US TG Data Quality Assessment Guide: How to judge data availability through efficiency, activity, duplication rate and field integrity

In overseas marketing, US TG data (US Telegram user number) is an important resource to reach overseas users. But faced with a list of numbers from complex sources and of varying quality, how do you judge whether a batch of data is worth investing in? After blindly recharging and screening accounts in batches, it is discovered that the efficiency is low and there are few active users. This not only wastes the budget, but may also cause the account to be reported due to frequent interruption of invalid users. This article provides practical evaluation methods and screening criteria from the four core dimensions of efficiency, activity, repetition rate, and field integrity to help you select the most usable American Telegram data at the lowest cost.

Why do we need to evaluate the quality of “US TG data”?

Many teams are accustomed to purchasing a fixed number of call lists at once and then importing them directly into the screening tool. The results often reveal:

• Waste of recharge costs: Invalid numbers account for more than 50%, and the number screening fees are all spent on empty numbers.
• Waste of screening resources: The same number appears repeatedly in different batches, and fees will be deducted for repeated testing.
• Poor reach: Even if the number is valid, the user is offline for a long time, no one responds to private messages, and is even restricted by Telegram due to large-scale sending.
• Data orientation bias: The gender and age fields are missing, making it impossible to perform refined screening, and the final promotion effect is greatly reduced.

In the “pay by item” model (such as KK-DATA’s billing method), data quality directly determines your ROI. High-quality US TG data can significantly increase the private message opening rate and community joining rate, while low-quality data means double the cost and half the effect. Therefore, before batch screening, it is a basic skill to learn to evaluate data quality.

What are the core indicators for evaluating US tg data?

The following four indicators cover the entire link from “whether the number exists” to “whether it can be targeted and reached.” You can think of them as a “checklist” for data quality.

Efficient

Definition: The ratio of registered Telegram account numbers to the total number submitted.
Business significance: Effective numbers are the user base you can reach. If the efficiency is lower than 30%, it means that most of this batch of data is empty or unregistered TG, and it is not worth investing in full screening of numbers. Common efficient reference ranges: Randomly generated numbers are often less than 10%; pre-screened or second-hand data may reach 60% to 80%.

Activity

Definition: The proportion of valid numbers with online behavior within a specified time window (such as 7 days, 30 days).
Business Significance: Valid does not necessarily mean active. A user may have registered six months ago and never come online, or may have been abandoned. Activity determines whether your private messages are seen and whether community invitations are responded to. For interactive promotion (such as private messages and community recruitment), activity is more important than efficiency. Generally, the 7-day online ratio below 30% is considered low.

Repeat rate

Definition: The proportion of duplicate numbers in the same batch (i.e. the number of duplicate numbers / the total number of numbers after deduplication).
Business Significance: Duplicate data means that you pay for multiple detections for the same number, which may also lead to repeated interruptions to the same user. The ideal repetition rate should be less than 5%. More than 10% indicates that the data source is of poor quality or highly overlaps with previous batches.

Field integrity

Definition: The filling rate of additional information fields (such as gender, age, avatar, tgid, etc.).
Business significance: The completeness of the fields determines whether you can perform targeted screening. For example, if you want to promote to users “about 30 years old men”, you need gender and age fields. But note: having data in a field does not mean that the field is accurate. The age field comes from algorithmic inference and is not ID card-level accuracy. When the field fill rate is less than 50%, the targeting effect is greatly reduced.

How to calculate the specific effectiveness and activity of a batch of US Telegram data?

The practical operation is divided into three steps: sampling → batch testing → interpretation of results.

Test sample selection and batch control

Suggested Strategy: Conduct small batch inspections first. Randomly select 500~1000 numbers from the target data source and submit a number screening task. After confirming that the efficiency and activity are up to standard, submit the full amount for testing. This can avoid excessive one-time deductions. For example, if the effectiveness of a small sample is only 20%, then the full probability is also very low, so there is no need to continue investing.

Interpret the test result table

Taking the results exported by the KK-DATA console as an example, you will see fields similar to the following:

Number	Activation	Online (7 days)	Online (30 days)	Gender	Age
+1xxx	Yes	Yes	Yes	Male	32
+1yyy	No	No	No	Empty	Empty
+1zzz	Yes	No	Yes	Female	Empty

• Activated = The number has been registered with Telegram, that is, “valid”.
  有效率 = 开通为“是”的数量 / 总样本数 × 100%
• Online (7 days) = Users who have been online in the past 7 days.
  7天活跃占比 = 在线（7天）为“是”的数量 / 总有效数 × 100%
• Online (30 days) = Users who have been online in the past 30 days.
  30天活跃占比 = 在线（30天）为“是”的数量 / 总有效数 × 100%

You can quickly calculate proportions using an Excel pivot table. Special note: If the 7-day active ratio is very low, but the 30-day active ratio is acceptable, it means that the user frequency is low, which is suitable for low-frequency notifications but not suitable for high-density interaction.

What level should the duplication rate of US TG screening data be?

Duplicate data usually occurs for the following reasons:

• There are duplicate number sources in the same batch (for example, duplication is not removed when merging from multiple channels).
• The numbers between different batches overlap (for example, some of the numbers you filtered in the previous batch are imported this time).
• The number generator has the same random seed and produces the same virtual number.

It is recommended that the repetition rate be controlled within 5%. More than 10% indicates that the data source or management process needs to be optimized. In order to save balance, you can use the data deduplication warehouse - before submitting a new task, first compare the new number with the historical detected number and automatically eliminate duplicates. KK-DATA’s deduplication warehouse supports cross-task comparison, ensuring that each number is screened only once.

