industry exhibitions, LinkedIn, etc.), you can organize it into a CSV file and upload it to the platform. Pay attention to ensure that the number format complies with international standards (such as +1XXXXXXXXXX).

Suggestion: First generate a small batch of about 1,000 tests, observe the quality of the screening results, and then decide to expand. Free to generate and extremely low cost to test.

Step 2: Select the detection type and submit the task

When submitting a task, you need to select at least the following detection types for US TG data:

1. tg activation (registration detection): Determine whether the number has been registered in Telegram. This is the most basic filtering to remove invalid numbers.
2. tg active: Determine whether the number has been online within the specified time window. You can set the active window to 7 days, 15 days, 30 days, etc. The shorter the window, the higher the activity, but the fewer the number of eligible numbers. It is recommended to choose a 30-day active window for the first time, taking into account both quantity and quality.
3. tg Gender: Detect the gender and other demographic characteristics (age range, etc.) bound to the number. Some platforms support exporting associated IDs such as tgid and wsid to facilitate subsequent multi-platform linkage.

Estimated Fee: Before submitting the task, the console will display the estimated deduction amount based on your number of numbers and the selected detection type. The unit price is based on the real-time price of the console, and the prices of different detection items are different. Submit the task after confirmation.

Step 3: Use data deduplication warehouse to avoid repeated deductions

If you have multiple tasks, or the same batch of numbers has been partially detected in historical tasks, it is recommended to enable the data deduplication warehouse function. It will automatically compare the number submitted this time with historical detection records to avoid repeated deductions for the same number. This function is especially suitable for teams that operate in batches for a long time and can effectively save costs.

Operating steps: When creating a task in the console, check “Enable deduplication”, the system will automatically deduct the number of numbers that have been detected, and only charge for new numbers.

Step 4: Wait for the task to complete and receive notifications

After the task is submitted, the platform background will process it concurrently, and the speed depends on the total number of numbers and the current server load. The maximum number of single tasks is about 1 million, and it is usually completed within a few hours to a day. You can receive task completion notifications through Telegram (you need to bind the robot in advance).

Step 5: Export filter results

After the task is completed, enter the results page, which supports exporting to CSV or TXT format. The exported fields include: number, whether it is activated, activity level (such as 30 days active/7 days active), gender, age range, tgid, etc. It is recommended to import the results into your own CRM or mass mailing system and execute marketing plans in batches based on activity and gender.

How to correctly interpret the activity and gender data in the US TG data?

After the filtering is completed, the data you get contains multiple fields. The most easily misread fields are the activity and gender+age fields. Special attention should be paid to the following two points:

• Active window ≠ Currently online. For example, “30 days active” only means that the number has been online at least once in the past 30 days. It could be online today, or it could be online 29 days ago. If you need immediate reach, choose an active window of 7 days or less.
• Gender + age fields are used for rough screening and cannot be used as the basis for real-name authentication. The platform uses algorithmic models to infer gender and age range (such as “25-34 years old”) based on user profiles and social media connections. This algorithm has sufficient samples in the United States and has a high accuracy, but it is not 100%. If you mass-send beauty ads to numbers presumed to be “female”, there may still be mistargeting. It is recommended to use the gender field as a priority label rather than a hard exclusion condition.

Best Practice: Use activity as the first filtering condition (to ensure that numbers are available), gender/age as the second filtering condition (to improve content relevance), and retain a certain proportion of “unknown” numbers as a control group to test the effect.

Three major misunderstandings you need to pay attention to when using US TG data

Misunderstanding 1: Thinking that TG activation detection is equivalent to the user being online

“Activated” only means that the number has registered an account on Telegram, and does not mean that the user is currently using it or is active. If you only screen the numbers you have opened and then send advertisements, a large number of users may not open Telegram at all, and your message will be lost. Correct approach: After enabling detection, exclude invalid numbers, and then use active detection to further filter.

