US ws number gender screening tutorial: teach you step by step how to use the gender field to do secondary stratification

In overseas marketing, US ws number (i.e., the mobile phone number that enables WhatsApp in the US) is the main customer acquisition resource for many teams. However, just getting a batch of “activated accounts” and sending them out in bulk often leads to low response rates and high complaint rates. The key to the problem is not that there are not enough numbers, but that there is no stratification - you do not have differentiated access based on basic attributes such as user gender.

This article will focus on the main line of Gender Screening to explain how to obtain a US WhatsApp number with a gender field through the KK-DATA platform, and use the gender field for secondary stratification. The full text includes complete operating steps, boundary identification instructions, and practical checklists to help the overseas team truly make use of the data.

What is US ws number gender screening? Why is it important to acquire customers overseas?

US ws number, simply put, is a number that belongs to the United States and has opened WhatsApp. Gender screening is based on these numbers and uses a model to infer the gender (male/female/unknown) of the account corresponding to each number, thereby providing a basis for subsequent differentiated marketing.

The core value of this operation is:

Reduce the sense of harassment: A unified template is sent to both men and women at the same time, and the content is often “unpleasant to both sides.” After gender stratification is done, fine-tuning can be made in the wording and picture style of the copy.
Increase the reply rate of private messages: Promote tools and game content to men, and promote beauty, makeup, and maternal and child content to women. The response rate is usually higher.
Suitable for multiple scenarios: For example, cold start of a cross-border e-commerce standalone site, or local promotion of a dating app, both require a round of user portrait stratification based on gender.

One sentence summary

US WS number + gender screening = not only get a number that is “used”, but also know “who is who”, so you can do smarter things.

How to get the US WA number with gender field?

If you want US ws number with gender, the core process is “number generation → screen number → export”. The KK-DATA platform fully supports this pipeline.

Step 1: Generate or import the US number database

Option A: Use the Global Number Generation module, select the country/region as “United States”, and the system will automatically generate a random number segment. This part of the build is free.

Option B: If you already have your own number pool (such as US mobile phone numbers collected from other channels), import the CSV file directly in the console. Pay attention to the file format: one number per line, without country code symbols.

Step 2: Submit the screening task and enable gender detection

Log in to [Application Console] (https://app.kkdata.cc/) and select the WhatsApp Screener type. Among the detection items, be sure to check both:

Activation Test (required) → Confirm whether the number is registered with WhatsApp
Gender Detection (optional) → Infer account gender

Important order: “Open” must be detected first, and then “Gender” must be detected. An unactivated number does not have a WhatsApp account and naturally does not have gender data.

Before submitting the task, the system will display Estimated Deduction. Please refer to the real-time price of the console for the rate.

Step 3: Export the results and view the gender column

Once the task is complete, export to CSV or TXT. The export file will contain a Gender column with values 男性, 女性, or 未知.

phone_number,country,whatsapp_status,gender
+12025551234,US,开通,男性
+12025559876,US,开通,女性
+12025550001,US,未开通,未知

It is recommended to run an experimental task with 10,000-20,000 numbers when trying it for the first time, observe the gender distribution ratio, and then decide on the next step.

How to use gender field for secondary stratification?

After getting the CSV with gender column, the specific layering method is as follows:

Filter by gender column in Excel or Google Sheets, copy male, female, and unknown to three worksheets respectively.
Combined with the activity field (if you also detected “activity”), perform another filter within each gender group: only retain “active” users. This can exclude “open but inactive” zombie accounts.
Create differentiated content for different layers:
- Male group → Recommended tool apps, games, and content information
- Women’s Group → Recommended beauty, clothing, mother and baby, lifestyle
- Unknown group → Keep the neutral copy and adjust it later based on the open rate

Layering Tips

Don’t base your metrics solely on gender. It is recommended to first use “activate + active” to screen out high-potential numbers, and then perform secondary grouping through the gender field. This can not only control the sending volume, but also improve the accuracy.

Avoid repeated addition: Do not add numbers repeatedly between different layers. If a number appears in both groups (rarely), priority will be given to the group with a clear gender.

Correct understanding and common misunderstandings of gender identification fields

Source of the gender field: The KK-DATA platform makes inferences based on public information such as the avatar, nickname, and personal information of the WhatsApp account, combined with the model. It is not ID verification and is not 100% accurate.

Common Misunderstandings:

Misunderstanding	Correct understanding
Gender identification = true gender	It is just model inference and there may be misjudgments, especially for neutral information or accounts with undisclosed gender
”Unknown” can be thrown away	It is recommended to keep the unknown group and send reach test copy separately, which may accidentally cover high-intent users
The gender field is permanently fixed	Users may modify the information later, so it is recommended to retest regularly

Identify boundary reminders

The gender field is the result of model inference, and there is a certain misjudgment rate (especially for neutral information or accounts with undisclosed gender). It is recommended to group “unknown” into a separate group to avoid accidentally hurting potential users.

Best Practices and Checklist for Gender Screening of US WS Numbers

Preparation checklist before operation

Confirm that the balance is sufficient and understand billing instructions
Upload historical detected numbers to Data Deduplication Warehouse to avoid repeated deductions
Check the real-time unit price of WhatsApp gender detection (console display)

Monitoring list during operation

Confirm that “Enable + Gender” detection is checked in the task
Check the estimated deduction amount before submitting
It is recommended that the number of single task numbers be controlled within 100,000 to reduce waiting time

Review list after operation

Statistical gender distribution ratio (how many are male/female/unknown)
Compare the activity proportions of different genders to determine the focus of the next step
Save the unknown group number for subsequent observation, do not delete it blindly

FAQ

**Q: How accurate is the gender screening of US WS numbers? **

Answer: The gender field is inferred based on public information such as account avatars, nicknames, and information. The accuracy rate is about 70% to 90% on the platform, but it cannot reach the ID card level. Recommended for directional stratification, not the sole basis for decision-making.

**Q: Why do many genders in my exported US WA numbers show “Unknown”? **

Answer: There may be two reasons: first, these numbers have not been activated by WhatsApp (if not activated, there is no information and cannot be inferred); second, they have been activated but the account information does not have a gender or the information is incomplete. It is recommended to screen the “activated” numbers first, and then perform gender testing on the accounts with data.

**Q: Are there any additional charges for gender screening tasks? **

Answer: Yes. Different detection types (such as activation only vs. activation + gender) have different unit prices. For specific costs, please refer to [Console Real-time Price] (https://app.kkdata.cc/). The estimated deduction will be displayed before submitting the task, please confirm that the balance is sufficient.

**Q: Can I filter only “male” or “female” US WS numbers? **

Answer: Yes. After exporting to CSV, filtering by gender field can be done. However, since the gender field is the result of model inference, there is a small amount of misjudgment. It is recommended to combine cross-validation with other fields (such as activity).

**Q: How to avoid repeatedly testing the gender of the same batch of numbers? **

Answer: Use KK-DATA’s [Data Deduplication Warehouse] (https://docs.kkdata.cc/) function to upload numbers that have been detected in the past to remove duplicates, so as to avoid repeated payment for a new batch of the same numbers.

If you want to experience the US WS number gender filtering function immediately, please log in to the console to create a task; if you encounter any problems during the operation, please contact customer service in both directions for real-time support. The official website and documentation can assist with more detailed operating instructions.

👉 Log in to the console to start screening numbers 📞 Two-way customer service contact: https://t.me/kkdata_robot 📖 Usage documentation: https://docs.kkdata.cc/ 🌐 Official website homepage: https://kkdata.cc/

U.S. WS number gender screening tutorial: teach you step by step how to use the gender field to do secondary stratification

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