A must-have for B2B SaaS overseas customers: How tg US data can help you accurately locate ICP and improve conversion rates

#B2B SaaS Essential for acquiring customers overseas: How tg US data can help you accurately locate ICP and improve conversion rate

When your B2B SaaS team sets its sights on the US market, has it ever encountered a scenario like this: spending a whole day buying a bunch of so-called “US numbers” from public groups, forums or even the black market, only to find after importing them into Telegram that half are empty numbers, one third are bots, and the rest either never reply or are not in your target industry at all? The screening capability of TG US Data can solve this problem from the source - instead of giving you a bunch of numbers, it gives you a bunch of “tagged” numbers: which ones are activated? Which ones have been active lately? Which user age and gender match your ideal customer profile? This article will start with the definition of ICP (ideal customer profile), and tell you step by step how to translate business needs into screening tasks, so that every cent of testing cost is spent wisely.

Why do B2B SaaS teams need TG US data? ——From “casting a wide net” to “precisely reaching”

There are two fatal problems with traditional customer acquisition methods:

Number quality is uncontrollable: The purchased list may contain a large number of canceled, unregistered Telegram or long-term inactive numbers, resulting in high reach costs and low response rates.
Unable to target people: Even if the number is valid, you cannot judge whether the other party is a decision-maker (related to age or position) or has the intention to use it in the near future.

The filtered TG US data can combine “US region + Telegram activation + active in the past 7 days + target gender/age” into an executable filtering condition. A typical B2B SaaS team, before contacting potential customers, can basically confirm that the other party is an individual in the United States, using Telegram, recently online, and may be a decision-maker. This is the first step in the implementation of ICP.

What is ICP? How do B2B SaaS teams define your “ideal customer persona”?

ICP (Ideal Customer Profile) is the core framework that determines who you sell products to. For B2B SaaS, ICP typically includes:

Dimensions	Example
Industry	SaaS / Technology / Fintech
Position	CEO / CTO / Product Owner
Region	United States (especially California, New York, Texas)
Company size	10-200 people
Behavioral activity	Recent social activities such as Telegram

The key point is: Business ICP must be converted into executable fields. For example, the description “Decision makers aged 30-40, North America, likely to use Telegram” is mapped in the KK-DATA screen number system to:

Country filter: US
TG activation detection: Yes
TG active detection: last 7 days
TG gender test: gender (male) + age (about 28-45 years old)

From “Business ICP” to “Screen Number Executable Field”

If ICPs include “decision-makers,” they are generally disproportionately male (depending on the industry) and age concentrated around 30 years old. You can use the age field in TG gender detection to do rough clustering. For example, first filter the age range of 25-45 years old, and then combine the gender field to refine the target group.

Common ICP definition errors and corrections

Error 1: “All US users” - too broad, high detection cost and low effective lead rate. Correction: Add “recently active” or “specific gender/age”.
Mistake 2: Ignore the activity level - only do activation detection. If the other party has not logged in for several months when sending a private message, it is equivalent to an invalid contact. Fix: Added “Active in last 7 days/30 days” filter.
Mistake 3: Not considering privacy regulations - US users are sensitive to privacy and need to pay attention to data compliance (such as the US Privacy Act) when screening. It is recommended to avoid using too precise age thresholds.

How to choose the appropriate detection level? ——From “open” to “active” to “gender and age”

KK-DATA provides multiple detection levels for Telegram, with costs increasing in order. It is recommended to combine them as needed:

Detection level	Main purpose	B2B SaaS scenario applicability
TG activation	Verify whether the number is registered with Telegram	Basic filtering, exclude invalid numbers
TG active (last 7 days/30 days/customized)	Determine whether the user has been online recently	Core selection to ensure effective reach
TG Gender	Returns fields such as gender, age, avatar, etc.	Targeting decision-makers and age groups

cost advice

If the budget is limited, give priority to the two dimensions of TG activation + TG active (last 7 days). After confirming that the number is valid and online recently, decide whether to add gender detection based on the conversion rate.

For example, for B2B SaaS such as SDK/API tools (the target users are mostly technical decision-makers), you can only activate + activate, because technical people usually use Telegram more frequently; while for products such as HR SaaS and marketing automation (which need to be targeted to specific functional roles), it is recommended to superimpose the gender and age fields.

After obtaining TG US data, how to interpret and use the key fields?

Files exported after filtering usually contain the following fields (specifically subject to console export):

phone: number
tgid: Telegram internal ID, which can be used for subsequent followers or private messages
Activation status: 0/1
Active status: last 7 days/30 days/custom window
Gender: Male/Female/Unknown
Age: integer (divisor, such as 30)
Avatar link: can assist in judging occupation or industry (use with caution, cannot be used as an absolute standard)

Correct use of age field

Important note: The age field is a probability value inferred by the model based on the user’s nickname, avatar, behavior, etc. It is not ID-level accurate data. It is suitable for group cluster analysis (for example, “people around 30 years old” ≈ 25-35 years old), but cannot be used for precise judgment of a single user. It is recommended to combine it with other fields (such as whether the avatar contains a tie, office background, etc.) for comprehensive reference.

