How to use US active TG to accurately acquire customers when going overseas for B2B SaaS? First define the ICP, detection level and list fields

When Chinese B2B SaaS teams go overseas, the U.S. market is an unavoidable high ground. But when you open the delivery background, you will find that the CPA of a registered user costs tens of dollars, and a large amount of traffic will not open the message at all. The truly efficient way to reach is not to buy directly, but to accurately reach your ideal customers through active TG numbers in the United States - the premise is that you must do three things right first: define ICP, understand the detection level, and make good use of the list field.

This article does not talk about theory, but directly gives methods. You will see:

Why American Telegram users are worth poaching by B2B teams
What is ICP and how it maps to phone number screening
Four tiers of US active TG detection, from provisioning to ICP matching
Execution steps of a complete screening task
Interpretation and verification methods of exported fields
3 real-life application scenarios

Why does B2B SaaS need an active US TG number when going overseas?

American Telegram user portrait and B2B value

As of 2025, Telegram has more than 80 million monthly active users in the United States, and its growth rate is particularly fast among technology practitioners and the management of small and medium-sized SaaS companies. The reason is simple: Telegram’s group, channel and bot functions are more suitable for industry communities, technical discussions, and product feedback than WhatsApp. Many B2B decision-makers (CTOs, heads of growth, product managers) maintain their own professional circles on Telegram.

This means: US Active TG Number is not only a “phone number to send messages to”, but also an entry point into the B2B decision-making chain. If you can filter out the active users in this batch of numbers and combine them with gender, age and other fields, you can improve the accuracy of cold outreach by at least one order of magnitude.

Three common misunderstandings about direct screening

Many teams buy number lists and import them directly into mass messaging tools. As a result, their accounts are either blocked or their open rates are extremely low. There are three core reasons:

Number is valid ≠ Active: People who have registered for Telegram but have not logged in for half a year will not read your private messages when they receive them.
Active ≠ ICP Match: Active users may be students, housewives, not the B2B decision-makers you want.
Unclear ICP → Waste of budget: The ideal customer profile is not defined in advance, and the filtered numbers can only be vaguely classified by “male gender”, and the effect depends entirely on luck.

The value of US active TG data is that you can stack “valid”, “active”, “gender/age”, and “custom fields” layer by layer through detection levels, and finally get a directly reachable list that complies with ICP.

What is ICP? How should the B2B SaaS overseas team be defined?

ICP (Ideal Customer Profile) is not a general “large and medium-sized enterprise”, but a set of quantifiable labels: industry, company size, position, decision-making role, usage habits, etc.

To give a real example: a sales automation tool (B2B SaaS) for cross-border e-commerce, its ICP may be:

Industry: cross-border e-commerce, independent websites, DTC brands
Company size: 10–100 people
Position: Marketing Director, Head of Growth, CEO
Decision Chain Role: Final decision maker or key influencer
Usage Habits: Active in industry Telegram communities, aged between 28–45 years old

How do these dimensions map to number screening? Key field comparison table:

ICP Dimensions	Mappable Screen Number Fields	Description
Industry (such as cross-border e-commerce)	Cannot be obtained directly through TG screen number	Requires external data sources or list sources with industry tags
Position/role	Unable to obtain directly	Can be indirectly inferred by combining gender + age + active community
Company size	Not available directly	Same as above
Age range	Age field in gender detection (approximately 30-year-olds)	Secondary filter, non-exact date of birth
Activity	Activity detection (specified time window, such as online within 30 days)	Core filter conditions
Target country	Number country (United States)	Filter by number location or region generation

Note: Telegram screen numbers cannot directly provide industry, company, and position information. You need to match the “US active TG number” with external data (such as LinkedIn list, industry community member list), or do probability filtering through gender + age + active window, combined with subsequent contact verification.