If your team often processes multiple batches, it is recommended to establish a deduplication whitelist mechanism: deduplicate before each screening and then submit for testing. The lower the duplication rate, the less money you’ll waste on “checked” numbers.

How does field integrity affect the ability to direct US Cable data?

Complete fields ≠ Accurate fields, but the fill rate directly affects whether targeted filtering can be done. Listed below are some of the most commonly used targeting fields and their business values:

• Gender: Male/Female. Suitable for gender-specific marketing (such as men’s games, women’s beauty).
• Age: Numeric range. For example, filter for people around 30 years old (the age field may be between 25–35).
• tgid: Telegram’s unique ID, which can be used for more advanced correlation analysis or secondary marketing.
• Avatar/Ethnicity: Only optional in some filtering tasks and used for visual orientation.

Misunderstandings in the interpretation of gender and age fields

Special clarification is needed: tg 30-year-old data refers to the age field in the gender detection results, which can be used to screen/interpret people around 30 years old, and is not ID-level accurate data. There is a bias in the age field inferred by the platform through algorithms. For example, a user who is actually 35 years old may be marked as 31 years old. So don’t ask for “complete accuracy”, but use it as a probabilistic directional tool.

What to do when there are insufficient fields?

If the field filling rate of a certain batch of data is less than 50% (for example, a large number of gender fields are empty), it is recommended:

1. Reduce dependence on fields, mainly relying on efficiency and activity for rough screening.
2. Purchase special gender/age filtering items - Some platforms support separate payment for gender and age detection. You can only add detection on valid and active numbers to save costs.
3. Consider field completeness as a bonus: Under the same efficiency and activity, give priority to batches with higher field filling rates.

What kind of US tg data is considered “usable”?

The following recommended standards are based on common overseas promotion scenarios (private messages, social recruitment). The specific values ​​are for reference only. Please adjust according to your own business.

Indicators	Recommended Standards	Description
Effectiveness	≥ 60%	Batches below 40% should be discarded or only ultra-low-cost testing done
Activity (7 days)	≥ 20% or ≥ 30% (interaction scenario)	20% is acceptable for wide-spread notifications, and more than 30% is recommended for private message interaction
Duplication rate	≤ 5%	More than 10% requires deduplication or changing the data source
Field filling rate	≥ 70% (directional scenario)	Ordinary reach can be relaxed to 50%, otherwise the directional effect will be poor

Scenario Weight Example:

• Community Recruitment: Activity > Effectiveness > Field Integrity.
• Targeted Promotion: Field Completeness > Activity > Effectiveness (the target group needs to be determined first).
• Brand Notification: Effectiveness > Activity > Field Completeness.

From evaluation to screening: How to use tools to obtain high-quality US TG data in batches?

Assessment is just the first step, ultimately you need to get quality verified numbers in bulk. Taking KK-DATA as an example, the process is as follows:

1. Global Number Generation: Select US + area code to generate or import a custom number list (free).
2. Submit screening task: Select the Telegram detection type (activation, activity, gender, etc.), and the system will return fields such as effectiveness, activity, gender/age, etc.
3. Duplicate Warehouse: New tasks automatically compare historical detected numbers to avoid repeated deductions.
4. Export results: Filter out valid and active numbers based on fields, and export CSV/TXT for the next step.

During the entire process, you only need to pay for the number of items actually detected (billing per item, no subscription package). The recharge method supports USDT (TRC20), which is anonymous and convenient. Before submitting a task, the console will display the estimated cost so you have an idea of ​​the cost.

If you have a batch of U.S. TG data to be evaluated, you might as well spend tens of dollars to do a small batch test and use the indicators in this article to evaluate the data quality. If it is qualified, then invest in full amount. If it is not qualified, give up or change the data source to avoid greater waste.

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FAQ

Question: What is the usual effectiveness and activity of US TG data?

A: Data from different sources vary greatly. The effectiveness of randomly generated or unfiltered numbers may be less than 10%; the effectiveness of pre-screened data can be as high as 60%–80%. The activity level depends on the source channel of the number, usually the proportion online within 7 days is less than 30%. It is recommended to always do small batch testing first.

Question: When evaluating US TG data, which is more important, activity or effectiveness?

Answer: It depends on the business scenario. If it is a wide-spread notification (such as announcements), efficiency is more important; if it is interactive promotion (such as private messages, community interaction), activity is more important. The ideal situation is that efficiency and activity are both high.

Question: How much repetition rate is too high?

Answer: It is recommended to control it within 5%. A repetition rate of more than 10% indicates poor quality of the data source, or a high degree of overlap between multiple batches. It is recommended to use a deduplication tool (such as KK-DATA’s deduplication warehouse) to compare across tasks before submitting the screening number.

Question: If the field integrity is not good, can the data still be used?

Answer: It can be used, but the orientation ability will be reduced. If the field fill rate is less than 50%, it is recommended to mainly rely on efficiency and activity for rough screening. If precise targeting (such as gender, age group) is required, the field fill rate should exceed 70%.

Question: Which one is more suitable for evaluating data quality, a one-time payment to purchase a phone list or a pay-per-item screening platform?

Answer: Per-item billing (such as KK-DATA) is more flexible: you can first spend a small fee to test a few hundred items to determine whether the entire batch of data is worth the investment. A one-time purchase list cannot verify the quality and the risk is concentrated. Under the pay-per-item model, evaluation costs are controllable and suitable for trial and error.

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👉Log in to the console to start screening numbers Two-way contact customer service https://t.me/kkdata_robot For more instructions, please refer to Usage Documentation