When doing e-commerce launches in the South American market, many teams will encounter similar problems: the sending volume is not low, but the reply never comes up. The data seems to be growing, but the actual conversion has not changed significantly. This situation is often not a problem with the delivery strategy, but rather with the data itself not being processed cleanly.
When the filter number step is added, the change in the response rate will be very obvious. The improvement to 50% or even 70% can be achieved after structural adjustment. The key lies in whether the screening logic is in place.
Original state: lots of data, but few effective users
Without filter numbers, the raw data is usually in a mixed state.
A batch of numbers will also include:
- Unregistered WhatsApp number
- Low active accounts that have not been used for a long time
- Numbers with abnormal or unavailable status
- Real users using it
If these data are used directly for delivery, the number of people actually reached will be far lower than the number sent, and it will naturally be difficult to increase the response.
Step 1: Remove unavailable numbers first
The first step to improvement is not to increase sending, but to reduce invalid sending.
Through number detection, you can first filter out unregistered or abnormal numbers. After this step is completed, all the sending targets will become "at least reachable" users.
Although the data volume will decrease, the reach base will be more stable.
In actual implementation, Amman can complete this layer of detection in batches, quickly identify the WhatsApp registration status, and directly eliminate unusable data.
Step 2: Screen out low active users
Available numbers do not equal valid users. Although many accounts have registered for WhatsApp, they have not used it for a long time. Even if such users are contacted, it is difficult to generate feedback.
Continuing to filter activity levels, you can filter out these low-value users and retain only accounts with usage behavior.
This layer is the key to improving the response rate because it directly determines whether the message will be seen.
Step 3: Make the data structure centralized
After two rounds of screening, the data will undergo an obvious change: the number will be reduced, but the structure will be more concentrated.
The originally mixed data is compressed into a group of users who are “reachable + have usage behavior”. The sending target changes from a random group of people to a part closer to the target users.
After this step is completed, the actual feedback will increase significantly even if the sending volume decreases.
Why the response rate can be increased to 70%
The improvement of the response rate is not essentially a technical issue, but a change in the target being reached.
When there are fewer invalid numbers and less active users in the data, the remaining users are more likely to see the message and more likely to respond. The originally wasted sending times began to be concentrated on effective users.
In this case, the response rate will naturally increase instead of relying on additional actions.
Batch screening allows this method to be stably reused
If it is just a one-time screening, the effect may be accidental. What is really valuable is to fix this process so that every batch of data is processed according to the same logic.
Through Amman, number detection and activity screening can be completed in the same process, batch processing is supported, and API can also be accessed. In this way, when new data comes in, the filtering can be completed directly without repeated operations.
From "hair more" to "hair more accurately"
In South American e-commerce advertising, the real change is not to increase the number of contacts, but to reduce invalid contacts.
When the data changes from a messy state to a clearly structured part, each send is closer to the effective user. The improvement in response rate is not accidental, but the natural result of screening.
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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