TG US data acceptance standards: file number, fields, duplication rate and timestamp detection list

After getting the Telegram US data, did you directly import the tool or start promotion? If so, you may be wasting your time and budget. The quality of a tg US data file is far more important than quantity. Acceptance is not a formality, but ensuring that your subsequent marketing actions are based on reliable data.

Many overseas teams often ignore systematic checks after purchasing or screening TG US number data. Directly using unaccepted data will result in a very low open rate of private messages, deduct multiple fees due to duplicate numbers, or even waste time on invalid numbers due to misjudgment of activity.

This article provides a complete TG US data delivery acceptance checklist, covering file quantity verification, field integrity (active/gender, etc.), duplication rate control, timestamp verification and detection type confirmation. Whether you generate it through a third-party platform or prepare your own number list, this standard can help you ensure the quality of Telegram US data and avoid ineffective waste.

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What is TG US data acceptance? Why is it more important than screening?

“Acceptance” in the tg US data procurement or screening process is not simply “getting the file and done”, but a systematic quality inspection. Screening focuses on “selecting numbers that may be valid”, while acceptance answers “whether these data really meet the needs of use.”

Many teams spend all their energy on number generation and screening, but ignore the quality verification after data delivery. The result is: 100,000 numbers have been screened, but less than 50% are actually valid and reachable. The rest are either duplicate imports, missing fields, or the active window does not meet expectations.

The difference between acceptance and traditional data cleaning

Traditional data cleaning generally refers to operations such as deleting null values, unifying formats, and deduplication, while acceptance is a comprehensive review from the perspective of “data availability.” Acceptance includes cleaning steps, but puts more emphasis on field integrity, detection type matching, and timestamp timeliness.

Comparison items	Data cleaning	Data acceptance
Goal	Correct format errors and remove invalid data	Confirm whether the data meets business requirements
Scope	Number format, null value processing	Field integrity, detection type, repetition rate, timeliness
Output	Clean data set	Basis for decision-making whether it can be put into use

What basic conditions should a qualified TG US data have?

Before delving into the acceptance steps, let’s clarify the bottom line criteria:

• Clear source of numbers: You know how these numbers were obtained or generated.
• Transparent test results: Clearly know what tests each number has undergone (activation, activity, gender, etc.).
• Fields correspond to detection types: Only if active detection is checked, there will be an active mark in the export file; if only activation detection is performed, the active field should not appear.
• Repetition rate is controllable: When importing across tasks, the proportion of duplicate numbers should be extremely low (the ideal target is less than 1%).
• Time stamp interpretable: able to distinguish between “detection time” and “last active time” without confusion.

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Number and format of files: the first threshold for acceptance

After getting the data package, the first thing is not to open the file, but to check the basic attributes of the file.

Check the number of documents. If your data volume exceeds one million levels, the platform may export it in volumes (such as tg_us_data_part1.csv to part10.csv). During acceptance, first confirm the number of files and whether the naming rules are continuous. If a volume is missing, the data will be incomplete.

Format Consistency. Confirm that all files are in the same format (CSV or TXT). The CSV format is easy to process with Excel or scripts, and the TXT usually has one number per line. If mixed formats appear, there may be a problem with the export process. Look at the first row (Header row) and make sure the field names are what you expected.

Line Limit. Sometimes there is a maximum line limit for a single file (such as 100,000 lines/file). If the data volume exceeds the limit, the platform will automatically split the volume. Merging is not required during acceptance, but it is necessary to ensure that each sub-volume is not truncated.

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Field integrity detection: Is your data “missing an arm or a leg”?

Missing fields are one of the most common quality issues with tg US data. After completing the screening, if you find that some key fields are blank, it means that they have not been tested.

List of fields that must exist: from mobile phone number to active tag

Depending on the detection type you select, basic fields include:

• Mobile phone number (Phone Number): a unique identifier, meaningless if it is missing.
• tgid: Telegram user’s unique ID, which can be used for subsequent precise operations.
• Activation status (activated/not activated): the most basic test results.
• Activity Flag (Active/Inactive or Active Days): If activity detection is checked.
• Gender (Male/Female/Unknown): If gender detection is checked.
• Age field: Inferred value from the model, usually presented in age groups (such as 25–34), used for population proportion analysis and cannot be accurate to the individual’s true age. Please do not use it the same as ID card information.
• Detection time: Helps you determine the freshness of data.

Other fields such as avatar, user name, etc. are optional based on platform support.

How to quickly identify missing fields or too many null values

Open the file with Excel or a text editor and quickly browse the first 100 rows of each column.

• Null value ratio: If a column (such as active tag) has more than 30% null values, the detection may be incomplete or the configuration is incorrect.
• Unexpected value: For example, if the proportion of “Unknown” in the “Gender” column is too high, it means that the model cannot judge. This is a normal phenomenon, but if it exceeds 50%, you need to pay attention.
• Missing Columns: Compare the list of fields you expect and check if any columns are completely missing.

If you find that fields are missing or there are too many null values, return to the screening platform to confirm the task configuration, or resubmit for supplementary testing.

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Duplication rate check: avoid paying repeatedly for the same number

Duplicate numbers not only waste detection costs, but also interfere with subsequent marketing statistics. If a number is detected three times, you have paid three times, but the business value is still once.

