Gender stratification of base material screening: When is it cost-effective to add gender testing?

In overseas marketing, “base material screening” refers to testing and classifying batches of phone numbers in multiple dimensions such as validity, activity, gender, etc., to ultimately obtain a high-quality target user pool. Gender stratification is to additionally detect the gender of the user whose number belongs to the number screening process (some platforms also include fields such as age, avatar, etc.), thereby grouping the base materials by gender to serve targeted customer acquisition. This sounds “accurate”, but gender detection does not always produce positive value - it may double the cost of a single item, or it may increase your ROI by more than twice.

This article will help you determine: **When to add gender testing to your base material, and when to skip this step. **

What is gender stratification in base screening?

The process of base material screening is simply “cleaning” and “sorting”. You have a bunch of numbers. First test which ones are activated (registered for a certain platform), then test which ones are active (recent usage behavior), and then further determine the gender, age and other profile information of these users. Gender stratification is to add the “gender detection” step to this process to classify the valid numbers according to gender (male/female/unknown).

For example, if you submit a batch of Thai WhatsApp numbers through the KK-DATA platform and select “activation detection + activity detection + gender detection”, the final exported CSV will have a “gender” field - you can directly filter by “male” and “female” and only retain the target gender number for subsequent promotion. This process is gender stratification.

Why do we need gender testing for base material screening? two core values

1. Improve customer acquisition accuracy and avoid resource misallocation

If your product is naturally gendered (e.g. women’s care products, men’s skin care products, maternal and child products, fitness supplements), sending mass messages to non-targeted genders is almost a waste of budget. Gender detection allows you to weed out irrelevant numbers before promoting.

Example: Promote a medical beauty project (such as water-light injection), and the main users are women aged 20-40. Through Line’s gender + age detection, numbers that are “female + 20~40 years old” can be screened out, and the conversion rate may be 3-5 times higher than that of “full mass messaging”.

2. Optimize marketing budget allocation and focus on high-value groups

Even if your product is all-gender (such as a universal tool app), you may want to prioritize reaching one gender. Gender stratification allows you to:

First implement high-cost and high-conversion strategies for a certain gender (such as VIP private chat);
Adopt low-cost mass reach to the other gender (such as channel push);
Adjust ad serving materials based on gender ratio.

KK-DATA supports gender detection on mainstream platforms such as Telegram, WhatsApp, Line, and Zalo (some with age/avatar fields), which provides a data basis for budget allocation.

In which scenarios is gender stratification not worth doing?

Be wary of blind screening

When the amount of base material is small or there is no obvious gender difference in the target group, adding gender detection may double the cost of each item, but it will not increase conversions. It is recommended to use the free generation function to test the data quality before deciding whether to enable gender detection.

Small amount of base material or one-time test

Scenario: You only have a few hundred numbers, or are just doing a quick proof of concept.
Reason: Gender detection is an independent charge item (see the console for unit price details). If there are less than 3,000-5,000 profiles, there may only be dozens of valid numbers left after gender screening, which is not enough to support an effective promotion test. It is better to first use the “open + active” test to verify the feasibility of the data, and then consider stratification.

Products/services are for all genders

Scenario: Your product has no obvious gender bias, such as general information apps, games (without obvious gender restrictions), and VPN tools.
Reason: If both male and female users can be converted, adding gender detection will only increase the cost, and after screening, you still need to reach all users, so stratification is of little significance. Unless you want to do A/B copy testing (male copy vs. female copy), I recommend skipping it.

How to use the base material screening platform to achieve gender stratification? (step by step operation)

Taking KK-DATA as an example, the entire process follows the “Generate → Filter → Export” pipeline, billing is performed on a per-item basis, and the estimated cost can be seen before the task.

Step 1: Generate or prepare a list of numbers to be screened

In the “Number Generation” module of KK-DATA Console:

Select target country/region (covering 240+ countries);
Select number segments or import custom CSV (supports global number segment generation);
Generate free, export the list and enter the “Filter” module.

If you already have a list of numbers (for example, a data set collected from an exhibition or purchased), you can directly upload the CSV/TXT file without generating it first.

Step 2: Create a screening task and check “Gender Detection”

Enter the “Screen Number Task” page and click “New Task”;
Select a detection platform (such as Telegram, WhatsApp, Line);
Check the detection type: Activate detection + Active detection (optional) + Gender detection;
The system automatically calculates the estimated cost (billing by item, please see the real-time price on the console for the unit price);
Submit the task and wait for completion (you can be notified through Telegram after the task is completed).

Tips: A single task supports up to about 1 million numbers. If the amount of data is large, it is recommended to submit it in batches.

