How to Filter Viber Users by Gender and Age? An Approach Based on Number Screening Platforms
In Viber's targeted messaging scenarios, the clarity of user profiles directly impacts reach effectiveness. Many teams only discover after investing resources that a large number of messages are being sent to users outside the target gender or age group, resulting in engagement rates far below expectations. The key to solving this problem lies in implementing Viber's gender and age filtering . Through a systematic Viber gender and age filtering system , it's possible to identify the truly relevant user group from the outset, rather than blindly sending content to all users.
Based on practical experience in screening, this article breaks down how to stratify and screen Viber users by gender and age within a compliant framework, and introduces how to standardize this process using a screening platform.
I. Why do we need to filter Viber users by gender and age?
Viber boasts hundreds of millions of active users worldwide, with significant differences in user demographics across different countries and regions. Without proper filtering, typical issues that might arise include:
Content mismatch with gender : Content intended for male users is sent to female accounts, resulting in a large number of invalid impressions.
Age mismatch : A service designed for users aged 25-35 was heavily targeted at people over 45, resulting in a sharp drop in interaction rates.
Statistical bias : Conclusions drawn from full sample analysis cannot guide accurate business decisions.
Resource dispersion : With the same reach cost, the proportion of non-target users is too high, which lowers the overall conversion efficiency.
By filtering by gender and age, the target audience can be narrowed down to 30% or even lower, but the effective interaction rate often increases by 2-3 times. This is an effect that cannot be achieved by "casting a wide net".
II. Feasible Dimensions and Judgment Basis for Viber's Gender Filtering
Gender information for Viber users is not publicly available and must be determined through indirect features. In practice, the following dimensions have proven to be highly valuable:
Avatar Feature Recognition : Based on image recognition and analysis of the gender tendency of avatars, the accuracy rate can reach over 85%.
Nickname semantic analysis : Nickname word choice, suffix, and common name database matching in multilingual environments
Usage time distribution : There are statistical differences in the active times of different genders on Viber, which can be used as an auxiliary weight.
Linked social information : If a user has linked other social media accounts and their gender is publicly disclosed, this can be used as a basis for cross-verification.
It should be noted that any judgment based on a single dimension is subject to error. Screening platforms typically employ a multi-dimensional weighted scoring mechanism, and only when the overall score exceeds a set threshold will a user be marked as "reachable".
III. Viber's Age Filtering Layering Method and Data Model
Age-based screening requires more complex models than gender-based screening. In practice, the following hierarchical strategies are commonly used:
Registration duration inference : The longer a Viber account has been registered, the older the user is likely to be. Accounts registered within the last year show a significantly higher proportion of users aged 18-30.
Active time characteristics : Younger users (18-25 years old) are significantly more active after 10 PM than users over 35 years old.
Device type correlation : Users of the new high-end models are more concentrated in the 20-35 age group.
Message response speed : Younger users have a shorter average response interval, which can be used as a dynamically adjustable feature weight.
After combining the above features into an age probability model, users can be divided into four intervals: 18-25, 26-35, 36-45, and 46+. In actual screening operations, this can be further merged into three coarse-grained categories: "young," "middle-aged," and "middle-aged and elderly," or fine-grained categories can be maintained and extracted as needed.
IV. Operational Procedures for Gender and Age Combination Screening
In actual screening tasks, screening by gender or age alone is rare; more often, a combination of both is used. The standard operating procedure is as follows:
Step 1: Define your target demographic . For example, female users aged 25-35, or male users aged 18-30. Different demographics correspond to different feature weights.
Step 2: Import the original number pool . Upload the list of Viber numbers to be filtered to the number filtering platform. It is recommended that each batch not exceed 100,000 numbers to ensure processing efficiency.
Step 3: Set the filtering rules . Select "Female" for the gender dimension and "25-35" for the age dimension, and set the confidence threshold (it is recommended not to be lower than 75%).
Step 4: Perform filtering and verification . The platform returns a list of filtered numbers, along with a confidence score for each record. 200-300 records can be randomly selected for manual review.
Step 5: Output and Usage . Use the high-confidence numbers that pass the screening for subsequent outreach tasks. Numbers that do not pass can be downgraded for generalized notification scenarios.
Based on actual project data, the effective interaction rate of the number pool after combination screening is on average about 170% higher than that of the unscreened pool. This increase is sufficient to cover the cost of the screening itself.
V. Data Quality Verification and Iterative Optimization after Screening
Filtering is not a one-time process. Regular validation and iteration are essential as user behavior changes and models are updated.
A/B testing validation : 5000 numbers were selected from the filtered numbers and 5000 numbers were randomly selected. The same content was sent to each, and the interaction rate and conversion rate were compared.
Misjudgment Case Analysis : Regularly conduct manual reviews of users with low confidence levels who actually match the user profile, analyze their characteristic patterns, and feed back into the model.
Adjust parameters by country : Viber user behavior patterns differ significantly in countries such as Vietnam, the Philippines, and Saudi Arabia, requiring different weights to be set for each region.
Monthly Model Updates : User behavior can change over time, so it is recommended to retrain or adjust the age/gender assessment model monthly based on the latest data.
A well-functioning screening process typically reaches its optimal performance after three consecutive months of iterations, after which only monthly fine-tuning is required.
In practice, efficiently completing the above five steps requires a mature and stable number screening tool. ITG Global Screening is an integrated platform designed for such needs—it incorporates a Viber gender recognition model and age stratification algorithm, supports multi-dimensional combined screening, and provides real-time confidence scoring and data verification functions. Users only need to upload the original number list and set the target profile parameters; the system can automatically complete the entire process from feature recognition to stratified output, while also supporting fine-tuning by country, device, and time period. For teams that need to perform Viber user screening in large batches over a long period, ITG Global Screening compresses hours of manual work into minutes, while maintaining a verifiable and stable accuracy rate.
In summary, Viber's gender and age screening is not a mysterious technology, but rather an engineered method based on weighted judgment of multi-dimensional features. By establishing a reasonable screening process, using reliable tools and platforms, and consistently conducting regular verification and iteration, the accuracy of Viber's outreach can be significantly improved. We hope that the five operational points provided in this article can offer a practical reference for teams exploring Viber's user screening methods.
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.)