The increasing difficulty of Viber customer acquisition is rooted in data quality and user structure. This article breaks down data filtering, active user identification, and conversion pathways.
In recent years, more and more businesses have realized a growing challenge when acquiring customers through Viber: customer acquisition is becoming increasingly difficult, and conversion rates are dropping. Many attribute this to increased market competition or lackluster marketing content, but in reality, the root cause often lies in the data itself. Inaccurate data sources, low-quality users, and inactive accounts can directly impact marketing performance.
Understanding the Challenges of Viber Customer Acquisition
Viber, covering Eastern Europe, the Middle East, and Southeast Asia, has a large user base. However, a large number of users does not guarantee high marketing efficiency. Using unfiltered data can significantly reduce conversion performance.
Declining Data Quality
As competition intensifies, multiple companies are relying on the same data sources, leading to high duplication and lower quality. Some sources are unverified, containing many invalid numbers.
Low Active User Ratio
Even if a user has registered on Viber, it doesn’t mean they are active. Many accounts are dormant or used only occasionally, reducing the effectiveness of marketing messages.
Poor User Demand Matching
Without user profiling, marketing content may not align with user needs, lowering click-through rates and conversions.
Data Filtering: The First Step to Improved Acquisition
High-quality data is the foundation of effective Viber marketing. Accurate and filtered data can significantly boost conversion, while poor data will drag down results.
Global Phone Number Validation
Checking if phone numbers are valid and registered on Viber allows businesses to quickly remove invalid data and enhance dataset quality.
Duplicate Data Removal
Removing duplicates from multiple data sources avoids redundant outreach and reduces resource waste.
Regional and User Segmentation
Selecting users by target regions and applying behavioral filters ensures alignment with business goals.
Active User Identification: Key to Conversion
After basic data filtering, identifying active users is critical. Only genuinely active users have conversion potential.
Online Behavior Analysis
Analyze login frequency and online duration to determine activity levels.
Message Interaction Frequency
Track message sending, replies, and group participation to identify highly interactive users.
Active Time Distribution
Understanding when users are active helps optimize marketing campaign timing.
From Data to Conversion: End-to-End Process
Filtering data is just the first step. Achieving higher conversion requires a complete process including reach, engagement, and conversion.
Precise Reach
Filtered high-quality data allows for precise messaging and higher delivery rates.
Content Alignment
Marketing content tailored to user profiles improves engagement.
Conversion Guidance
Limited-time offers and promotions can guide users to complete conversions.
Case Study: Data Optimization Boosts Results
A company using unfiltered Viber data initially had a conversion rate of only 6%. After implementing data filtering and active user identification, the conversion rate rose above 28%, while acquisition costs dropped by over 40%.
Building a Long-Term Data System
Sustainable Viber marketing success requires a complete data system, including collection, filtering, analysis, and application, to ensure stable growth.
Continuous Data Updates
Regular updates ensure data remains accurate and current.
User Tagging and Segmentation
Segment users through tags to improve targeting accuracy.
Automation Tools
Utilizing automation improves processing efficiency and reduces manual effort.
SuperX — The World’s Leading Data Filtering Platform
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With high-concurrency processing and intelligent algorithms, SuperX enables businesses to quickly acquire real user data, optimize marketing performance, and significantly reduce customer acquisition costs.
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