B2B SaaS goes overseas to acquire customers in the United States: How to use U.S. data to accurately define ICP and screen high-value leads?

#B2B SaaS going overseas to acquire customers in the United States: How to use U.S. data to accurately define ICP and screen high-value leads?

Customer acquisition teams often make a mistake: they send out a batch of “US number data” in bulk, resulting in an open rate of less than 3%. The reason is simple - data itself has no value, only matching ICP has value.
This article focuses on the B2B SaaS overseas scenario, from defining ICP to hierarchical detection of US data, and provides an implementable four-step screening system to help you turn “US number data” into “high conversion leads”.

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Why does B2B SaaS have to define ICP before obtaining US data when going overseas to the United States?

The ideal customer profile (ICP) is the yardstick for customer acquisition. Without it, you’re faced with millions of unrelated U.S. number data.
For example: If your product is HR SaaS, your target customers are HR VPs at 100–500-person technology companies in the United States. The target group of the activity is very small, but through the age field in Telegram’s gender detection and activity filtering, invalid numbers can be reduced by more than 80%.

On the contrary, if the numbers are directly sent in bulk without any filtering, the cost will be high, the conversion will be poor, and the platform risk control will be triggered. U.S. data, as material, must first match the ICP field before it is truly usable.

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What core fields do you need to pay attention to when defining US ICP?

Industry and position dimensions

Target industries (e.g. IT Services, Fintech, Logistics) and specific job titles (CTO, Marketing Director) for B2B SaaS are not directly accessible from the number.
You can indirectly use the avatar, description, and age fields in Telegram/WhatsApp gender detection to assist in judgment. For example, a user whose avatar is in a business suit style and whose age is between 35 and 50 years old is likely to be a decision-maker.
If you need more accurate job data, you can combine it with LinkedIn data (the platform needs to support this detection type).

Activity and Behavior Dimensions

“Activated” only means registration, not online in the near future.
B2B customers need to be active within a certain time window (such as within 7 days) to be easily reachable. Telegram activity detection supports specified windows (such as 1 day, 7 days, 30 days), and some platforms also provide the last online time.
One of the key filter conditions of ICP: Only keep active users and block zombie accounts.

Regional and language dimensions

The US number data must be combined with the area code segment (such as the main US number segment +1) or IP attribution detection to ensure that the target is within the country.
Some screening platforms (such as KK-DATA) provide a global number generation function. You can specify a country to generate a number segment, and then use the screening number to complete regional filtering.

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Which detection levels should be divided into when filtering US number data?

Divide testing into four levels, in ascending order of cost. B2B teams choose tiers based on budget and goals:

Level	Detection Type	Function	Cost
First level	Number activation	Filtering invalid numbers (unregistered/empty numbers)	Lowest
Second level	Recent activity	Specified time window online detection	Lower
Third level	Gender/Age/Avatar	Role of auxiliary judgment decision maker	Medium
The fourth layer	Export TGID/WSID	Used for subsequent precise contact	Lower (export function)

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What is the “four-tier testing system” of US data? (Corresponding to different filtering depths)

The first level: number activation detection

Purpose: Confirm whether the number is a valid user of the corresponding platform.
Typical scenario: You get a batch of CSV numbers. First run Telegram to activate the test and filter out unregistered numbers.
The lowest cost, usually very low cost per 10,000 items.

Second level: recent activity detection

Purpose: Filter out recently online users.
If you only do activation detection without activity detection, you will send a large number of zombie accounts that are “not used after registration”, and the reach rate will be extremely low.
Telegram activity detection can specify windows such as 7 days, 30 days, etc., and some platforms provide the last online time.

The third layer: gender/age/avatar detection

Purpose: Use public information to assist in determining user portraits.
For example, if the test result shows “male, about 30 years old, with an office scene”, you can infer that he is a potential decision-maker.
Note: Age is an estimate, not precise data. Please do not use it as the only basis for judgment.

Level 4: Export TGID/WSID

Purpose: Export the platform ID in the detection results for subsequent automated contact.
For example: After exporting Telegram ID, you can import group targeted addition or message push tools to achieve precise reach.

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How to use deduplication and pay-as-you-go billing to reduce US data customer acquisition costs?

