Which industries are suitable for number filtering first? E-commerce, finance or community operations.

In recent years, number filtering has not only been done by data service providers, but also more and more teams engaged in cross-border operations have begun to incorporate it into their own front-end processes. especiallyAs the amount of data on platforms such as WhatsApp, Telegram, LINE, and Facebook increases, many teams will encounter the same problem: the list is growing, but the number of people who can actually reply, close deals, and operate in the long term has not increased simultaneously. It is precisely because of this that "filter first, then operate" has begun to become the default action of more and more teams.

However, different industries have different needs for number filtering. Some industries value activity more, some industries value long-term stability, and some industries pay more attention to interactive behavior and private domain precipitation. Therefore, number filtering is not a unified logic, but should be viewed in conjunction with business scenarios.

Why did the e-commerce industry first begin to pay attention to number filtering?

Cross-border e-commerce is one of the first industries to use number filtering extensively. Because e-commerce itself consumes traffic very quickly, advertising costs are also getting higher and higher. If a large number of low-quality users are mixed into a batch of numbers, subsequent customer service, mass mailing and repurchase will become increasingly difficult.

The previous thinking of many e-commerce teams was: first place a large amount of advertising, make the list as large as possible, and then slowly screen out customers. But now more and more people are discovering that this approach will lead to increasing pressure on back-end customer service. especiallyIn the WhatsApp private domain scenario, a large number of low-active users will continue to occupy sending resources. Customer service is busy responding every day, but it is difficult to promote truly effective transactions.

Therefore, many mature e-commerce teams have now begun to filter numbers in advance. First filter out empty accounts, abnormal accounts, and long-term silent users, and then import highly active users into the customer service system first. In this way, subsequent private chats, repurchases and membership operations will be much more stable.

Especially e-commerce teams working in long-term private domains will increasingly rely on active user screening.

Why the financial industry pays more attention to"Long-term stable user"

The biggest difference between the financial industry and e-commerce is that it relies more on long-term trust. Whether it is investment consulting, trading platforms or high-order financial products, the truly valuable users are usually not short-term interactive users.

Therefore, when filtering numbers for financial projects, the focus is usually not just"Whether the number can be used", but "whether this user is a long-term stable user".

For example, many financial teams will pay special attention to:

long-term online behavior;

long-term usage habits;

Account stability;

Active duration.

Because what the financial industry fears most is not a few users, but too many low-quality users.

If a batch of numbers contains a large number of short-term registered users, abnormal accounts, or low-active users, it will be difficult for subsequent customer service and communities to establish stable trust.

Because of this, the financial industry usually pays more attention to user quality than other industries, rather than simply pursuing list size.

Why community operations increasingly rely on active screening

Many community operation teams used to like to pursue"Group size". But now more and more people have realized that the number of people does not equal activity.

especiallyAfter a large number of low-active users enter Telegram, WhatsApp and Facebook communities, the most direct problem is: the group looks large, but the interaction is getting lower and lower.

Many teams will later discover that aA highly interactive group of 500 people may be worth more than a silent group of 5,000 people.

Therefore, more and more community teams now complete number filtering and activity screening before users enter the community. Because people who are truly suitable for long-term community operations usually have relatively obvious long-term online behaviors and interaction habits.

If the front-end does not filter, the communities behind will become more and more like"Number stacking" rather than effective user pool.

Many mature community teams have now begun to screen highly active users before deciding who to enter key communities.

Why do different industries have different filtering priorities?

Although number filtering is"Screening users", but what different businesses really value is different.

E-commerce pays more attention to repeat purchases and long-term activity, because it requires continuous private chat and membership operations later. Finance pays more attention to stability and long-term behavior because the user trust cycle is longer. Community operations rely more on interactive behavior, because the real community value comes from active users.

So now more and more people are beginning to realize that number filtering is not a unified template, but industry logic.

The same batch of data may have completely different priorities in different businesses.

This is why more and more teams will:

activity;

region tag;

Device information;

long-term behavior;

Platform status;

Put them into the filtration process together.

Because only in this way can subsequent operations be more accurate.

Digital Planet is more suitable for front-end data filtering

Many teams are most afraid of two problems when doing number filtering: first, the data quality is unstable, and second, the subsequent system is getting more and more chaotic. In actual operation, you can first use Digital Planet to do screen number detection, filter empty accounts, abnormal accounts and low-active users in advance, and then combine different business scenarios to complete further stratification. Digital Planet supports free trial screening test.

For example, e-commerce projects can focus on screening high-active users, financial projects can focus on long-term behavior, and community projects are more suitable to prioritize retaining highly interactive users.

This will make subsequent customer service, mass messaging and private domain operations significantly easier.

Number filtering will become more and more biased in the future"Industrialization"

In the past, many teams did number filtering, which was more like a unified screen number. Now more and more people are starting to adjust the rules by industry. Because what is really important in the future is no longer the amount of data, but the match between data and business.

E-commerce needs repeat users, finance needs stable users, and communities need active users. Different businesses, yesThe definition of a "high quality number" itself differs.

In the future, number filtering will increasingly focus on industry-based, long-term behavior identification and user label stratification. Because what really determines the back-end effect is never the number itself, but the person behind the number.

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