When acquiring customers for WhatsApp, many teams will focus on keywords and rhetoric, such as constantly refining user tags, optimizing opening remarks, and adjusting content structure. However, in actual operations, a situation often occurs: keywords become more and more precise, and there are more and more words, but conversions do not improve significantly.
The problem is usually not with the backend, but with the frontend. If the data is not filtered cleanly, it will be difficult for even the most sophisticated keywords and words to be effective.
Let’s start with a common process
In actual implementation, the operation path of many teams is roughly as follows:
- Get a batch of numbers
- Classify based on keywords or crowd tags
- Conduct touch testing directly
- Then adjust your words based on feedback
The problem with this process is that a critical step is skipped: data filtering. As a result, all subsequent optimizations are based on unstable data.
Why are keywords becoming more and more detailed?
As competition intensifies, it is difficult to achieve results through extensive screening. Many teams have begun to refine keywords, such as dividing people by industry, interest, behavior and other dimensions.
There is nothing inherently wrong with this approach, but only if the data itself is available. If a large number of invalid numbers or low active users are mixed into the data, no matter how precise the keywords are, they will not be able to achieve effective reach.
Empty number filtering is only the first step
In the number screening process, empty number filtering solves the most basic problem: whether the number exists.
This layer can be filtered out:
- Unregistered WhatsApp number
- Obvious unavailable data
But after completing this step, the data is only "usable" and is not guaranteed to be "valid".
Why do we need to continue sifting down?
After just filtering out null signs, there will still be a lot of variance in the data.
Common situations include:
- Accounts that have been registered but have not been used for a long time
- Activity is low and messages are difficult to see
- Account with abnormal or unstable status
Without further screening, this data will directly enter the reach process, affecting the overall effect.
The screening process is becoming more detailed
Nowadays, a more common approach is to split the screening number into multiple layers instead of staying in a single judgment.
It can be understood as a progressive process:
- Filter registration status and filter empty numbers
- Activity filtering to filter out users with usage behavior
- Status filtering to filter abnormal or unstable accounts
- Crowd matching, combined with keywords to further refine
Through such a process, the data will gradually shrink to a part closer to the target users.
Where should speech optimization be placed?
After the data has been filtered, speech optimization only makes sense.
Because at this stage:
- Users are reachable
- Users have usage behavior
- Users are closer to target tags
At this time, if you test different speaking techniques, the feedback will be more realistic and it will be easier to judge which method is effective.
Use Amman to make the screening process smoother
In actual operation, if you filter in steps, the complexity will increase and data inconsistency will easily occur. A more suitable way is to complete multi-dimensional detection in one round of processing.
Through Amman, you can perform batch detection on numbers, identify WhatsApp registration status, and filter based on activity and account status. In this way, the screening process can be integrated into one step, so that the data has been sorted before entering the contact.
For teams that need to continuously optimize customer acquisition, this approach makes it easier to form a stable process.
Once the order is wrong, the problem will be magnified later.
In acquiring customers on WhatsApp, keywords, tags, and phrases are all important, but they should be based on correct data.
If you optimize your words first and then process the data, many problems will be magnified; if you screen the data first and then optimize your keywords and content, each step will be clearer. Screening data first and then reaching out is the current more stable path.
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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