US Active TG Delivery Acceptance Checklist: Detailed explanation of file quantity, fields, duplication rate and detection type

#US Active TG Delivery Acceptance Checklist: Detailed explanation of file quantity, fields, duplication rate and detection type

What are you most afraid of when purchasing active TG data from the United States? The numbers obtained were mixed with a large number of zombie numbers, the repetition rate was extremely high, and the fields were incomplete, which led to a sharp increase in subsequent marketing costs and no improvement in conversions. This article provides a complete list of acceptance criteria. Whether you export data from the KK-DATA platform or obtain US Telegram active data from other channels, you can follow the following steps to check each item to ensure that every investment is worthwhile.

What are the US active TG data delivery acceptance criteria?

“US active tg” in business scenarios usually refers to numbers that have Telegram online behavior within the specified active window, such as “active in the past 30 days” or “active in the past 7 days”. The acceptance criteria is a set of quantifiable and reproducible checklists used to determine whether the delivered data files meet the order specifications. Establishing a standard acceptance process can effectively avoid budget waste and marketing effect deviations caused by data quality problems.

Why can’t acceptance just look at “quantity”?

Simply checking the total number is not enough. A typical counter-example: the supplier only did Telegram activation detection (that is, whether the number is registered with Telegram), but claimed to deliver active data. The activation detection can only exclude empty accounts, but cannot distinguish between zombie accounts and active users. If you only check numbers, you may receive a large number of numbers that have never been online. The acceptance list must cover dimensions such as field completeness, repetition rate, detection type, and timestamp.

The first item of acceptance: Confirm the number and format of files

After receiving the delivery data, first conduct source verification:

• File quantity: Compare the number of tasks or volumes agreed in the order to confirm whether the number of delivered files is consistent. For example, a task with 100,000 items may be split into two files with 50,000 items.
• Export format: Commonly used is CSV or TXT, you must confirm whether it contains UTF-8 encoding. Missing encoding declarations may cause garbled characters when imported into CRM or mass sending tools.
• File name information: Check whether the file name contains traceability information such as task ID, detection type (such as “active_30days”), timestamp, etc. Standardized file names facilitate archiving and review.

If the number or format of documents does not match the agreement, redelivery should be requested during the acceptance stage.

Acceptance item 2: Check data field integrity

Open the sample file (randomly select the first 100 to 200 rows) and check whether the fields are complete column by column. The following is a typical list of required fields:

Field	Description	Used for “US Active tg”
phone	International format mobile phone number (such as +1xxxxxxxxxx)	Yes, you need to confirm that the number segment matches the US area code +1
tgid	Telegram internal user ID	Optional, export tgid to facilitate subsequent API calls or group sending
active_status	Active status tag (such as active/inactive)	Core field, used to distinguish active or not
last_active_time	Last online time (timestamp)	Core field, verification active window
gender	Gender (male/female/unknown)	Optional, can be used for crowd orientation analysis
age	Age (such as 25-30)	Optional, for trend reference
detection_type	Detection type (such as “tg_active”)	Required, confirm that this detection is active rather than enabled

Common causes and countermeasures for missing fields

• Cause 1: This detection item was not checked during task configuration (for example, gender recognition was not checked).
• Cause 2: Some numbers cannot obtain tgid due to privacy settings, and this field may be empty.
• Reason 3: Age/gender data are only inferred based on public information, and there are some missing parts.

It should be agreed during acceptance: If more than 10% of the required fields are missing (or as agreed in the contract), the quality will be deemed to be unqualified. For non-required fields (such as age), the missing ratio can be appropriately relaxed.

Acceptance of the third item: Calculate number repetition rate

Duplicate numbers will cause two problems: First, it will waste the detection balance (duplicate deductions), and second, it will affect the uniqueness of the data. Sending multiple messages to the same user during marketing will cause complaints. The acceptance process is as follows:

1. Combine all deliverables into one dataset.
2. Use Excel’s UNIQUE function or Google Sheets’ =COUNTIF formula to count the number of deduplicated mobile phone number columns.
3. Duplication rate = (total number of rows − number of deduplicated rows) / total number of rows × 100%.

Acceptable Threshold: Generally less than 5% is qualified. If the duplication rate is too high, require the supplier to redelivery the duplicates. The KK-DATA platform has a built-in data deduplication warehouse function, which can automatically remove duplicates across tasks before formal acceptance to avoid repeated detection. It is recommended to explicitly enable this feature at the time of purchase.

Acceptance item 4: Check detection type and timestamp

This is the most overlooked but most critical step. Two detection results need to be distinguished:

• Telegram activation detection: only determines whether the number is registered with Telegram, and does not care whether the user is active.
• Telegram active detection: By detecting the server status, obtain the recent online time and determine whether it is within the active window.

Check the value of the detection_type column during acceptance: if it is “tg_open”, it means that only the opening test has been done; if it is “tg_active”, there should be a corresponding last_active_time column.

