How to make batch Medium filtering more efficient: account status, user quality and active tags are processed at once

MediumThe changes in the past two years have been obvious. In the past, many teams regarded it as a pure content platform, but now more and more teams are doing overseas promotion, independent websites,AItool,SaaSPeople who pay for knowledge have begun toMediumUsers are treated as a special type of traffic. The reason is very practical. This type of users naturally have reading habits, content consumption ability and certain active search behaviors. Compared with a large amount of general traffic, the stability of subsequent conversions is often higher.

But the problem is becoming more and more obvious. Many teams obtain it in batchesMediumAfter entering the account or related users, you will directly enter the group sending, email, private chat or advertising redirection process. After running for a while, you will find that: there is a lot of data, but not many people are really effective. The list seems huge, but the number of people who actually interact consistently is getting smaller and smaller.

Many times the problem is not with traffic, but with filtering.

WhyMore and more people are paying attention to Medium data

MediumIt’s not the same as ordinary social platforms. Many platforms focus on entertainment and real-time interaction, whileMediumUsers prefer content consumption and active reading.

This means that users themselves will leave more behavioral characteristics.

For example:

Reading habits; long-term attention direction; continuous active account behavior; content preferences; long-term online patterns.

For many projects, this type of data is naturally easier to filter further.

Especially in:

AITools; cross-borderSaaS;Financial information;Knowledge products;Software services;

In these scenes,MediumThe subsequent user value is usually significantly higher than ordinary general traffic.

However, the larger the data size, the proportion of mixed low-quality accounts will also increase simultaneously.

Why can’t we directly import operations in batches?

Many teams are most prone to a situation:

Once the list is in hand, import it directly.

Then start:

Bulk email; advertising remarketing; private chat reach; community operation.

It may seem very efficient in the short term, but it usually appears later: the open rate drops, the interaction rate drops, and there are fewer and fewer replies.

The reason is not complicated.

Because the list is usually mixed with:

Long-term silent accounts; low-active users; abnormal accounts; short-term behavioral accounts; low-value extensive reading users.

If these data are not processed, the entire operational efficiency will be lowered later.

Therefore, truly mature teams usually do not start, but screen first.

What exactly does batch screening screen for?

When many people understand screening, their first reaction is to remove duplicates.

But in real long-term operations, deduplication is only the first step.

What more and more teams will really look at now is:

Account status; long-term usage; active behavior; online patterns; user quality labels.

because:

Account exists≠The account is active;

Account active≠long term value.

For example, for the same two users, one has been reading for a long timeAITool content, one is opened once in a while, and the values ​​behind the two are completely different.

Therefore, the focus of screening has begun to shift from"Do you have an account?"become"Is the account worth operating?".

Why active tags are increasingly important

In the past, many teams liked unified marketing.

All users:

Unified import; unified sending; unified reach.

But now more and more people are finding that running this way is getting harder and harder.

Because the subsequent performance gap between different active users will be very large.

Highly active users usually mean:

Long-term online; continuous interaction; easier to reply; easier to enter the private domain.

Low active users may:

Not opened for a long time; almost no interaction; quickly silenced after short-term registration.

If operated uniformly, customer service and marketing resources will be quickly diluted.

So now more and more teams are starting to do active tags first.

Why are more and more projects starting to layer user quality?

An obvious change in recent years is:

People are talking less and less about traffic scale, and more and more about user quality.

Because many teams have realized later:

What really affects the results is not how many users are imported, but the proportion of high-quality users.

Especially when:

email marketing;SaaSPromotion; community operation; knowledge products;

In these scenarios, the proportion of high-quality users usually directly determines the subsequent results.

Many mature teams will now complete:

Account status identification; activity screening; long-term behavior detection; user tag classification.

In actual operation, you can first use Digital Planet for screening detection, and then combine active and long-term behavior tags for further screening. Digital Planet supports free trial screening test.

In this way, subsequent contact, marketing and private domain undertakings will be much more stable.

Medium screening will become more and more like “user identification” in the future

In the past few years, what everyone compared was:

Who has a bigger list; who gets it faster; who has more data.

Now more and more people are starting to compare:

Whose data is cleaner; who has more long-term users; who has a higher proportion of high-quality users.

Because what is really important in the future is no longer how many accounts you get, but how many people you can screen out in advance who are worthy of long-term operations.

Especially in content-based platforms, what is truly scarce is never accounts, but people who are continuously active.

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