• Data Deduplication Warehouse: All detected numbers are automatically stored in the warehouse. Subsequent new tasks with the same number will no longer be deducted. This feature can save 30%–50% of the detection budget in cases where U.S. numbers have a large data volume but a high invalidity ratio.
• Price-by-item billing without subscription: Suitable for flexible needs, no need to waste money on packages. Show estimated cost before task submission.
• Batch detection strategy: First use activation detection (lowest cost) to filter out invalid numbers; then use active detection; and finally use portrait detection.

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Scenario: How does a B2B SaaS company use US data to screen 1,000 precise leads?

Hypothetical team target: Marketing VP or CEO of a US SaaS company.

1. Get initial list: Global number generation function, generate 20,000 US numbers (random number segment + main number segment mixed).
2. First level detection: Telegram activation detection → Filter out 12,000 invalid numbers, leaving 8,000.
3. Second level detection: Telegram activity detection (within 7 days) → Filter out 4,000 inactive numbers, leaving 4,000.
4. Third level detection: Gender + age + avatar detection → Screen out users who are male, 30–50 years old, and have business-style avatars → retain 1,500 items.
5. Fourth level import: Export TGID → Push to the sales team, and then add it through the LinkedIn distribution tool or Telegram group.

Result: 1,000 final leads (some repeated filtering), the average reach rate increased from 5% to 35%.

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What are the common pitfalls when filtering US data? How to avoid it?

Common Pitfalls	Consequences	Solutions
Mistakenly believing that the user will be reached upon activation	The user has been abandoned and the open rate is low	Activity detection must be done and the window must be specified
Ignore the gender detection error	The age field is an estimate and misjudges the decision maker	Comprehensive judgment based on avatar and description, not as the only criterion
Test the entire quantity at one time	Cost is out of control	Layered testing in batches, small sample test first
Do not verify the official customer service of the platform	Be tricked into purchasing invalid data or phishing links	Contact through official channels (such as https://t.me/kkdata_robot) and do not believe in group chat ads

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FAQ

Question: How much does it cost to check US number data once?

Answer: The detection fee is calculated based on the platform and detection type. The unit prices are different for different platforms (Telegram, WhatsApp, Line, etc.) and detection levels (activated, active, gender). For specific prices, please log in to the console to view real-time prices. The estimated cost will be displayed before the task is submitted.

Q: Is the age field in the US data accurate? Can it be used to precisely target decision-makers over the age of 35?

Answer: The age field comes from the platform’s public information or algorithmic estimation, and is not ID card-level accuracy. Can be used for trend judgment (such as “about 30 years old”) and large-scale filtering, but is not recommended as the only filtering criterion. It is recommended to make a comprehensive judgment based on activity, avatar, description and other fields.

Question: I only do US B2B SaaS, which platform number should I check first?

A: It depends on your reach channel. Telegram and WhatsApp are both suitable for B2B customer acquisition. Telegram is highly active in the technology/entrepreneurial circles, while WhatsApp has wider penetration in traditional industries. If the target customer is a CTO/CEO of a technology company, priority should be given to testing Telegram activity + gender; if the target customer is a traditional industry (such as logistics, manufacturing), it is recommended to enable WhatsApp testing and then further screen.

Question: The data volume of US numbers is huge, how to avoid wasting budget by repeated detection?

Answer: It is recommended to use the data deduplication warehouse function. All detected numbers are automatically stored in the database, and the same number will not be deducted again in subsequent new tasks. In addition, it is recommended to conduct testing in batches and conduct small sample testing first before expanding.

Question: What should I do if it prompts “Insufficient balance” during the test?

Answer: Currently only USDT (TRC20) recharge is supported, with a minimum of about 50 USDT. The balance will be updated in real time after recharging. Credit cards or Alipay are not supported. It is recommended to estimate the recharge amount based on the detection volume to avoid insufficient balance affecting the task.

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After reading this article, if you are screening high-quality number data for US B2B SaaS customer acquisition, you may wish to experience it now. Log in to the console to create the first detection task, or contact customer service for a one-on-one solution.

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot More product information: https://kkdata.cc/ Usage documentation: https://docs.kkdata.cc/