The key to remarketing lies in tg active data: how to accurately reach high-intent users based on active windows

The ideal state of remarketing is “every message has someone responding to it”, but in actual work, after a large number of messages are sent out, nothing happens. The problem often lies not in the way of speaking, but in the target audience—many of the Telegram accounts corresponding to the numbers have not been logged in for a long time or have even been abandoned. tg activity data is the key to solving this pain point: by detecting the user’s recent login time, you can stratify the list by activity before reaching it, avoiding wasting budget on silent accounts.

Why does remarketing often “no one responds to messages”? ——Silent account is the biggest waste

The essence of remarketing is to re-reach existing leads, and the status of Telegram users changes dynamically. A number may be activated today and no longer be logged in next month; it may also be used only once after registration and then abandoned. If you send a direct message with a list from six months ago, a lot of messages will go into your inbox that will never be read.

What is tg active data? ——More than just “online”

tg activity data does not simply mark “online/offline”, but determines the user’s current usage status by detecting the last time the user logged in to Telegram. KK-DATA’s tg activity detection supports filtering by active window, and can also export additional fields such as gender, age, etc., allowing you to have a more complete portrait of the population when stratifying.

How to divide the active window

Different windows correspond to different user stickiness levels:

Active window	User behavior characteristics	Remarketing strategy suggestions
Within 7 days	Frequent use, high sensitivity to messages	Fast conversion, higher frequency
Within 15 days	Moderately active, maybe logging in 1-2 times a week	Gentle wake-up, avoid harassment
Within 30 days	Low-frequency use, you may log in only for notifications	Careful access, high-value content
60 days/90 days	Almost silent, the number may have been abandoned or changed	Very low frequency, only a one-time attempt

These windows can be freely selected when submitting tasks in the KK-DATA console, and the specific fields can be used directly after being exported.

Activity + gender/age = accurate crowd portrait

Looking at the activity alone can only tell “how long has it been since the user came”. Combining the gender and age fields can answer “who hasn’t come for a long time”. For example, if you screen out male users over 30 years old who have been active within 7 days, and then push financial instruments or overseas investment products to this group of people, the response rate is usually significantly higher than that of the general population. KK-DATA’s Telegram gender detection will return gender, age and other fields (such as “male” and “25-34”). You can combine and filter directly on the EE interface without additional processing.

How to use tg active data to develop a remarketing tiering strategy?

The core of the layered strategy is: The more active users are, the more worthy of high-frequency contact, and the more silent users are, the more careful they need to be protected. The following are three typical layering and corresponding playing methods.

Highly active users (within 7 days) - fastest conversion, lowest harassment

Strategy: Instantly push limited-time offers, new product launches, and community activities. This type of user is used to checking Telegram every day and has a high tolerance for messages, but they must also pay attention to the frequency to avoid fatigue. It is recommended that there be no more than 2 posts per day, with intervals between each post.

Medium active users (15–30 days) - reawakening, gentle guidance

Strategy: Provide long-link content, such as case sharing, operation tutorials, and industry reports, and guide them back to the group or channel in conjunction with key events (such as live broadcast room links, free resource collection). The frequency of messages is 1-2 times a week, and the content is more informative than promotional.

Low active or silent users (more than 30 days) - low frequency, small amount, cautious contact

Strategy: No more than 1-2 times a month, each message must be of high value - free tools, exclusive discounts, VIP quota, etc. Low active users are more likely to be blocked or reported, so words must be “one hit”.

Remarketing implementation steps based on tg active data (taking KK-DATA as an example)

Four replicable steps are given below, from preparing number sources to final execution.

Step 1: Prepare remarketing number source

• Own customer list: Import from CRM, order system, historical activity CSV
• Global Number Generation: Use KK-DATA’s [Global Number Generation] (https://kkdata.cc/) module to generate a test number of numbers according to the target country and number segment.
• Note: The number source should contain international area codes. It is recommended to remove duplications before submitting (the platform has a built-in deduplication warehouse)

Step 2: Configure tg active detection task

Log in to Console and enter the screen number module:

