Guide to avoiding common misunderstandings in data detection: Why will only looking at the total volume waste your overseas customer acquisition costs?

When acquiring overseas customers, whether through Telegram, WhatsApp or Line promotion, the first step is often data detection—verifying whether the number is activated, active, and has the correct crowd tag. But many teams make the same mistake: Only look at the total number of test results, not the differences in test types.

For example: you get 100,000 numbers, and the test result shows that 80,000 numbers are “activated”. You excitedly start pushing in batches, but the response rate is less than 2%. Why? Because only 30,000 of these 80,000 accounts may be recently active users, and the rest are either zombie accounts, or users have already changed platforms. Looking at the total amount without looking at the type is equivalent to spending money on invalid data.

This article sorts out 8 common misunderstandings in data detection, each of which is a pitfall that has been encountered in actual combat. After reading, you will find that after optimizing the detection strategy, the same budget may bring more than 3 times the conversion improvement.

Misunderstanding 1: Confusing activation detection and activity detection

Key points of this section: Open (registered) and active (recently online) are completely different indicators. Many users only do the activation test and think the number is “valid”. As a result, a large number of users do not reply after sending.

Open ≠ Reachable - why activity is the key to conversion

The activation test only tells you whether the number is registered with the platform (for example, whether there is a Telegram account). But “activating” does not mean that users will see your messages. A more common situation is: the user has not used the platform for a long time, or has even uninstalled the app, but the account is still there.

Activity Detection can filter out users who have recently performed operations (such as those who logged in within the past 7 days or 30 days). For scenarios such as sending marketing messages, community invitations, and private message promotions, the value of active users is much higher than that of just activated users.

Detection type	Meaning	Reference value for customer acquisition
Open the test	The number has a registered account on the platform	Basic threshold, but there is no guarantee that it can be reached
Activity detection	The number has usage records within the specified time window	High-value targets, significantly higher conversion rates

How to set the active window? Short-term active vs long-term active options

Active windows usually have options of 7 days, 30 days, 90 days, etc. Which one to choose?

Short-term active (7 days): Suitable for time-sensitive promotions, such as limited-time activities and flash sale notifications. Users are using it frequently and the message opening rate is the highest.
Mid-term active (30 days): Suitable for general brand contact and community invitations. Users are still using it continuously, but may not open it every day.
Long-term active (90 days): Suitable for awakening existing users and maintaining long-term customer relationships. But the response rate may be lower than short-term activity.

Active detection recommendations

It is recommended to choose an active window based on the marketing cycle. For example, if your customer acquisition goal is to attract new members, choosing 7-day active users can achieve a higher enrollment rate. KK-DATA supports customizing the active window, which can be set when submitting the task (see Usage Documentation for details).

Misunderstanding 2: Ignore the gender/age dimension and push indiscriminately

Key points of this section: Gender and age fields can help you target customer acquisition, but many people either ignore it or misuse it. In particular, “tg 30-year-old data” is often misunderstood as precise age, and should actually be used as a probability reference.

What scenarios is the gender field suitable for?

Gender recognition is one of the screening functions of many platforms, such as Telegram, WhatsApp, Line, etc. After getting the gender tag, you can:

Male Targeting: Promote games, tools, and financial products
Female Targeting: Promote beauty, clothing, maternal and infant products
Mixed delivery: Select the gender ratio based on product characteristics to avoid wasting exposure through indiscriminate push.

Reasonable interpretation of the age field: How to use “about 30 years old” data

The gender detection results of some platforms (such as Telegram) will be accompanied by an age field, but this age is a probability value calculated from the user’s public information (such as avatar, numbers in nicknames, social behaviors, etc.) and is not the precise ID card age. For example, the label “about 30 years old” in the detection results indicates that the user’s profile tends to be in the 25-35 age range.

Reasonable Usage: Use the age field as a reference dimension for crowd screening. For example, when targeting users “25-40 years old”, filtering the numbers whose age field is in the 25-40 range can greatly increase the probability of reaching the target group.

Avoid over-interpretation of “tg 30-year-old data” - not ID card level accuracy

Many customers will ask: “Can I filter out only users who are 30 years old?” The answer is no. No third-party screening tool can achieve single-digit age detection. If you see someone claiming that they can achieve “30-year-old precision”, please be wary of false advertising.

The correct understanding is: the age field is a probability label, used to narrow the scope of the crowd, rather than precise instant killing. In KK-DATA, the age field is an incidental output of the gender detection package. The specific field name and value range are subject to console export. It is recommended to conduct small batch testing and verification first.

Misunderstanding 3: Only look at the total number of numbers, not the source of the platform

Key points of this section: User habits of different platforms (Telegram, WhatsApp, Line, Zalo) are very different. If you only focus on the total number and ignore which platform the numbers are concentrated on, it may lead to directional errors.

For example, you have obtained 50,000 global numbers from a certain channel and want to expand into the Southeast Asian market. After testing, it was found that 40,000 messages were from Telegram users and only 5,000 were from Zalo users. But your target market is Vietnam, and Zalo is the mainstream. In this case, if you just look at the total number and think “50,000 is a lot”, the actual effective target is only 10%.

Recommendation: Identify the main platforms of the target market before testing, and then carry out cross-platform screening in a targeted manner. KK-DATA supports multi-platform screening numbers, and can detect which platforms the same batch of numbers are activated/active on at one time, helping you determine the platform distribution.

Misunderstanding 4: Ignoring number deduplication and wasting the cost of repeated detection

Points of this section: If the same batch of numbers is submitted for testing multiple times, fees will be deducted repeatedly. Many teams used the same number pool for multiple tasks, resulting in testing the same numbers three times and spending three times the money.

