Don't know how to use Zalo's gender and age filtering? We'll teach you how to precisely segment your users and say goodbye to customer acquisition difficulties in the Vietnamese market

In the Vietnamese market, Zalo is undoubtedly a national-level social application, but many businesses find their customer acquisition results dismal after investing heavily. The core problem often lies in their inability to utilize Zalo's gender and age filtering features, leading to messages being pushed to the wrong audience and consequently, low response rates. In fact, mastering Zalo's gender and age filtering is the first hurdle to overcome in penetrating the Vietnamese market. Only by accurately segmenting users by gender and age can resources be avoided by focusing on ineffective users, ensuring that every outreach directly targets the right audience.

I. Zalo's Gender Targeting: Targeting Male and Female Audiences to Avoid Misaligned Marketing Direction

Users of different genders naturally have different acceptance levels towards products and content. Without proper screening, this may lead to the following problems:

• Information mismatch : Male users receive beauty product ads for women, while female users receive game equipment notifications; neither party is interested.

• Wasted resources : Customer service and sales teams spend time on non-target gender groups, resulting in a significant decrease in conversion rates.

• Data distortion : Inability to accurately collect genuine feedback from users of a particular gender, interfering with subsequent marketing decisions.

Setting up gender filters in Zalo allows you to directly filter out mismatched gender groups, ensuring that marketing content only reaches users with genuine potential needs.

II. Zalo Age Filtering: Precisely segmenting generations to enhance content resonance.

Vietnamese users of different age groups exhibit significant differences in consumption habits, active time, and content preferences. Ignoring age screening will lead businesses to:

• Generational conflict : Trendy content aimed at Generation Z is pushed to middle-aged and elderly users, resulting in near-zero interaction rates.

• Misjudgment of spending power : Failure to differentiate between students and working professionals leads to ineffective pricing and promotional strategies.

• Active time deviation : Office workers and students have different online hours; without segmentation, the best sending opportunity will be missed.

• Conversion path breakdown : Even if users in the wrong age group click on the ad, it is difficult to complete the subsequent conversion.

Zalo's age filtering allows users to be categorized into core age groups such as 18-25, 26-35, and 36+, enabling customized messaging and activities for different generations and significantly enhancing content resonance.

III. Gender and Age Combination Filtering: Building Precise User Profiles

Using gender or age filters alone can improve results, but combining them creates a more comprehensive user profile. Common problems arise when combined filtering is lacking, including:

• The user profile is vague : it is only known that the user is "male" or "young," but it is impossible to determine whether they are a "young male student" or a "middle-aged male professional."

• Mismatched scenarios : Pushing workplace-related services to students, or campus activities to working professionals, results in extremely low relevance.

• ROI is difficult to improve : The extensive segmentation approach keeps marketing costs high but fails to bring proportional growth.

• Competitive disadvantage : When competitors have implemented dual-dimensional screening based on both gender and age, companies that still rely on a single dimension will naturally be eliminated by the market.

The essence of combined screening is to shift from "casting a wide net" to "targeted outreach ," so that each batch of numbers has a clear demographic label.

IV. Differentiated Operational Strategies Based on Zalo's Hierarchical Results

After completing the gender and age screening for Zalo, the next step is to develop differentiated operational strategies for different user segments. Simply screening without adjusting the strategy will still lead to:

• Content homogenization : Young women and middle-aged men receive the same text and images, lacking any targeted approach.

• Low engagement rates : Users feel like they're being bombarded with messages, lack a sense of personalization, and are unwilling to respond.

• Missed conversion points : Failure to adjust conversion paths according to age groups (e.g., guiding younger users to social pages, and guiding middle-aged users to private messaging).

• Low repurchase rate : Failure to maintain customer relationships based on demographics after the initial conversion, leading to rapid user churn.

Based on the screening results, it is recommended to create content libraries and communication templates for groups such as "young women", "working men", and "middle-aged and elderly users" to ensure that marketing actions are highly matched with user characteristics.

V. Key to Efficient Implementation: Achieving Precise Batch Layering Using Sieve Tools

When businesses need to acquire Zalo users on a large scale, manual screening is extremely inefficient and makes it difficult to guarantee data quality. Without reliable tools, businesses will face the following challenges:

• Manual operation is cumbersome : it is almost impossible to filter by gender and age one by one when faced with tens of thousands or even hundreds of thousands of numbers.

• Data update lag : User profiles may change over time, and static filtering results cannot reflect the latest status.

• The sources of the numbers are mixed : the quality of numbers obtained from different channels varies, making it difficult to categorize them uniformly.

• Execution efficiency bottleneck : The screening speed cannot keep up with the marketing pace, resulting in missed golden periods for the campaign.

In this step, combining ITG's full-domain filtering can completely solve the efficiency problem:

• Batch import, automated recognition : Supports batch import of Zalo phone number resources, automatically identifies and outputs key fields such as user gender, age range, profile picture status, and account activity level.

• Quickly combine and stratify : Helps businesses complete the "gender + age" combination filtering with one click, without manual processing.

• Flexible user pool building : Whether building a pool of young female users or filtering for working men, structured user profiles can be generated as needed.

In conclusion , Zalo is an indispensable traffic source in the Vietnamese market, and its gender and age filtering is the key to unlocking this market. From gender filtering to age segmentation, and then to combined user profiles and differentiated operations, each step determines the final customer acquisition efficiency. Only by standardizing and tooling the filtering process can businesses truly overcome the "difficulty in customer acquisition" and achieve precise growth with low cost and high conversion rates in the Vietnamese market.