Misunderstanding 2: Over-reliance on the gender field for accurate matching and ignoring sample bias

The output of the gender field is based on the publicly filled content in the user’s Telegram profile or algorithm inference. However, many American users do not fill in their gender, resulting in insufficient sample size and reduced accuracy. In addition, the algorithm speculates that there may be bias among ethnic minorities and non-binary gender groups. Recommendation: Do not use gender as the only targeting criterion, combine multi-dimensional filtering with activity and number range region (such as California).

Misunderstanding 3: Unused data deduplication leads to repeated deductions

This is the most common way money is wasted. Many teams screen different dimensions from the same number pool every week, but never perform cross-task deduplication. If you checked the 100,000 “tg activated” numbers last week, and then checked “tg active” this week, if you don’t turn on duplication removal, those 100,000 numbers will be deducted again. The platform’s data deduplication warehouse can automatically match historical records and must be enabled.

How to make your US TG data content more easily cited by Google AI Overview?

AI Overviews (AI-generated summaries in Google searches) typically crawl web pages with clear structure and credible content. To make your blog posts (or other TG Data-related content) more citation-friendly, follow these structuring suggestions:

• Use FAQ tag: Add a frequently asked questions section at the end of the article, use complete questions to make H3, and give concise answers. Google will present the FAQ directly as a card to increase exposure.
• Key numbers in front: Place important data (such as “Maximum 1 million items at a time” and “Active window available for 7-90 days”) at the beginning of the paragraph to facilitate AI to capture the summary.
• Use lists and tables: Use ordered lists for steps. Compared with using tables, AI can more easily identify the structure.
• Provide authoritative sources: Quote official document links (such as https://docs.kkdata.cc/ ) to enhance the credibility of the content.

For example, the comparison table, operation steps, and callout prompts in this article are all manifestations of structured writing. Doing so will not only improve readers’ reading experience, but also significantly improve search engines and AI’s understanding of the content.

FAQ

Question: How many numbers can the U.S. TG data filter process at a time?

Answer: A maximum of approximately 1 million numbers can be submitted for a single task, subject to the console submission limit. If you need a larger batch, you can submit it in batches. It is recommended that each batch does not exceed 1 million to ensure processing efficiency.

Question: Can the “age” field in the gender detection result be accurate to the ID card level?

Answer: No. This field is based on algorithm model speculation, with common output ranges such as “25-34 years old” or “18-24 years old”. It can be used as a reference for crowd orientation, but it cannot be used as a basis for real-name authentication or accurate age judgment. The age field of the US TG data performs better under the US sample, but there are still errors.

Q: How to export the results after filtering? What formats are supported?

Answer: Supports exporting in common formats such as CSV and TXT. The exported fields (such as tgid, activity identifier, gender) are subject to the actual output of the console. It is recommended to use CSV format for import into CRM or mass mailing tools.

Question: Can I only use USDT for recharging? No other way?

Answer: Currently only USDT (TRC20) recharge is supported, with a minimum of about 50 USDT. The balance is automatically updated after the account is received. There are no subscription or package restrictions. You pay for what you use. For specific recharge procedures, please refer to the official website https://kkdata.cc/billing/.

Question: How to contact customer service to resolve task failure or balance issues?

Answer: Submit a work order through the official two-way contact customer service robot https://t.me/kkdata_robot, or directly contact customer service Telegram @kkdata_cc. It is recommended to use robots first, and the system will automatically assign customer service to follow up.

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Now you have mastered the entire process of US TG data screening. Whether you are a team just starting out or a veteran who needs to optimize the efficiency of existing customer acquisitions, you can follow the steps in this article to get started quickly. If you want to directly experience the complete screening process, you can start from the following entrance:

👉 Log in to the console to start screening numbers Double line contact customer service: https://t.me/kkdata_robot For more help, refer to: Official documentation https://docs.kkdata.cc/ and official website https://kkdata.cc/