ICP definition sample template

For example: Marketing director or head of growth at a small to medium-sized SaaS company in the United States, age 30–45, using Telegram to participate in industry communities. It can correspond to the age field in gender detection for people around 30 years old, and the active window is set to within 30 days.

Detection levels of active TG in the United States: from activation to ICP matching

“Opening detection” alone is not enough to support B2B customer acquisition. You have to understand the fine level of detection and use combo punches.

Level 1: Activation detection - whether the basic verification number is registered for TG

This is the most basic number screening: detecting whether the number is a valid Telegram user (registered). Output result: activated / not activated.

Value: Exclude invalid numbers and avoid sending messages to empty numbers. But that’s about it – if the original list you get is of good quality (e.g. from an industry event registry), the open rate may be higher; if it’s from randomly generated numbers, the open rate may be less than 5%.

Level 2: Activity Detection—Lock TG users who have been online recently

Based on the activation, activity detection is added: whether the user has online behavior in the past N days (7 days, 15 days, 30 days). Output: active/inactive (with timestamp).

Value: This is the core filter for B2B customer acquisition. Only an active user can view your messages and click on links. The recommended window is 7–30 days, taking into account both reach and quantity.

Level 3: Gender and age data - screening B2B decision-making characteristics of the population

Gender and age range (not precise) are inferred from publicly available data associated with numbers. Output fields: gender (male/female), age (range of about 20–40 years old, etc.).

Value: Assists in narrowing down the target range. For example, if you want to reach male corporate decision-makers (technical or business-oriented), you can filter for male + age 30–45. Note: Age is an inferred value with limited accuracy. It is recommended to be used as a weighting factor rather than a hard condition.

Level 4: ICP field combination—constructing the final reachable target list

Combine the results from the first three tiers: US Number + Open + Active (7–30 days) + Gender Male + Age ~30–45 years old. This step is not the filter function itself, but the secondary filtering after you get the exported data. But you can use task settings (such as active window, gender detection) to eliminate unmatched ones at the screening stage.

Examples of shortlist fields: number, tgid, active timestamp, gender, age range, detection time, etc. This list can be used for subsequent private messages or community invitations.

How to create a “US TG active number” screening task?

The following is the standard process for creating tasks in the KK-DATA console (general principles apply to other screen platforms):

Determine the target country: Select the United States (USA). Note that the number origin may cover the +1 segment, but you need to confirm whether the list source contains local US numbers.
Select detection type: Check tg active + tg gender. If you need to export tgid, also check it (for subsequent deduplication or secondary matching).
Set active window: 30 days recommended. For more activity, choose 7 days.
Age range (if applicable): There will be an age field in the gender detection results. You can filter by age range after exporting, or set it in the task parameters (some platforms support pre-screening).
Submit task: The system displays the estimated deduction amount (see the real-time price on the console for details). Submit after confirmation.
Wait for completion → Export CSV/TXT: Download the results after the task is completed.

Task setting tips

It is recommended to conduct a small-scale test first (such as 1,000 items) to observe the accuracy and distribution of the exported fields, and then scale it up to tens of thousands. KK-DATA supports up to about 1 million items at a time, but hierarchical testing can reduce waste.

Interpretation of list fields: How to verify ICP matching after export?

Field mapping: export data vs ICP dimensions

The export result usually contains the following fields (taking KK-DATA as an example):

Field	Meaning	Correspondence to ICP
phone	number	unique ID
tgid	Telegram numeric ID	Can be used for deduplication or API operations
tg_active	Whether it is active (true/false)	Core condition: active
last_active_time	Last online timestamp	Verifiable active window
gender	Gender (male/female/unknown)	Match ICP character gender orientation
age	age range (e.g. 25-34)	match age requirement
reg_time	Registration time (provided by some tests)	Determine whether the account is old or new

ICP Match Verification: Sample 200 messages, send an insignificant test message (such as an industry white paper download link), and count the open rate and click rate. If the click-through rate ≥ 15%, the list is of high quality. If it’s less than 5%, the activity settings may be too loose or the ICP may be incorrectly defined.

