How to find high-spending people over 45 years old? It is more stable to use device identification and active status together

In the screening of high-value users,The high-spending group over 45 years old has always been an underestimated category of data. Many people do user screening and tend to focus on users over 25 years old and users over 35 years old, but ignore that people over 45 years old have more stable value in many industries. Especially in high-customer single products, long-term services, and local high-net-worth consumption scenarios, this group of people is often not the largest in number, but they are likely to be of higher quality.

The problem is that this group of people is not easy to find based solely on age tags. if only doIf you screen for people over 45 years old, the screened data will easily only match the age group, but they may not necessarily be high-spending people. This is why more and more people are beginning to use device identification and activity status together, because looking at one of the dimensions alone is often not stable enough.

WhyHigh-spending people over 45 years old are increasingly worthy of separate screening

The reason is very practical. High consumption power is not evenly distributed.Users over the age of 45 are more likely to have stable spending power and long-term value in many businesses.

Especially for high-customer single products, health services, and asset-based businesses. Although the decision-making cycle of this group of people is not always fast, the value is often higher than the general traffic.

This is whyThe screening of high-spending people over 45 years old has recently been increasingly included in the refined user screening logic.

The point is not that a new crowd appears, but that serious screening begins now.

Why can’t such users be found by age tags alone?

Because the age is appropriate, it does not mean high consumption.

Among users over 45 years old, there will also be high-value users and ordinary users.

If you only rely on age tags, it is equivalent to only solving the direction of the basic population and not solving the problem of user quality.

This is why many people doScreening for users over 45 years old, the effect is unstable. The problem is not necessarily that the crowd is wrong, but that the screening dimensions are too single.

If the goal is to findFor high-spending people over 45 years old, age alone is not enough.

What is the use of device identification in this type of user filtering?

The value of device identification does not lie in directly telling you who is a high spender, but in assisting in determining the user level.

Because the user structure of some devices has certain reference significance in terms of consumption power and usage habits.

Therefore, device identification is often used asA layer of supplement for filtering users over 45 years old.

It does not determine the results alone, but combined with the age tag, it can often improve the screening accuracy.

This is why device identification screening numbers are now more and more often discussed together with high-value population screening.

Why active status must be viewed together

Because high-consumption groups also need real usage behavior.

If you only look at age and equipment without looking at activity status, you are likely to screen outData that "looks good" but isn't suitable for reach.

Active user screening solves whether the data is currently valuable.

especiallyUsers over the age of 45 cannot be highly active by default, so this step cannot be omitted.

This is why device identification and activity status are used together, which is more stable than just looking at the device alone.

When device identification and active status are used together, what is the most reasonable combination?

From a practical point of view, the more reasonable order is usually not to look at the equipment first, but to screen for activity first and then look at the equipment.

First, filter active users to filter out obviously low-activity data.

Combined with device identification, further perform crowd quality filtering.

finally combineAge tags over 45 years old to extract the target group.

This order is important, because if the order is reversed, it is often easy to waste judgment actions on low-activity data.

And if active first, then device, then age, the filtered data structure will usually be more stable.

Which step is suitable for accessing Digital Planet?

At this stage, many teams will use Digital Planet toThe combined processing of user screening for users over 45 years old, active user identification and device identification. Digital Planet supports free trial screen number detection, which is suitable for filtering high-value groups, device identification screen numbers and screening of highly active users. It can separate high-spending groups over 45 years old and do separate stratification.

The value of doing this is not just to screen outUsers over 45 years old are closer to identifying high-quality users over 45 years old.

Compared with age screening alone, this method is usually much more stable.

WhyScreening for high-spending people over 45 years old is becoming more and more like combined screening

Because it is becoming increasingly difficult to solve the problem with a single label.Age label solution direction.Equipment identification assists in judging quality.The active state solves the use value.These three dimensions originally solve different problems.

If you take them apart and look at them individually, the accuracy is limited; when combined, you can get closer to the target group.This is whyScreening for high-spending people over 45 years old is now more and more like a combination screening rather than a simple age screening.

The logic of screening high-value groups has actually changed

In the past, the focus was on finding people of the right age first.Now the focus is on finding people who are age-appropriate, of higher quality, and worth reaching.

This is a completely different level.The use of device identification and active status together is essentially part of this change.The competition in the future will not just be whether there isData on users over 45 years old, but who knows better how to screen out high-spending people over 45 years old.

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