How ​​to evaluate the duplication rate: Use Excel’s “Delete Duplicates” function or a script to count the total number of rows vs the number of rows after deduplication, and calculate the duplication rate = (Total number of rows - Number of rows after deduplication) / Total number of rows × 100%.

Reasonable range: Within a single task, the repetition rate should be extremely low (ideally less than 1%). If it is imported across tasks, the duplication rate may be slightly higher, but it should be controlled within 3%.

The Deduplication Warehouse of the KK-DATA platform automatically filters detected numbers across tasks and controls duplication from the source. At the time of acceptance, if you have completed multiple tasks within the same platform, the repetition rate should be close to zero.

Quick processing: If you find that the duplication rate is too high, use Excel or script to remove duplications first, and then resubmit for screening. Do not use the data before deduplication directly.

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Timestamp verification: How to confirm the validity of data?

The value of tg US data decays over time. A number may have been active two weeks ago, but may have been deregistered or made inactive today.

Understand the timestamp field: Data usually contains two types of timestamps:

• Detection Time: The time when the number is scanned and detected by the platform. This time tells you “the data was collected X days ago”.
• Last active (if supported): The last time the number was active on Telegram. For example, “Active within 7 days” means that the number has been used in the past 7 days.

The difference between active window and current activity: If “active within 7 days” is set, “active” in the detection results refers to the behavioral data within 7 days, not the instant status of the detection. When accepting, you need to check whether the active window matches your promotion rhythm - if you plan to send messages within the next week, then the number “active within 30 days” may not be as effective as the number “active within 7 days”.

Judge whether the data is out of date: It is generally recommended to use detection data no more than 30 days old. For high-precision requirements (such as instant private messages), it is recommended that the detection time does not exceed 7 days.

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Detection type confirmation: What exactly did your data measure?

This is the most overlooked but most critical step in acceptance: confirming which inspection types are reflected in the data.

Open detection vs active detection vs gender detection:

Detection type	Output fields	Applicable scenarios
Activation detection	Mobile phone number, tgid, activation status	Determine whether the number is registered with Telegram
Activity detection	Activity mark, last active time	Determine whether the user has recently used Telegram
Gender detection	Gender, age (inference)	Crowd targeting, marketing accuracy improvement

If only “Enable Detection” is checked, there will be no active markers or gender information in the exported file. In the same way, if only “Activity Detection” is checked, the file will not show whether it is enabled (because the prerequisite for activity is activation).

Need to check during acceptance: What are the business goals you need?

• If the goal is mass notification, enabling detection is sufficient.
• If it is a precise private message promotion, it must be activated + active detection at the same time.
• If it is gender/age targeting, gender detection must be checked.

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tg US data acceptance summary: five inspection items make the data reliable

Consider the following five inspection items as your SOP (standard operating procedure), and check them one by one every time you get tg US data:

1. File number and format: Confirm the number of files, naming rules, format consistency, and no truncation or missing.
2. Field integrity: Check core fields (mobile phone number, tgid, activation status, active mark, gender, etc.) and check the proportion of null values.
3. Repetition rate control: Count the number before and after deduplication to ensure that the repetition rate is < 1% (single task) or < 3% (cross-tasks).
4. Timestamp verification: Clarify the detection time and active window to ensure that the data timeliness meets expectations.
5. Detection type confirmation: Check the actual checked detection type according to business requirements to avoid mismatch between fields and requirements.

This set of acceptance checklist applies to all types of Telegram data sources. If you use the KK-DATA platform for screening, all data exports follow the above standards, and the platform has a built-in deduplication warehouse to avoid double billing. One-stop completion from generation to acceptance, helping you skip the tedious steps of manual verification and focus on mining the value of data.

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FAQ

**Q: How accurate is the “age field” in TG US data? ** Answer: The age field is an inferred value from the model. It is usually presented in age groups (such as 25–34) and is used for population proportion analysis. It cannot be accurate to the true age of an individual. Please do not use it as equivalent to ID card information.

**Q: What should I do if it is found that the repetition rate is very high during acceptance? ** Answer: First, confirm whether the same number has been imported repeatedly in different tasks. KK-DATA provides a deduplication warehouse that can automatically filter detected numbers before submitting new tasks. If the file has been exported, you can use Excel or script to remove duplicates and then resubmit for screening.

**Q: What are the possible values ​​for the “active” field in the file? ** Answer: Generally divided into “active”, “inactive” or a specific number of days. For example, “Active within 7 days” means that the number has used Telegram in the past 7 days. The active window is set by you in the screening task. During acceptance, you need to check whether the window meets expectations.

**Q: Can TG US data be used for IM private message promotion? ** Answer: Yes, but you need to pay attention to the compliance requirements of each country. The active numbers selected are only the results of technical verification and cannot guarantee a 100% open rate; they must also comply with Telegram’s anti-spam policy, and it is recommended to use compliant words and frequency control.

**Q: If the gender field is not detected in the data, can it still be added? ** Answer: Yes, but you need to resubmit the screening task and check the “Gender Test” option. Previous task results do not have a gender field and cannot be supplemented afterwards. It is recommended to clarify the type of detection required during the task setup phase.

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If you want to experience the complete process from number generation, screening to data export, and ensure that tg US data acceptance standards are in place in one step, you can try the KK-DATA platform: 👉 Log in to the console to start screening numbers; if you have any questions and need to contact customer service in both directions, you can communicate directly through https://t.me/kkdata_robot. For more usage instructions, please refer to Official Documentation.