Quality verification: Sampling verification of number authenticity and reach rate

Don’t rely on a platform’s claims of accuracy. Do a small test yourself:

Randomly select 200 numbers from the exported list
Send a non-violating welcome message (or private message) via Telegram
Statistics of the proportion of reads (blue double ticks) within 24 hours
Real active US TG users, the open rate can usually reach 20–30%

If you find that a large number of numbers cannot be reached, check whether the correct active window is selected, or there are a large number of virtual numbers in the list source itself.

Typical application scenarios of US active TG data in B2B SaaS

Scenario 1: Industry community recruitment

You meet a bunch of potential clients on LinkedIn but don’t have direct contact. Use their mobile phone numbers (commonly found in conference sign-in sheets and partner lists), use US Active TG Screening Number to screen out open and active users, and then invite them to join your Telegram industry community (such as the “Cross-Border E-Commerce Growth Guide” group). Since they are originally in the target industry, the probability of joining after being invited is significantly higher than that through random invitation.

Scenario 2: Private message targeted promotion

Target male + age 30–45 + active within a week TG users by sending a personalized private message introducing your SaaS product. Because it has passed the activity filtering, the probability of the other party seeing the message in the short term is high; gender + age improves the ICP matching, and the content can be more accurate (such as function demonstrations for technical decision-makers).

Scenario 3: Orientation activity invitation

You hold an online webinar or offline workshop, hoping to invite American B2B decision-makers. With the US Active TG Numbers list, you can quickly filter out active users and send active links. Telegram direct messages typically have 3–5x higher open rates than email invitations.

FAQ

Q: What is the difference between an active US TG number and a regular US TG number?

Answer: Ordinary numbers only verify the registration status (activated/not activated); active numbers also detect the user’s online behavior within a specified time window (such as 30 days). For B2B reach, active numbers are more likely to see your messages and avoid contacting users who have abandoned their accounts.

Q: Do I need to set an active window when filtering active TGs in the United States? How long is appropriate?

Answer: Yes. For B2B SaaS customer acquisition, 7–30 days is recommended. The smaller the window, the higher the activity but the smaller the quantity that can be obtained; it can be flexibly adjusted according to the target channel. If you want to reach high-frequency users, set it to 7 days; if you want to expand coverage, set it to 30 days.

Q: Is the “age field” in the exported data accurate? Can we accurately screen 30-year-olds?

Answer: The age field is inferred based on the public data associated with the number, which can assist in screening people “about 30 years old”, but it is not accurate at the ID card level. It is recommended to use it in combination with other fields such as gender and activity to reduce the false screening rate. You can also manually filter by age range after exporting.

Q: What is the maximum number of US TG numbers that can be processed in one screening job?

Answer: A single task supports up to about 1 million numbers. If the list exceeds this amount, it can be split into multiple tasks and repeated detection can be avoided through the data deduplication warehouse. KK-DATA also provides a global number generation module. After free generation, you can directly screen the number, which is very convenient.

Q: How to ensure that the filtered US active TG numbers are B2B relevant contacts?

Answer: First define a clear ICP (industry, position, etc.), then use gender detection + age screening to narrow the scope (e.g. male 30–50 years old, favoring technical or business roles). Note that position information cannot be obtained directly through the TG screen number and needs to be verified twice with other data sources. The most effective way is to test with a small sample first, verify the reach rate, and then use it on a large scale.

👉 Act now: Define your ICP, log in to KK-DATA Console to create a US active TG screening task; or contact customer service through two-way https://t.me/kkdata_robot to consult on the best solution.
Documentation guide: https://docs.kkdata.cc/ | Official website: https://kkdata.cc/

How to use active TG in the United States to accurately acquire customers when B2B SaaS goes overseas? First define the ICP, detection level and list fields

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