How to Effectively Filter Zalo Accounts? A Practical Approach Based on Account Activity and Feature Tags
In Southeast Asian social marketing and user outreach scenarios, Zalo's effective filtering is becoming a core competency that more and more operators are focusing on. Unlike simple number collection, Zalo's effective filtering emphasizes differentiating accounts' true status, usage habits, and response potential from the source, thereby avoiding ineffective sending and wasted resources. Based on long-term tracking and classification practices of Zalo account characteristics, this article summarizes a reusable filtering logic to help practitioners establish a clear operational framework.
I. Why is the activity level of a Zalo account the first screening criterion?
Many businesses, when trying to reach customers with Zalo, often only focus on whether the number is registered, neglecting the fact that "registration does not equal usability." A Zalo account that has not been logged into for a long time has a near-zero message open rate and may even have been reclaimed or frozen by the platform. The core of activity screening lies in identifying the following three types of accounts:
Highly active accounts : Accounts that have logged in or chatted within the last 7 days, with a message opening probability exceeding 60% within 1 hour of arrival.
Medium-active accounts : Those that have logged in within the last 30 days but have not initiated any chat activity, suitable for non-urgent outreach.
Inactive/Zombie Accounts : Accounts with no login activity for more than 60 days are recommended to be excluded.
An activity scoring model can be established by correlating user last online time with message read receipts. In practice, it is recommended to prioritize accounts that have logged in within the last 15 days, as the response rate in this period is typically 3 to 5 times higher than that of accounts logged in more than 30 days ago.
II. How do feature tags help identify high-value Zalo users?
Besides activity level, account-specific tags are another important dimension for Zalo's effective filtering . Zalo officially allows users to set public information such as profile picture, cover image, personal signature, gender, and region. These fields show significant differences in response times during actual outreach.
Real profile picture vs. default profile picture : Accounts with real photos or casual pictures have a response rate approximately 40% higher than accounts with default profile pictures.
Users who fill in a personal signature are generally more active and more willing to interact.
The regional tags are clearly defined : users in different provinces of Vietnam (such as Ho Chi Minh City and Hanoi) show varying levels of acceptance of the same type of message.
Gender tags : Certain vertical categories (such as beauty and maternal and infant products) show significantly higher conversion rates on female accounts.
It is recommended to establish a tag weighting system, for example: real profile picture +10 points, non-empty signature +5 points, clearly stated region +3 points. Accounts with accumulated scores below a certain threshold can be temporarily excluded, thereby concentrating resources on user groups with more complete characteristics.
III. Field Identification and Duplicate Filtering: Avoiding Three Invalid Outreaches
When batch processing Zalo account data, a common problem is not invalid numbers themselves, but rather duplicates, incorrect formats, or missing fields. A rigorous field preprocessing workflow should include:
Number format standardization : Vietnamese Zalo accounts are usually linked to mobile numbers starting with 84, so leading zeros, spaces, hyphens, and other interfering characters must be uniformly removed.
Removing duplicate numbers : If the same number appears more than three times in different batches, it is often because the number itself is of poor quality and has been repeatedly mixed in.
Blacklisting : Create a separate exclusion list for numbers that have been previously complained about or blacklisted to prevent secondary contact.
Registration status verification : Through the registration status query interface at the Zalo protocol level, distinguish between three statuses: "Registered with Zalo", "Not Registered", and "Deleted".
The cost of data cleaning is far lower than the risk of account suspension due to invalid sending. In practice, it is recommended that each batch of numbers to be processed undergo four steps: "format cleaning → deduplication → blacklist matching → registration verification". This step can increase the overall sending success rate to over 85%.
IV. State Hierarchy Strategy Based on Timestamps and Message Receipts
Zalo's effective filtering relies not only on static features but also on the support of dynamic data. The receipt status after message sending is the most authentic feedback signal:
Delivered but unread : This may indicate that the user has abandoned their account or turned off notifications. Three consecutive unread messages should be moved to the low-activity pool.
Read but not replied to : This indicates that the message was seen but there was no interest. You can try changing your wording or contacting them again after 15 days.
Reply received : High-value interaction signals should be tagged with a "response" label and included in the priority reach queue.
Sending failed/Account does not exist : Permanently exclude.
In addition, it is recommended to record the timestamp of each message. Analysis revealed that Vietnamese users had the highest read rate between 7:00 PM and 9:00 PM on weekdays, while the read rate dropped by more than 30% in the early morning or on weekday mornings. Incorporating timestamp data into the filtering dimension can further optimize the reach window.
V. From Single-Instance Filtering to Building a Dynamic Tag Library
The above four levels of filtering logic (activity level, feature tags, field cleaning, and receipt stratification) are inefficient and difficult to continuously optimize if executed independently. A more efficient approach is to establish a dynamic tag library and continuously update the following tags for each Zalo account:
Activity level (high/medium/low/inactive account)
Feature completeness (avatar/signature/region/gender)
Response history (replied/read but not replied/complained)
Optimal reach time (based on historical open time statistics)
Once the tag library reaches a certain size, subsequent rounds of outreach can be directly filtered based on tag combinations. For example, only accounts with "high activity level + ≥3 feature completeness items + no complaint history" can be selected. This approach improves filtering efficiency by an order of magnitude while significantly reducing subjective bias from human judgment.
In practice, relying entirely on manual processes or fragmented scripts for data collection, cleaning, tag calculation, and filtering across the five dimensions mentioned above would be extremely time-consuming and prone to errors. This is why more and more practitioners are leveraging professional filtering tools to implement effective Zalo filtering processes. Taking ITG's global filtering as an example, this tool integrates functions such as Zalo account activity recognition, avatar and signature feature extraction, automatic deduplication of duplicate numbers and blacklists, and data feedback analysis of sent receipts, integrating the five steps into an automated workflow. Users simply import the original number list, and the system automatically outputs filtering results with activity levels, feature tags, and response scores, and supports exporting high-quality reach lists based on custom thresholds. This data-driven approach avoids the inefficiency and errors of manual operations, making effective Zalo filtering a truly scalable, standardized process, rather than a "feel-good" task dependent on experience.
ITG Global Screening is a leading global number screening platform that combines global number range selection, number generation, deduplication, and comparison. It offers bulk number screening and detection for 236 countries and supports 20+ social and app platforms such as WhatsApp, Line, Zalo, Facebook, Telegram, Instagram, Signal, Amazon, Microsoft and more. The platform provides activation screening, activity screening, engagement screening, gender/avatar/age/online/precision/duration/power-on/empty-number and device screening, with self-screening, proxy-screening, fine-screening, and custom modes to suit different needs. Its strength is integrating major global social and app platforms for one-stop, real-time, efficient number screening to support your global digital growth. Get more on the official channel t.me/itgink and verify business contacts on the official site. Official business contact: Telegram: @cheeseye (Tip: when searching for official support on Telegram, use the username cheeseye to confirm you are talking to ITG official.)