1. Upload the number file (CSV/TXT), the maximum number of single files is about 1 million
2. Select “Active Detection” as the detection type and check the required active window (such as 7 days, 30 days, etc.)
3. Check “Gender”, “Age” and “tgid” in the export fields (if subsequent in-depth analysis is required)
4. The system will automatically calculate the estimated cost (see the real-time price on the console for details), and submit the task after confirming that the balance is sufficient.
5. After the task is completed, you can receive the results through Telegram notification

Step 3: Export and layer by activity

Download the CSV file after the task is completed, containing the following typical fields:

phone	active_7d	active_30d	gender	age_range
8613888888888	1	1	male	30-34
8613999999999	0	1	female	25-29

Use Excel or Python script to filter out users with different activity levels according to fields such as active_7d, active_30d, etc., and save them as independent lists.

Step 4: Execute remarketing reach in batches

• High Activity: Use automated tools (not limited to specific brands) to send messages once a day with short-term activities
• Medium active: Twice a week messages, including link back to the group
• Low Active: monthly high-value content, such as free industry report download links
• Silent User: Only a one-time “final recall” will be done. If there is no feedback within 7 days, they will be removed from the list.

Remarketing effect optimization: not only look at the active window, but also look at the user portrait

Activity level provides the basis for “when to reach”, while gender and age answer “to whom and what to say”.

Remarketing focus for male vs female users

• Male Users: Respond faster to tools, financial investments, cross-border logistics, and technical information. Message formats can be concise and direct, highlighting data and efficiency.
• Female Users: Pay more attention to community benefits, discounts, and lifestyle content. The tone of the message can be gentle and friendly, with emoticons and guidance.

Remarketing adaptation for age groups

• 25-35 years old: Telegram’s main user group, accustomed to reading quickly, messages need to be short and concise, and links should be deep. The effect is better when combined with email reach.
• Age 35 and above: May prefer a combination of traditional graphics and text, a clear message structure, and avoid too many link jumps. Appropriately increase customer service interaction with real people.

After exporting the data in KK-DATA, you can directly use Excel’s filtering function to complete cross-grouping. For example, filter “active_7d=1 and gender=male and age_range=30-34” and export them separately.

After the remarketing task is completed, how to deduplicate and reuse the data?

Screening once is not the end, the data needs to be continuously maintained and reused.

Cross-task deduplication - no more repeated deductions

KK-DATA has a built-in data deduplication warehouse. When you submit a new task, the platform will automatically check whether the number has been detected. If it is a duplicate number, the system will skip it and deduct the fee to ensure that the budget is spent on the new number. This feature is particularly suitable for multiple remarketing scenarios - you don’t need to spend money to re-test the status of the same users each time.

Activity expiration and update rhythm

The user’s active status is dynamic. It is recommended to update the list at the following frequency:

• High Activity List: Retest every 1-2 weeks (because the 7-day active window is short)
• Medium Active List: updated monthly
• Low Active List: Revalidate quarterly (silent users tend to be more stationary)

When updating, you only need to re-import the changed parts of the active level in the old list for detection, and the deduplication warehouse will automatically avoid repeated deductions.

FAQ

**Q: How often is the tg active data detection updated? ** Answer: The active window and user status are instantaneous states at the time of detection. It is recommended to retest regularly according to the length of the window. For example, if it is active for 7 days, it is recommended to retest every 8–15 days, and if it is active for 30 days, it is recommended to retest every month.

**Q: Can tg activity detection distinguish real users from bots? ** Answer: Activity detection is mainly based on Telegram platform login/behavior data and cannot 100% distinguish between robots. But combined with the gender and age fields, numbers with high activity and real portraits have a higher probability of being real.

**Q: How many numbers can be checked at most in one detection task? ** Answer: A single task can detect up to about 1 million numbers. The details are subject to the console task configuration; submission cannot be made when the balance is insufficient.

**Q: How to ensure the accuracy of TG active detection? ** Answer: The detection results are based on real-time reachability determination. The platform provides field-level export, and it is recommended to use multiple samples for verification before use; different operators and network environments may cause minimal deviations.

**Q: What is the general ratio of highly active and silent users in the remarketing list? ** Answer: It depends on the number source and the quality of the crowd. Generally, in a general number pool, highly active users within 7 days account for 10%–30%; active users within 90 days account for approximately 50%–70%. It is recommended to do small batch testing first and then scale up.

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