As batch reach becomes more and more common, many people are accustomed to judging the effect by the volume sent, such as how many messages were sent and how many users were covered. However, the actual results are often not ideal, the volume of sending is not low, and there are very few people who actually respond.
The problem is usually not with the sending itself, but with the data structure. If the data contains a large number of accounts with low activity or even no use, no matter how large the coverage is, it will not translate into effective feedback.
Coverage rate and response rate are essentially two sets of logic
Reach focuses on how many people are reached, while response rate focuses on how many people actually respond. The two are not growing at the same time.
In actual use, a situation often occurs: the number of people covered increases, but the number of responses does not change significantly, or even decreases. This situation usually indicates a high proportion of low-quality users in the data.
Therefore, simply expanding coverage will not solve the problem, but may cover up the problems with the data itself.
Activity determines whether a message is seen
Account activity is the most direct factor affecting response rate.
If a user does not log in for a long time, or rarely checks messages, it will be difficult to interact even if the number is available. Once the proportion of such accounts in the data increases, the overall effect will be reduced.
The meaning of screening active users is to try to make every contact fall on "people who are likely to see the information" instead of wasting it on inactive users.
What will be the consequences of not doing active screening?
If you skip the level of activity filtering, common situations include:
Send volume continues to increase, but responses do not
Data performance is unstable, with large differences between different batches
It’s hard to tell whether the problem lies with words or data
These problems can make subsequent optimization difficult because the data itself is unstable.
How to improve response rates through screening
In the screening process, activity can be regarded as one of the key conditions instead of an additional condition.
A more reasonable process is:
Do a basic test first to make sure the number is available
Screen the registration status again to ensure that the account exists
Finally, filter activity and only retain users with usage behavior
Through this process, the data gradually converges to a subset that is more likely to generate a response.
Batch processing is the key to improving efficiency
When the amount of data is large, it is basically not feasible to manually judge activity, and it is difficult to maintain consistent standards.
Through Amman, the active status of the account can be directly identified during the screening stage, and low-active users can be filtered out in advance. The entire process supports batch processing and can be completed in one round of screening without the need for step-by-step operations.
The data obtained in this way can be used directly for contact without the need for repeated screening.
Only when the response rate is stable can there be room for optimization.
When the data structure is stable, the response rate will also become more controllable. Only at this time can you adjust your words or strategies to have a clear direction.
If the data itself fluctuates greatly, even if the sending method is continuously optimized, it will be difficult to judge which adjustments are effective.
From focusing on quantity to focusing on quality
In batch mass sending scenarios, data quality is more important than data size.
The function of activity filtering is to change the target of contact from "people who may exist" to "people who are currently using it". When this layer is handled in place, the overall response rate will be more stable and it will be easier to continuously optimize.
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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