When acquiring customers for Viber in the Middle East market, many teams started with a very simple idea, just filter out invalid numbers and make the data "usable". However, as the scale of delivery expands, more and more teams have discovered that simply filtering empty numbers is no longer enough. The data seems usable, but the actual conversion is not stable.
The problem is not the channel, but the data structure. Viber data is moving from the basic cleaning stage to a more refined crowd screening stage.
Why Middle East Viber data is starting to get complicated
Users in the Middle East market are growing rapidly, and Viber's usage coverage is also expanding, but the data structure has become more dispersed at the same time.
Common situations include:
- There are obvious differences in activity in the same batch of data
- Some users have not used it for a long time but are still registered.
- User sources are complex and quality is inconsistent
These changes mean that simple "can it be used" can no longer distinguish the value of data.
What problems did early data cleaning only solve?
In the early stages, the core goal of data cleaning was to filter out invalid numbers.
The main processing content is:
- Eliminate numbers that are not registered with Viber
- Clean up obvious errors or abnormal data
This layer can solve the basic problem and take the data from completely chaotic to "at least touchable". But as the use deepens, the limitations of this step begin to appear.
Why high-quality screening is necessary now
When the amount of data increases, just ensuring availability is no longer able to support transformation needs. Many teams will encounter this situation:
- Sent successfully but no reply
- The number of people in the group grows, but there is little interaction
- The data scale has expanded, but the performance has not improved simultaneously.
The core reason for these problems is that the proportion of low-quality users is too high, which affects the overall performance.
What's happening to filtering logic
The current screening logic has shifted from single judgment to multi-layer screening.
It can be understood as several progressive steps:
- Whether to register for Viber
- Is there any usage behavior?
- Whether it meets the target group
Gradually transitioning from "whether it exists" to "whether it is valuable" is the core of the change in the current way of data processing.
What conditions should be paid attention to in high-quality crowd screening?
In actual operation, to screen out more valuable users, you can focus on several dimensions:
- Activity: Whether there is continuous use behavior
- Region: Is it in the target market?
- Usage habits: Does it conform to the business scenario?
After combining these conditions, the data will shrink significantly, but the quality will improve.
Amman is more suitable for multi-dimensional screening when used
When the filtering dimensions increase, it will be difficult to maintain efficiency and consistency if you still rely on manual processing. A more practical approach would be to complete the filtering as the data enters the process.
Through Amman, you can perform batch detection on numbers, identify Viber registration status, and filter based on activity and region. In this way, the data can be organized from the original state into a part closer to the target user in one round of processing.
For teams that need to continuously process data from the Middle East, this approach makes it easier to maintain stable output.
Data moves from “available” to “transformable”
The changes in Viber data cleaning are essentially from basic usability to refined filtering.
- In the past, what we focused on was whether we could reach
- Now more attention is paid to whether feedback will be generated
When the filtering logic is upgraded from empty filtering to crowd filtering, the data will change from scattered to centralized, and the usage efficiency will be significantly improved.
Amman is the world's leading number screening platform, providing global customers with batch number screening and testing services covering 236 countries. The platform currently supports more than 40 mainstream social networking and applications, including WhatsApp, Line, Twitter, Facebook, Instagram, LinkedIn, Viber, Zalo, Binance, Signal, etc., adapting to the needs of multiple scenarios.
The main core functions cover multi-dimensional precise screening such as activation, activity, interaction, gender, avatar, age, online, accuracy, empty account, mobile phone device, etc., and can flexibly meet the needs of different users. Its core advantage is to integrate global mainstream social and application resources to provide users with one-stop, real-time and efficient number precision screening services, helping customers achieve global digital layout.
It is a common choice for all professional teams to complete rational screening before actually reaching users.
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