After Telegram numbers are screened, many teams will continue to use the original playstyle by default, such as batch messaging, unified speaking skills, and reaching out at the same rhythm. However, after actual running, you will find that the effect has not been significantly improved, or even not as good as expected.
The reason is simple: the data has changed, but the style of play has not. The data after screening has essentially changed from "mixed traffic" to "structured users". If it is still used in an extensive way, the advantages brought by this step of screening will be wasted.
Refined contact is not extra work, but the next step that must be done after screening.
The data structure changes and the access method must be upgraded simultaneously.
In unscreened data, user status is mixed, and unified sending is a "have to" choice. But after screening, the data already has basic stratification, such as:
- Reachable users (activated)
- Active users (active users)
- Users at different levels of activity
This means that each type of user should be reached in a different way. If you still send them uniformly, two problems will arise:
- Highly active users are reached inefficiently, wasting conversion opportunities
- Medium and low active users are over-reached, affecting overall performance
The core of refined access is to take advantage of data rather than continue to process it equally.
The core of refined touch is not complexity, but layering
Many people understand refinement as a complex process, but the essence is actually very simple, which is "doing different actions according to the user's status."
You can start with the three most basic layers:
- Highly active users
Already have obvious usage behavior, give priority to conversion and contact
- Medium active users
Used but not frequently, suitable for booting and testing
- Low active users
Unstable use and reduced contact frequency
After passing Amman screening, this stratification can be obtained directly without the need for additional judgment.
Reaching strategies for highly active users
Highly active users are the most valuable layer, and the focus of reaching them is “quickly establishing communication”.
Suitable methods include:
- Chat privately instead of sending a group message first
- The content is more concise and the core values are highlighted.
- Appropriately increase interactive guidance, such as replying to keywords
The characteristic of this type of users is that they can “see and respond”. The key is to seize the window period instead of reaching out repeatedly.
Medium active users are more suitable for testing
Medium active users are characterized by instability, sometimes responding and sometimes not.
This layer is more suitable for testing strategies, such as:
- different opening methods
- Different content directions
- Different delivery times
Through small-batch testing, you can screen out a subset of users who are easier to convert, and then upgrade them to key reach targets.
Low active users should not be used frequently
If low-active users are repeatedly contacted, two problems will arise:
- Lower the overall response rate
- Increase the risk of account use
A more reasonable way is:
- Control sending frequency
- Extend usage time
- Or do the light touch test alone
This type of data is not completely useless, but it is not suitable for use as main data.
Reaching rhythm is more important than reaching quantity
In refined touch, rhythm control is more critical than quantity.
A few practical principles:
- Do not contact the same user multiple times in a short period of time
- The same batch of data is sent in batches instead of all at once
- Users at different levels use different rhythms
This approach can make the overall behavior closer to real communication rather than batch operations.
Use Amman to make refined touch easier to implement
The prerequisite for refined access is that the data has a structure, otherwise it cannot be executed.
When passing Amman, this can be done at the screening stage:
- Telegram account opening detection
- Activity recognition
- Data hierarchical label output
The data imported into the system in this way contains layered information and can be directly used for different reach strategies instead of being sorted out later.
It also supports API access, allowing each batch of new data to automatically complete screening and stratification, reducing manual intervention.
Where is the effect of refined touch reflected?
When the contact method matches the data structure, there will be several obvious changes:
- Response rate is more stable
- High-value users are more concentrated
- Data fluctuations are significantly reduced
Compared with simply increasing the sending volume, this method is easier to create a lasting effect.
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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