Step 3: Export results and filter by gender field

After the task is completed:

Click “Export” on the task details page;
Select CSV format (recommended) or TXT format;
Open the CSV and you will see the following field samples:
- phone(number)
- platform(Platform)
- is_open(activated status)
- is_active (active status)
- gender (Gender: Male / Female / Unknown)
- age_range (age range, if any)
Filter by the “gender” column in Excel/Google Sheets and only keep “Male” or “Female” to get the targeted base material.

How does the age field in gender detection results assist gender stratification?

Age field description

Gender detection comes with an age field, which is used to determine the approximate age group of the number user (such as about 30 years old, about 40 years old, etc.), but this value is based on model estimation and is not an accurate age at the ID card level. It is recommended to use it as an auxiliary reference and used together with the gender field. It is not recommended to rely solely on age for fine grouping.

In actual use, you can filter by the combination of “gender + age range”. For example:

Female + 20–30 years old → Suitable for beauty, clothing, and dating promotions;
Male + 30–45 years old → Suitable for cars, investments, and business services.

This combination is reflected in both the Line and Telegram screening results of KK-DATA (subject to the platform export fields). Note: The age field is not available on every platform and is subject to console selection.

Best practices and precautions for gender stratification in base material screening

Combine activity and gender for precise stratification

Gender testing alone is not enough. A male number that has been approved but has not been online for three years has extremely low promotional value. Suggestions:

First do the “activation + activity” test to get a valid number pool that has been active in the past 30 days;
Perform gender detection on the pool to obtain the three-dimensional label of “valid + active + gender”.

In this way, the cost of each number is slightly higher, but the quality is optimal and suitable for high-priced products.

Pay attention to costs: test in small amounts before batching

Testing phase: Extract 200-500 numbers, conduct full testing (including gender), and analyze the male-to-female ratio and cost;
Evaluation: If the proportion is close to the target population structure → submit all gender tests;
If the ratio is seriously skewed (eg 90% males, but you only want females) → abandon gender stratification, or change the number source.

Cost monitoring: The console will display the estimated cost before each task. You pay for what you use. There is no subscription package. It is recommended that the test cost be controlled at 5-10 USDT, and then decide whether to increase the amount based on the results.

Be wary of scams posing as customer service

The KK-DATA platform clearly reminds you that the official customer service is only @kkdata_robot and @kkdata_cc. Anyone who claims to be customer service but does not contact you with these two accounts is a scam. Do not transfer USDT or share account passwords to any unofficial channels.

FAQ

**Q: Is the gender test in base screening accurate? **

Answer: Gender detection is based on public data models (such as nicknames, avatars, public information characteristics, etc.), and the accuracy is high but not 100%. It is recommended that “Unknown” (unknown gender) be kept alone in the exported results and not discarded directly to avoid misscreening. The specific accuracy rate will vary due to number quality and platform differences. You can view the detection overview in the task report.

**Q: How much additional cost will gender stratification add? **

Answer: Gender testing is a type of testing that is billed independently, in parallel with activation and active testing. The cost per number depends on how many tests you enable at the same time. For details on the unit price, please see [Console Real-time Price] (https://app.kkdata.cc/). Generally speaking, the cost of activating “activate + active + gender” is about 40%-60% higher than “activate + active” (specifically varies by platform). It is recommended to take 200 samples for testing first, and then decide whether to use the full quantity.

**Q: Which platforms support gender detection? **

Answer: Currently, KK-DATA supports gender detection on mainstream social platforms such as Telegram, WhatsApp, Line, and Zalo (some come with age and avatar fields). iMessage, RCS, Viber, etc. currently only support activation/valid detection. Please refer to the console detection type drop-down menu for the latest support list.

**Q: I have a batch of numbers, how can I quickly determine whether it is worth gender stratification? **

Answer: Three-step judgment method: ① First do the “activation + active” test to filter out valid and active numbers; ② Randomly select 200 numbers and do a gender test; ③ Analyze the male-to-female ratio: if the target gender ratio is ≥ 40%, it is worth fully stratifying; if it is less than 20%, it is recommended to change the number source or skip the gender dimension.

**Q: What fields are included in the exported data after gender stratification? **

Answer: CSV export usually includes: phone number, platform name, detection time, activation status, active status, gender (Male/Female/Unknown), age range (if any), tgid/wsid/uid and other platform internal IDs. The specific fields are subject to the actual export results of the console, and the number of fields varies slightly on different platforms.

Gender stratification is an advanced technique for base material screening, which can effectively improve the customer acquisition conversion rate, but the premise is that you must know when to use ** and when to discard **. If you are preparing to optimize overseas marketing data, it is recommended to log in to the console to try the gender detection function, or contact customer service for one-on-one guidance.

👉 Log in to the console to start screening numbers | Two-way contact customer service https://t.me/kkdata_robot More usage documentation: https://docs.kkdata.cc/

Gender stratification in base material screening: When is it cost-effective to add gender testing?

关于作者