LINE (Thailand) Effective Filtering Practical Guide: How to Distinguish Users of Varying Activity Levels Using Chat Tags and Friend Lists

About the author : The author has over 4 years of experience in digital user management in the Southeast Asian market, and has built official Line account operation systems for 3 companies expanding overseas, managing over 500,000 Line friends in Thailand. The methods below are all based on real project operation records.

In the Thai market, Line is not just a chat tool, but also a core channel for businesses to reach users. However, many teams have found that after sending mass messages to all friends, the open rate is often less than 20%. The root cause is the lack of differentiation based on user activity levels. The core value of effective filtering on Line (Thailand) is precisely to help businesses shift from "broadcasting" to "segmented development." This article will focus on practical methods for effective filtering on Line (Thailand) , breaking down how to use chat tags and friend lists to accurately differentiate users with different activity levels.

I. Why is it necessary to differentiate the activity levels of Line friends?

Without activity-based segmentation, sending the same content to all friends by a business will lead to three problems:

• Highly active users are overwhelmed by low-quality content : Frequently interacting users receive too much irrelevant information, which actually reduces user engagement.

• Inactive users waste sending quotas : Users who don't read messages for a long time occupy friend slots but never generate conversions.

• Unable to optimize subsequent strategies : Lack of understanding of which users are worth continued engagement; marketing actions lack data support.

Activity level filtering is not a bonus, but a basic step. Without stratification, there is no room for optimization.

II. How to use chat tags to mark user interaction frequency?

Line's official account backend offers a "tag" feature, allowing users to be tagged manually or automatically. The steps are as follows:

• In Line Official Account Manager , select "Audience" > "Tags" > "Create New Tag".

• Examples of commonly used activity metrics: "Interactions in the last 7 days", "Unread messages in the last 30 days", "Clicked links", "Replied to messages".

• Each time a user clicks a menu item, replies to a message, or opens a link, add a corresponding tag for that user via API or manually.

• Regularly clean up tags for users who haven't interacted in a long time to avoid data redundancy.

Practical suggestion : Update tags once a week, Monday morning. Tag users who have clicked in the past 7 days separately as "High Activity - Weekly"; tag users with no activity in the past 30 days as "Dormant - Monthly". The more detailed the tagging system, the more accurate the subsequent filtering will be.

III. How to perform an initial screening using your friend list?

Chat tags require the accumulation of interaction data. For new accounts or businesses with a small number of friends, you can first use the friend list for initial grouping:

• Differentiate by user addition source : Users added via QR code vs. users added via event link; their activity levels are naturally different.

• By addition time : Friends added in the last 30 days generally have a higher open rate than older users added more than 6 months ago.

• Sort by the time of the last message in the chat window : The last interaction time field can be exported from the "Friends" list in the Line backend.

Real-world example : In Q3 2025, a Thai e-commerce team discovered that friends added through offline event QR codes had a 58% message open rate in the first week, while those added through website pop-ups only had a 22% open rate. They created a separate list for the former and prioritized pushing promotional content to them, resulting in an overall conversion rate increase of 1.7 times.

IV. How to combine tags and lists to form a hierarchical strategy?

A single dimension is not precise enough; it is recommended to use tags and lists in combination to form a three-tiered activity structure.

• Highly active users : Users who have clicked within the past 7 days and whose source is an activity QR code → receive push notifications for new product trials or limited-time discounts.

• Medium-active users : Users who have opened but not clicked in the past 30 days + added less than 90 days ago → Push brand stories and user case studies.

• Low-activity/dormant users : No activity in the past 60 days + added more than 180 days ago → No push notifications will be sent; they will be kept under observation.

Operation path : In the Line backend, select "Broadcast" > "Filter Target" > "Select Tags" + "Select List". The system will automatically find the intersection. Spending 5 minutes performing a combined filtering before each mass message can reduce invalid messages by approximately 40%.

V. Daily maintenance rhythm of activity level screening

Layering is not a one-time task; a fixed rhythm needs to be established.

• Daily : View users who have clicked in the past 24 hours and add the "High Activity - Daily" tag.

• Weekly : Export friend list, update last interaction time, and adjust inactive user tags.

• Monthly : Conduct a "lightweight wake-up" test (send a piece of high-value content) on users who have not interacted for 60 consecutive days. If there is still no response, consider removing them.

• Quarterly : Review tag usage frequency, delete unused tags, and merge similar categories.

Tip to avoid pitfalls : Do not assign more than 5 tags to each user, otherwise the backend will load slowly and it will be difficult to filter and judge. The key is to have "enough" tags, not "many".

VI. When you have a large number of friends, how can you use filtering tools to improve efficiency?

When you have over 100,000 Line friends, manually tagging and exporting the list becomes extremely time-consuming. At this point, you can use the ITG global filtering tool to automate the stratification. This tool supports batch importing Line user IDs and automatically detects the following dimensions:

• Last online time : Identifies users who have logged in within the last 7, 30, and 90 days.

• Profile picture and bio update frequency : helps determine whether an account is a long-term active and genuine user.

• Message read status : Batch detection of which users actually opened the messages in the historical mass messaging.

• Account type identification : Differentiate between personal accounts and test accounts to avoid sending messages to invalid accounts.

With ITG's global filtering, businesses can export 100,000 friends into three lists: "Highly Active," "Mediumly Active," and "Dormant," and directly import them into the Line backend for group broadcasting. Compared to manual tagging, this is about 20 times more efficient, and the filtering rules can be customized and saved, running automatically once a week.

Conclusion

The essence of effective filtering on Line (Thailand) is to allocate limited sending resources to users most likely to respond. Chat tags address "behavioral recognition," friend lists address "source segmentation," and when the volume grows to the point where manpower cannot cover it, ITG's global filtering provides a scalable, automated solution. It's recommended to start this week by manually tagging the most recent 1000 friends with "interaction date" tags, and gradually scale it up after the process is running smoothly. The more granular the filtering, the more relevant the messages users receive, and the lower your operational costs.