In Facebook data acquisition and advertising delivery, account quality will directly affect the performance of all subsequent links. The data seems to be a lot, but if a large number of fake accounts are mixed into it, the delivery effect, conversion judgment, and even customer service follow-up will be delayed.
Filtering fake accounts is not an optimization action, but a basic action. Only by doing this step well can the subsequent data have reference value.
Common types of Facebook fake accounts
In actual data, fake accounts are not just "completely fake", but more of a collection of various low-quality or abnormal states.
Common ones include:
- Register accounts in batches with simple information and single behavior
- Accounts that have been inactive for a long time and have almost no usage behavior
- Accounts with abnormal behavior and interaction patterns that are not in line with normal users
These accounts exist in the system, but it is difficult to generate real interactions or conversions.
The direct impact of fake accounts
If no filtering is done, such accounts will be mixed with normal data, causing a series of problems.
Advertising data is distorted, and clicks or interactions cannot reflect real user behavior
The conversion rate is lowered and it is difficult to judge whether the strategy is effective.
Customer service resources are occupied and follow-up efficiency decreases.
These problems do not appear alone, but stack up together, making the overall effect increasingly difficult to control.
Why a lot of data “looks normal but is unusable”
In actual operations, many teams will encounter a situation: the amount of data is large, but the effect is always unstable.
The reason is usually not a channel problem, but a data structure problem.
Accounts with different statuses are mixed into a batch of data:
Some are real users
Some are low active accounts
Some are abnormal or fake accounts
In this case, even if you continue to optimize your ads or content, it will be difficult to get stable results.
Basic ideas for filtering fake accounts
Filtering is not a simple deletion, but a step-by-step filtering through multiple dimensions.
You can start from three directions:
Confirm whether the account actually exists
Screen active users and eliminate accounts that have not been used for a long time.
Identify abnormal behavior and filter unstable accounts
In this way, the original data can be gradually compressed into a part that is closer to the real user.
An executable filtering process
In actual operation, the filtering steps can be split and executed instead of being judged all at once.
Import the original data first
Perform basic checks to confirm account status
Filter active users
Filter abnormal accounts
Output available data
Through this set of processes, it can be ensured that the data entering subsequent steps is processed and not in a mixed state.
The actual role of Amman in filtration
In large-scale data processing, manual filtering is difficult to ensure efficiency and consistency. Through Amman, batch testing can be completed before the data enters use.
Can be achieved:
- Identify account status in batches
- Activity filter
- Abnormal account filtering
- Output structured tagged data
In this way, each batch of data has completed basic filtering before entering the advertising or reaching process, instead of being processed later.
It also supports API access, which allows you to embed filtering capabilities into the system so that data can be automatically processed when imported.
Changes after data filtering is completed
When the fake accounts are cleared, there will be several obvious changes.
Data size may be reduced, but quality is more focused
Delivery feedback is more stable
Conversion paths are easier to determine
Only at this time will it be meaningful to optimize advertising or adjust content.
Data quality determines all subsequent results
In Facebook operations, many problems seem to be delivery problems, but the source is often data.
If the proportion of fake accounts in the data is too high, any optimization will be disrupted. Only after filtering is completed, the data is available and subsequent actions are meaningful.
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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