How data cleaning tools can help filter out reachable

When many teams do overseas marketing, they will initially focus on"Get more data", but after entering the operation stage, the problem is often not that there is not enough data, but that the data is too messy.

The list is mixed with empty accounts, abnormal accounts, long-term silent users, duplicate data and low-quality traffic. On the surface, there are many customers, but there are fewer and fewer people who can actually reach, reply, and communicate for a long time.

Because of this, data cleaning tools have begun to become part of the front-end operation processes of many teams. Now more and more people are discovering that the subsequent mass messaging, advertising, customer service and private domain effects actually largely depend on whether the previous data has been cleaned.

Why"Reachable customers" are becoming increasingly difficult to screen

In the past, when the traffic environment was relatively simple, as long as the number or email address existed, some results would usually come out later. But it's different now. Although a large number of accounts are still there, users may have not used them for a long time; although many numbers can send messages, they rarely reply; and some data itself is batch generation or short-term marketing data, with very low subsequent value. If these data are not processed in advance, subsequent operational actions will become increasingly heavy and customer service workload will continue to increase, but the proportion of truly effective customers will not increase simultaneously.

What exactly are data cleaning tools cleaning?

Many people understand data cleaning as just removing duplicates or deleting empty numbers, but people who have really been doing data operations for a long time will break down cleaning into many layers. The first level is usually basic format processing, such as unified number format, national area code specification, and email format checking. The second level is basic status identification, such as whether the number is available, whether the account is opened, and whether the email is normal. The third layer only begins to enter user quality judgment, such as active status, long-term online behavior, abnormal status and user tags. Really valuable data cleaning is not just about deleting erroneous data, but also about screening out people who are more suitable for operations in advance.

Why active users are more important than regular users

Now many teams no longer pursue"The bigger the list, the better", but start looking at "the proportion of high-quality users". Because those who can really bring responses, interactions and conversions are often long-term active users. A user who is online for a long time and interacts continuously has a higher subsequent value than a dozen low-active accounts. If most of the users on the list are silent users, no matter how large the sending volume is, the subsequent customer service and private domain effects will not be stable. Therefore, many mature teams will now incorporate active screening into the data cleaning process instead of slowly eliminating it later.

Why data cleaning is suitable to be put in front of marketing

Many teams used to be accustomed to mass sending first, then checking the results, and finally screening users, but this method is now becoming more and more wasteful of resources. Because advertising costs are getting higher and higher, customer service investment is getting larger and larger, and mass mailing accounts are becoming more and more precious. If there are problems with data quality until later, the previous consumption has already been incurred. A more reasonable process is: clean first, then layer, and finally touch. In this way, subsequent advertising, mass messaging and customer service will be easier, and it will be easier to determine which users deserve to be focused on.

The focus of data cleaning in different industries is different

Not all data cleaning projects use the same set of logic. For example, cross-border e-commerce will pay more attention to activity and consumption tags, local services will pay more attention to region and long-term online status, and financial projects will pay more attention to long-term usage behavior and account stability. There are also some community projects that will pay extra attention to user interaction frequency and platform usage habits. Therefore, a truly practical data cleaning tool can usually not only detect basic status, but also support subsequent label classification and result stratification.

Data detection can help reduce back-end operational pressure

The problem for many teams is not that they don’t have customers, but that they have too much low-quality data. If there are a large number of invalid users in the list, subsequent customer service, private chats and group messages will be slowed down. In actual operation, you can first use Digital Planet to do screen number detection, filter unavailable data, abnormal accounts and low-active users in advance, and then combine industry labels and user status for further cleaning. Digital Planet supports free trial screening test. The data processed in this way is more suitable for direct entry into advertising, mass messaging and private domain systems.

The core of data cleaning is to make back-end operations more stable

Now more and more teams have realized that data cleaning is not an additional step, but a basic step. Because what really determines marketing effectiveness is no longer just sending capabilities, but front-end data quality. In the future, data cleaning tools will increasingly focus on user quality identification, long-term behavior analysis and active user screening. For a team that has been doing overseas marketing for a long time, what really matters is never how many lists you have, but how many customers you can continue to reach, interact with, and convert.

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