Activity field: Key to B2B SaaS efficiency optimization

Even if a number has opened Telegram, if it has not logged in for three months, the success rate of private messages will be almost zero. After filtering using the “Active in the last 7 days” field, your message delivery rate and response rate will be significantly improved. According to operational experience, the response rate of active users after screening can be 3 to 5 times higher than that without screening.

Practical suggestions

When combining activity with gender/age fields, it is recommended to run a small batch (such as 1,000) first and observe the proportion of the target population in the results. If you find that the age field fluctuates greatly, you can appropriately relax the age range (such as adjusting from “30 years old” to “25-40 years old”) to cover more potential decision-makers.

Practical case: B2B SaaS team’s before and after comparison of using TG US data

The following cases are based on descriptions of common phenomena in the industry and are not fictitious specific companies.

Background: A B2B SaaS company that provides cross-border e-commerce ERP. The target ICP is American e-commerce sellers (company manager/operation leader), aged 25-45 years old, and uses Telegram to manage inventory discussion groups.

No screening stage: 500,000 U.S. numbers are collected through public channels and directly imported into Telegram to join groups or send private messages. Result: The empty number rate is about 35%, and the reply rate is less than 0.5%, which wastes a lot of time and message quota.

After using TG US data:

Submit 500,000 original numbers to the console for TG activation + TG active (last 7 days) detection, and get about 180,000 valid active numbers.
Superimpose TG gender detection on these 180,000 numbers, and filter out about 80,000 data with gender = Male and age field between 25-45 years old.
Finally, use the avatar field (optional) to manually check about 5% of the samples to confirm that they are mainly adults and have no obvious robot characteristics.

Effect: The response rate increases to 3%-5% (depending on the content of the private message), and the cost of obtaining effective leads is reduced by more than 60%.

Precautions and best practices when using TG US data

Compliance first: The US market is subject to the US Privacy Act and state privacy laws (such as CCPA). Do not send commercial messages to users who have not agreed to receive messages. It is recommended to combine the opt-in mechanism or only contact in the user’s public group.
Age data is probability, not fact: Do not claim “100% accurate age” in product promotions to avoid legal risks.
Task Splitting: For first time use, test with 1,000-5,000 numbers to observe the quality and cost of the results, and then expand to tens of thousands or even 1 million.
Use deduplication warehouse: KK-DATA provides cross-task number deduplication function to avoid repeated detection of the same number and waste of balance.
Follow the real-time price of the console: The unit price of each platform detection type may be adjusted. The estimated cost will be displayed before submitting the task. It is recommended to estimate before proceeding.

How to get started quickly? ——From defining ICP to submitting the first screening task

Step 1: List your ICP fields in Excel For example:

Country: US -Age: Approximately 28-45
Gender: no limit (or male)
TG active: last 7 days
Target number: 10,000 available for private messages

Step 2: Generate or upload number

Provide two methods

Generate Numbers: Use the “Global Number Generation” module in the console, select the country United States, and generate 20,000 random numbers (it is recommended to generate 20% more to allow for filtering margin).
Upload existing numbers: If you already have your own list (e.g. collected from a trade show), import directly into CSV.

Step 3: Select the detection type in the console and submit the task

Select Telegram platform
Check “Enable Testing”, “Active Testing” (specify the past 7 days), and “Gender Testing”
The system automatically displays the estimated cost
Submit after confirmation and wait for completion (usually tens of thousands of tasks are completed within a few minutes)

Step 4: Export, clean and use

Download the CSV and filter out the numbers with “Active=1 + Active=1 + Gender matching”.
Use tgid or phone for subsequent Telegram followers or private messages.

FAQ

**Q: Can tg US data filter out users “around 30 years old”? **

Answer: Yes. KK-DATA’s tg gender detection results provide an age field, which can be used to delineate people aged about 25-35 years old. Please note that this field is the result of model inference and is suitable for crowd cluster analysis and is not suitable as an accurate ID card-level standard.

**Q: What is the best level of detection for B2B SaaS teams using tgUS Data? **

Answer: It is recommended to use a combination: Prioritize tg activation + tg active (in the past 7 days) to screen out valid and recently online people; if you need to further target the decision-making layer, you can overlay the gender and age information in the tg gender field.

**Q: What is the testing price of tg US data? **

Answer: The specific unit price varies depending on the platform (Telegram/WhatsApp, etc.) and detection type (activation/active/gender), and may be adjusted. Please refer to the real-time estimated cost displayed on the console before submission. There is no subscription package, and fees are charged per item.

**Q: How to ensure that the exported TG US number data will not be repeatedly detected and wasted balance? **

Answer: KK-DATA provides a data deduplication warehouse function that can detect duplicate numbers across tasks and avoid repeated payments for the same number. It is recommended to enable the deduplication function before submitting for review each time.

**Q: Can tg US data be used for Facebook or Instagram screens? **

Answer: Yes. KK-DATA supports social screens on multiple platforms including Facebook, Instagram, LinkedIn, Line, Zalo, iMessage, etc. If your ICP includes multiple platforms, you can select the corresponding filter types to use in combination.

Now you can convert ICP definitions directly into screening tasks. 👉 Log in to the console https://app.kkdata.cc/ to start your first ICP targeted screening, or contact two-way customer service https://t.me/kkdata_robot to communicate your specific needs. For more documents and cases, please refer to https://docs.kkdata.cc/.