US Data Source Acceptance Rating: The Ultimate Guide to Format Completeness, Duplication Rate, Opening Rate and Data Freshness

When acquiring customers overseas, the quality of US data directly determines the rate of return on marketing investment. Many teams spend a lot of money to purchase data, but the actual effective activation rate may be less than 30%, and even a large amount of detection budget is wasted due to format errors and duplicate numbers. How to systematically assess the availability of a batch of US number data? This article provides a set of implementable four-dimensional scoring tables - from format completeness, repetition rate, opening rate to data freshness, scoring each item to help you screen out high-value sources before importing to the screening platform. Whether you use Telegram, WhatsApp or Line to acquire customers, this set of acceptance methods can help you save costs and improve efficiency.

What is the U.S. Data Source Acceptance Rating Scale? Why does the overseas team need it?

The U.S. data source acceptance score sheet is an evaluation framework composed of four dimensions: Format Completeness (whether the number and auxiliary fields meet the standards), Repetition Rate (the proportion of duplicate numbers across batches), Opening Rate (the proportion of registered/valid numbers on the target platform), Data Freshness (the time when the number was generated or last updated). Each dimension is scored based on the actual test results, and the final comprehensive score reflects the true value of the batch of data.

Why is such a rating sheet needed? Because many overseas teams directly imported the data into the number screening platform in batches after obtaining the data. As a result, they found that a large number of format errors among hundreds of thousands of numbers caused detection failures, or the duplication rate was as high as 20% or more, resulting in in vain deductions. More commonly, the “high opening rate” claimed by the source is less than 30% after actual testing, and it is suspected that it is old data from a few years ago. With the score sheet, you can quickly judge whether it is worth investing in testing costs before submitting a task to avoid pitfalls.

Acceptance dimension one: Format integrity - whether the number and auxiliary fields meet the standards

Format integrity is the first level of data acceptance. If the number itself does not comply with the E.164 standard (such as missing country code, parentheses or spaces), the screening number platform cannot correctly identify it, directly leading to detection failure or missed detection. At the same time, the lack of auxiliary fields (such as country, group ID, gender) will affect the accuracy of subsequent targeted screening.

Common errors in US number format and quick verification methods

Domestic numbers in the United States are usually 10 digits, plus the international code + 1 for a total of 11 digits. Common formatting errors include:

• Missing international code: e.g. 2125551234 without +1.
• With brackets or dashes: such as (212) 555-1234, including spaces and punctuation.
• Wrong number of digits: one more or one less digit.
• Country code is soft replaced: Some sources write the number as 0012125551234 (use 00 instead of +). Although it can be converted by tools, direct processing will increase the complexity.

Quick verification method: Use regular expressions to extract pure numbers and complete them into +1 + 10 digits. You can also use Excel’s TEXT function or Python script to preprocess. The KK-DATA platform has automatically output the E.164 format in the number generation module, but it needs to be verified by itself when importing third-party data.

How to affect the targeting filter number when the auxiliary fields (country, group, gender) are missing

• Country field missing: If you only have a US number but lack a country label, the number screening platform may automatically identify it as other regions (such as Canada), causing the orientation to fail. Especially in mixed data from multiple countries, the risk of misjudgment is higher.
• Group ID is missing: If you plan to conduct group targeted marketing (such as adding Telegram groups), the lack of group ID means that you cannot filter by group and can only detect all numbers.
• Gender field missing: The gender field is often used for female/male targeted filtering. If the source does not provide a gender label, you can only rely on subsequent activation + gender detection to obtain it, which increases the cost of detection.

Recommendation: The format integrity must reach at least 95% before proceeding to subsequent acceptance steps. Data below 90% should be considered for preprocessing or return.

Acceptance dimension two: Repetition rate - the hidden cost of repeated numbers across batches

Duplicate numbers are the most easily overlooked invisible killer. Data from different sources and from different batches of the same source are likely to overlap significantly. Each duplicate number will be deducted repeatedly during number screening and will not generate any new value.

How repetition rate directly affects customer acquisition budget

Suppose you purchase 10,000 pieces of US data, and the source claims that there is no duplication. However, after deduplication warehouse testing, it was found that the duplication rate is as high as 20%, so in fact you only have 8,000 unique numbers. If the unit price of screen numbers is X yuan/piece, the extra 2,000 yuan paid is a pure waste. To make matters worse, if you purchase from multiple sources one after another, the duplication rate may accumulate to more than 30%.

Acceptable Threshold: It is recommended that the source duplication rate be less than 5%. For data exceeding 10%, you should ask for a price reduction or provide a new number.

Use data deduplication warehouse to avoid duplicate detection

KK-DATA provides a built-in data deduplication warehouse that can automatically identify detected numbers across tasks. You only need to upload historical detection data before submitting a new task, and the system will automatically filter duplicate numbers to avoid secondary deductions.

Example of steps:

1. Enter the “Deduplication Warehouse” module in the console.
2. Upload the previously detected number list (CSV/TXT).
3. Check “Skip detected numbers” when importing new data.

Deduplicating the warehouse not only saves money, but also speeds up task execution because the system does not need to process duplicate entries.

Acceptance dimension three: activation rate - whether the number is actually available

Opening rate is the core indicator for measuring data quality. It represents whether the number is registered or activated on the target platform (such as Telegram, WhatsApp, Line). Data below a certain threshold has basically no customer acquisition value.

