How to quickly differentiate between Android users and Apple users?

In the past, when many teams were doing user operations, they paid more attention to region, age, and activity. Now, more and more projects are beginning to pay attention to an additional label: whether the user uses Android or Apple.

The reason is not the device itself, but the user behaviors corresponding to different devices are becoming increasingly different. Especially in cross-border, e-commerce, finance, currency circles and high-end customer single projects, device tags have begun to directly affect subsequent conversion efficiency.

Many teams will ignore this dimension in the early stage, but after a long time, they will find that for the same batch of traffic, the differences in the interaction methods, consumption habits, and long-term retention capabilities of Android users and Apple users will become more and more obvious.

Why more teams are starting to differentiate between device users

In the early days of the mobile Internet, device tags were not that important because most teams were more concerned about traffic scale.

But now:

lThe cost of acquiring customers is getting higher and higher

lThe private domain operation cycle is getting longer and longer

lThe difference in user quality is becoming more and more obvious

In this case, the device tag starts to become a new filtering dimension.

In particular, many high-customer single projects have discovered:

The overall long-term value of Apple users is usually more stable than pan-Android traffic.

Therefore, more and more teams now add a layer of device labels in addition to number, region, and activity.

What are the usual differences between Android users and Apple users?

Not all Apple users are necessarily high-value, but in a large amount of data, some patterns do appear.

For example:

Apple users typically:

lEquipment replacement cycle is longer

lLong-term usage habits are more stable

lEasier to stay in the same account system

lMore active in finance, high customer orders and long-term private domain scenarios

And Android users:

lThe quantity is usually more

lA higher proportion of flood traffic

lActive fluctuations will be more obvious

lMore prone to short-term traffic and low-quality users mixing in

Therefore, many teams will now prioritize separate operations for Apple users.

Why financial and high-profile projects prefer to screen Apple users

What the high-customer single projects fear most is not a small amount of traffic, but too much low-quality traffic.

because:

lHigh customer service costs

lLong conversion cycle

lDifficulty in long-term maintenance for users

If a large number of low-quality users enter the front-end, private chat and community operations will become increasingly tiring.

Therefore, many financial, membership, currency circle and long-term private domain projects will be combined with:

liOS device tags

lLong active state

lRegion and age group

llong-term online behavior

Do user screening together.

Although this method will reduce the amount of data in the early stage, the subsequent operations will be much more stable.

Differences in actual marketing use between Android and Apple users

Many people think that device tags are just"It's just a look", but in fact, it has now begun to affect many operational actions.

For example:

Advertising

Users on different devices have significantly different clicking and conversion habits.

Private domain operation

Apple users are generally better suited for long-term maintenance.

Financial and currency circle projects

It is easier to screen out long-term stable users.

High customer single product

Pay more attention to long-term consumption power and user quality.

Therefore, equipment labels are no longer just auxiliary information, but have begun to affect the rhythm of subsequent operations.

Quickly differentiate device users, what they usually do

The more common methods now generally include:

lNumber detection

lSystem identification

lDevice label layering

lactive behavior analysis

Because it is increasingly difficult to judge the quality of real users simply by looking at numbers.

Especially in the long-term private domain:

Available numbers do not equal high-quality users;

Active users do not equal long-term users;

Online users are not equal to high-value users.

Therefore, many teams now include device tags in the filter.

Before device screening, the basic data should be processed cleanly and more stably.

If the data itself exists:

lEmpty number

lAbnormal account

lLong-term silent users

Then the subsequent device analysis will also be distorted.

In actual operation, you can first use Digital Planet to do screen number detection, and then combine it withiOS and Android tags for further filtering. Digital Planet supports free trial screening test.

This allows subsequent users to layer more accurately.

Why device labels are increasingly looking like long-term operational labels

In the past, many teams would treat equipment as technical information, but now it is more and more like user quality labels.

Because behind the equipment, it has begun to reflect:

lspending power

lUsage habits

lLong-term active tendencies

lUser stability

especially inIn long-term private domain scenarios such as WhatsApp, Telegram, and LINE, this difference will become increasingly obvious.

Why user operations will rely more and more on combined tags in the future

In the past, many teams only looked at:

larea

lgender

lage

Now more and more teams are starting to look at:

lActivity

lDevice type

lTraces of long-term use

lData completeness

lplatform behavior

Because it has become increasingly difficult to judge the true value of users with a single label.

The real difference between Android and Apple users is not just the device

Many times, the real difference is not the phone itself, but the long-term behavioral patterns behind it.

For example:

It is easier for Apple users to form long-term stable private domains;

Android pan-traffic is more prone to short-term fluctuations;

High customer single projects rely more on long-term stable users;

Low-quality ubiquitous traffic is more likely to be concentrated in low-threshold data.

Therefore, more and more teams now no longer only pursue data volume, but begin to pay attention to user structure.

User operations are starting fromThe "traffic era" enters the "filtering era"

In the past, people paid more attention to:

How to get more traffic.

Now more and more attention is paid to:

Which users are worthy of long-term operation?

Because what is really important in the future is not how many numbers you have, but how many long-term high-quality users you have.

Device tags may seem like just a small dimension, but they are slowly becoming a key reference in user filtering.

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