How to understand active windows and timestamps

The active window is set by the user in the task (for example, “active in the past 7 days”), and the platform determines it by returning the timestamp. During acceptance, randomly select 10 to 20 records and check whether last_active_time is within the agreed window (such as within 7 days before the current date). At the same time, you can search for the number through the Telegram client and observe whether its “last seen” time roughly matches the exported data (please note that privacy settings may hide the online status, for reference only).

Accurate range of gender and age fields

The gender and age fields are inferred based on public information (such as personal profile, avatar recognition) and cannot be 100% accurate. In particular, the age field is often used for group stratification rather than single individual identification. When purchasing active TG data in the United States, please do not use the age field as ID card-level information. During acceptance, the gender ratio can be calculated as a whole. For example, 60% to 70% of males are within a reasonable range; if the ratio is extreme (such as 100% males), there may be deviations.

Acceptance item 5: Sampling verification number accessibility

No matter how beautiful the laboratory data is, it still needs to be verified in a real environment. It is recommended to randomly select 100 to 500 items from the exported file (reference ratio: 500 items from 100,000 items of data), and verify them through the following methods:

• Search Verification: Search for the mobile phone number in Telegram to see if the corresponding user account can be found (some users’ privacy settings do not allow search, which is normal, but the proportion should not be too high).
• Test message: Send a non-harassing follow request or automatic reply detection to the sample number (pay attention to Telegram’s terms of service and do not operate frequently).
• Small sample detection function: The KK-DATA platform provides a “sample detection” function, which can first conduct a complete test on a small number of numbers to verify the data quality, and then decide whether to export the entire quantity.

If the sampling failure rate exceeds 10% (for example, the search cannot be performed or the user is obviously inactive), the quality of the entire batch of data is questionable.

Common acceptance misunderstandings and precautions

• Myth 1: Confusing “activation” and “active” - activation detection is a subset of active detection, and active detection must be accompanied by a timestamp.
• Misunderstanding 2: Ignore the duplication rate — When the duplication rate exceeds 5%, you should use the deduplication warehouse function or require redelivery.
• Misunderstanding 3: Thinking that the gender/age field is 100% accurate — This type of data is only suitable for group analysis and should not be used for one-to-one precision marketing.
• Misunderstanding 4: Thinking “active” equals “real-time online” - Activity is measured in a time window, for example, “active in the past 7 days” does not mean online at the moment.
• Myth 5: Not doing sampling verification — Perfect data in the laboratory may perform poorly in the real environment, and sampling verification is the last line of defense.

It is recommended that the above acceptance list be formed into a standardized SOP and be checked one by one each time the US active TG data is purchased to ensure that the data quality is traceable and reproducible.

FAQ

**Q: How long does “active” in the US active TG data mean? ** Answer: The active window is set by the user in the task (such as 7 days, 14 days, 30 days, etc.), rather than a fixed value. The acceptance must be consistent with the active window agreed upon in the order, and confirm whether the timestamp is within the window in the last_active_time column of the exported file.

**Q: The document I received contains both a mobile phone number and a tgid. Do they have to exist at the same time? ** Answer: Not required. tgid is Telegram’s internal user ID. Exporting tgid facilitates subsequent ID mass sending or API operations; if tgid is not needed, you can keep only the mobile phone number. Just check according to the agreed field list during acceptance.

**Q: Can the gender field accuracy reach 100%? ** Answer: No. Gender data is inferred based on public information (such as personal profile, avatar recognition, etc.) and is only suitable for group analysis (for example, men account for 60%) and cannot be used for one-to-one accurate judgment. The same goes for the age field.

**Q: Can I request redelivery if the repetition rate is too high? ** Answer: If the deduplication service is clearly agreed upon in the order (such as using KK-DATA’s deduplication warehouse function), the duplication rate should be lower than the agreed threshold (usually ≤5%). If the threshold is exceeded, the supplier can be asked to redeliver the duplicate delivery. It is recommended to clarify the duplication requirements when purchasing.

**Q: How to verify whether the timestamp is authentic? ** Answer: You can randomly select a small number of numbers, manually search and check the “last seen” time in Telegram, and compare it with the exported data. However, please note that some users hide their online status, and this method is only for sampling reference. A more reliable way is to use the platform’s built-in sample detection function to verify before exporting.

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If you are looking for an efficient and reliable US active TG data screening solution, you can try the KK-DATA platform: it supports custom active windows, batch deduplication, multi-field export, and adopts a per-item billing model, pay as you use, and no subscription package. You can flexibly configure detection types (activated/active/gender, etc.) in the console and preview the estimated costs before exporting. It is recommended to use the sample detection function to verify the data quality before formal purchase.

👉Log in to the console to start screening numbers Two-way contact customer service: https://t.me/kkdata_robot For more details, please refer to the official documentation: https://docs.kkdata.cc/