Reference for the opening rate range of common social platforms in U.S. data

The user bases of different platforms in the US market vary significantly, so the activation rates are also different. The following are empirical values (the actual test results shall prevail):

Platform	US number activation rate reference range	Description
Telegram	60% – 80%	Telegram users in the United States are growing rapidly and are friendly to new account segments.
WhatsApp	40% – 60%	The penetration rate of WhatsApp in the United States is relatively low, and the activation rate for some number segments may be less than 30%.
Line	15% – 30%	Line is not mainstream in the United States and mainly relies on overseas Chinese users.
iMessage	40% – 65%	Depends on whether the number segment is used by Apple mobile phone users.

Activity and gender data are advanced indicators of activation rate

Opening up is not enough. If the number is registered but has not been active for a long time (for example, no operations have been performed for more than 30 days), the success rate of sending private messages or joining groups is very low. Therefore, both activity (such as active in the past 30 days) and gender fields should be included during acceptance. KK-DATA’s screening task supports specifying active windows (such as 30 days, 60 days, 90 days), and returns gender, age and other labels. These advanced fields can help you accurately target high-potential users.

For example: detecting Telegram activation in the United States + activity in the past 30 days + gender (male), the output available numbers are usually only 30%-50% of the total number of activations, but the customer acquisition conversion rate can be increased several times.

Acceptance dimension four: data freshness - when the number is generated/updated

The opening rate decays over time. For a piece of data generated in 2020, 50% of the numbers may have been canceled or no longer active in 2025. Data freshness directly reflects the remaining “life” of this batch of numbers.

How to judge freshness?

1. Ask the source for the generation timestamp: Ask the other party to provide the data creation date or last update date. If the other party refuses or is vague, be very vigilant.
2. Sampling test activation rate: Take 1,000 numbers and run a Google Voice, Telegram or WhatsApp activation test. If the activation rate is significantly lower than the historical normal value of the same platform (for example, Telegram is lower than 50%), it is likely to be old data.
3. Use the global number generation module to obtain the latest number segment: KK-DATA’s global number generation module (240+ countries) can generate random or specified range numbers by country, number segment, and number digits. These numbers theoretically come from unallocated number segments. You can directly screen the numbers after generation to obtain the latest registration records. This is a supplementary means of verifying data freshness.

Recommendation: Only use numbers generated within the last 3 months. For data exceeding 6 months, the opening rate may drop by more than 30%. If the source cannot provide proof of freshness, it is recommended to lower the purchase price or give up.

How to comprehensively evaluate U.S. data: create your four-dimensional scorecard

The above four dimensions are quantified into scores, with a full score of 25 points for each dimension and a total score of 100 points. The following are recommended scoring criteria:

Dimensions	Scoring rules (0–25 points)
Format Completeness	≥95% is worth 25 points; 90%-95% is worth 20 points; 80%-90% is worth 15 points; less than 80% is worth 0 points
Repetition rate	Less than 5% is worth 25 points; 5%-10% is worth 20 points; 10%-20% is worth 10 points; >20% is worth 0 points
Opening rate	Telegram ≥70% or WhatsApp ≥50% will get 25 points; if it is lower than the corresponding value by 10 percentage points, it will get 15 points; if it is lower than 20 percentage points, it will get 5 points; if it is lower than the corresponding value, it will get 0 points (can be adjusted according to the platform)
Data freshness	25 points for generation time ≤3 months; 15 points for 3-6 months; 10 points for 6-12 months; 0 points for >12 months or unknown

Total score evaluation:

• ≥85 points: high-quality data that can be immediately put into screening and marketing.
• 70 – 84 points: Acceptable, it is recommended to make targeted supplements (such as format correction or deduplication first).
• 60 – 69 points: Risky, it is recommended to reduce the price or only use some fields.
• 少于60 points: Severely unqualified, it is recommended to discard or return.

Using this rating scale, you can quickly rate each US data source and negotiate more reasonable prices with suppliers.

FAQ

**Q: What is the minimum standard that the U.S. data format integrity must meet to be considered qualified? ** Answer: The number must follow the E.164 format (such as +1xxxxxxxxxx), and have at least a country code and a complete number. If a group ID or gender field is included, the field value should not be empty or garbled. Recommended format completeness ≥ 95%.

**Q: How much does it cost to use KK-DATA to accept a batch of US data? ** A: Cost depends on platform, test type and quantity. For example, if Telegram is activated and active, the unit price is calculated based on the real-time price of the console. The fee will be estimated before submitting the task, and the balance must be sufficient (USDT recharge, minimum about 50 USDT). See the real-time price on the console for details.

**Q: How long does it take for data to be considered “fresh”? ** Answer: It is recommended to use numbers generated within the past 3 months. For data exceeding 6 months, the opening rate may drop by more than 30%. If the source cannot provide the generation time, a small number of samples can be used to detect the opening rate and infer the freshness.

**Q: What should I do if the repetition rate is too high? ** Answer: First use the data deduplication warehouse to eliminate the measured numbers; if there are many repetitions with previous tasks, you can ask the source to provide new data after deduplication, or reduce the purchase price.

**Q: If the activation rate is very low after acceptance, can I get a refund? ** A: Data quality is usually agreed between the buyer and seller. It is recommended that the minimum activation rate be agreed in writing with the source before purchasing and the test report should be retained as evidence. The platform itself does not intervene in data transaction disputes.

Conclusion and call to action

High-quality US data is the starting point for overseas customers, and the four-dimensional rating table can help you quickly filter out low-quality sources to avoid wasting real money. Whether you are purchasing data or mining numbers yourself, first spend 10 minutes using format verification, deduplication warehouse, and sampling activation testing to score, and then decide whether to import the numbers into the screening platform in batches.

Try it now: 👉 Log in to the console to start filtering to create the first acceptance task; or contact customer service in both directions https://t.me/kkdata_robot to get one-on-one help; for more operation details, see Usage Documentation. Every testing expense you spend should be spent on truly valid data.