For example, you submitted “Telegram activation detection” and “WhatsApp activation detection” successively, and the two tasks used the same batch of 100,000 numbers. If two tasks are deduplicated, you only need to detect the activation status once and then cross-match. But if you don’t have a duplication mechanism, the system may send the same number to the detection process twice and deduct fees twice.

KK-DATA has a built-in data deduplication warehouse that can automatically identify historically detected numbers when submitting tasks to avoid repeated deductions. Strongly recommended: Use the deduplication function to clean the number list before submitting each task.

Misunderstanding 5: Not understanding the billing model and incorrectly estimating costs

Key points of this section: KK-DATA adopts the model of per-item deduction + no subscription package. Different platforms and different detection types have different unit prices. Many people only calculate their budget based on the total amount and ignore the differences in detection types, resulting in actual costs far exceeding expectations.

For example, the unit prices for Telegram activation detection and activity detection are different. If you want to screen out 20,000 active users out of 100,000 numbers, you actually need to initiate an activation test (deduct fees) first, and then initiate an activity test (deduct fees again) on the activation results. If you mistakenly believe that “one test and everything is done”, the budget will double.

Key Tips:

You can check the real-time price on the console before placing an order, and the costs of different detection types are clear at a glance.
Before submitting the task, the system will display the estimated cost, please be sure to confirm before submitting.
Don’t just look at the unit price of a single task, consider the total cost of the entire pipeline (generate→filter→export).

Cost estimate reminder

Before submitting a screening task, be sure to check the console’s estimated cost. Even for the same batch of numbers, the unit prices on different platforms may differ several times. If necessary, conduct small batch testing first, and then submit in batches after confirming the cost. For details, see Billing Instructions.

Misunderstanding 6: Export fields are not checked and key information is missing

Key points of this section: The CSV/TXT exported after the task is completed contains a large number of fields, such as tgid, active time, gender, age, etc. If you don’t check the default export settings, you may be missing key fields you need.

For example: You only want to export Telegram numbers that are “activated and active”, but after exporting, you find that only the “Number” and “Activation Status” are exported by default, and there is no “Active Time” column. This means you can’t tell which numbers have been recently active. Task needs to be resubmitted.

Best Practice:

Carefully select the export fields when submitting a task. KK-DATA console supports custom export columns.
If you are not sure, export 200 samples first to check the field contents, and then officially export all after confirmation.
Common necessary fields include: number, platform, activation status, active status, last active time, gender, tgid/uid, etc.

Misunderstanding 7: Tasks are backlogged without notification, and the deadline window is missed.

Points of this section: The number screening task may need to be queued due to the huge amount of data (for example, it takes several hours to half a day to detect 1 million numbers). If you do not download the results in time after the task is completed, the user’s active status may change over time, resulting in a decrease in data timeliness.

For example, you submitted an “Active in the past 7 days” check and you forgot to download it after the task was completed. When I remembered to download it 2 days later, the original “7 days active” window had changed to “9 days ago”, and the reference value of the data was reduced.

Solution: Utilize KK-DATA’s Telegram Task Notification feature. After the task is completed, a notification will be sent through your bound Telegram account, and you can download the results as soon as possible. The setting method is in the “Notification Settings” of the console.

Misunderstanding 8: Relying only on data from a single platform and ignoring cross-platform crossover

Points of this section: Many customer acquisition teams only focus on one platform (for example, only doing Telegram screening), but the actual target users may be active on multiple platforms at the same time. Cross-platform detection can help you find a more complete user profile.

For example, a user might receive your message on Telegram but not reply, but be active on WhatsApp at the same time. If you only relied on Telegram data, you might have missed this user. By checking the same batch of numbers across platforms, you can discover:

Which numbers are active on multiple platforms (high value targets)
Which numbers only exist on specific platforms (platform preference)

Operation Suggestion: Submit a batch of numbers to multiple platforms for testing at the same time (KK-DATA supports selecting multiple detection types on one page). After the results are exported, filter them according to the “multi-platform active” condition to give priority to these users.

FAQ

**Q: What is the difference between “activated” and “active” in data detection? ** Answer: Activated means that the number has a registered account on the platform; active means that it has usage records within the specified time window (such as 7 days, 30 days). Only doing activation detection may reach a large number of zombie accounts; adding active detection can greatly increase the conversion rate.

**Q: Is the age field in Telegram filter results accurate? ** Answer: Not precise. The age field is a probability value inferred from public information and can be used as a reference for the population (for example, “about 30 years old” refers to the 25-35 year old range), but it cannot be used as ID card-level data.

**Q: How to avoid wasting money on duplicate number detection? ** Answer: Use KK-DATA’s built-in deduplication warehouse. Before submitting the task, upload the number to the deduplication module, and the system will automatically eliminate numbers that have been detected in the past.

**Q: Is the testing fee fixed? ** Answer: Not fixed. The unit price is different for different platforms (Telegram, WhatsApp, Line, etc.) and different detection types (activation, active, gender). For details, see the real-time price of the console. The estimated cost will be displayed before submitting the task.

**Q: How to know the result after submitting the task? ** Answer: You can set up Telegram notifications in the console, and you will receive reminders after the task is completed. You can also actively log in to the console to view task status and download results.

The above 8 misunderstandings are the most common pitfalls in data detection. I hope this guide will help you avoid detours and spend every penny on truly effective users.

👉Log in to the console to start screening numbers If you have any questions during use, you can contact customer service in both directions https://t.me/kkdata_robot For more details, please see Usage Documentation

A guide to avoiding common misunderstandings in data detection: Why will only looking at the total volume waste your overseas customer acquisition costs?

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