Precise iOS invalid number filtering solution to quickly clean up low-quality user data to improve delivery results

Many people who do overseas marketing will have an illusion:iOS users generally have higher quality, so as long as the data has an iOS tag, it can basically be used directly.

But after actually running for a while, you will find a problem.There is also a large amount of data in the iOS user pool that "looks high-quality but is actually unavailable". These data will not directly report errors, but will continue to reduce delivery efficiency.

The meaning of iOS invalid number filtering is to clear out this "invisible noise".

A typical misunderstanding of iOS data

When many teams do user screening, they williOS treats it as a high-quality label, but ignores a practical problem:

lThe device isiOS ≠ user accessible

lNew system version ≠ User active

lHigh quality in countries and regions ≠ The data is real and usable

What really affects the delivery results is never the device itself, but the match between the number and the behavior.

invalidWhat iOS data usually looks like

In real data, it doesn't workiOS users are usually divided into several categories:

lThe device information exists, but the number is down

lThe registration platform is incomplete and cannot be reached

lRepeated imports cause data pollution

lLow active or even silent users who have not used it for a long time

lVirtual or unusual source data mingling

These data will not expose problems immediately, but will have a concentrated impact in the later stages of launch.ROI.

WhyiOS users are more likely to be “misjudged”

The reason why iOS user data is easily overestimated is because it is visually "cleaner":

lEquipment models have strong uniformity

lUsers’ ability to pay is relatively high

lThe average active period is longer

But the problem is, these are"Probability advantage" is not "absolutely effective".

Without filtering, the system can easily convert low-qualityiOS data misidentifies high-value groups.

The real impact of invalid numbers on delivery

whenAfter iOS invalid data enters the delivery system, several typical problems will occur:

lAD Learning Model Offset

lClick costs rise abnormally

lBroken conversion path

lRemarketing cannot close the loop

lPrivate domain import failure rate increases

These problems will not explode in a concentrated manner, but will slowly erode overall efficiency.

The key to filtering is not"Delete", but "layer"

matureThe iOS invalid number filtering solution is not to simply delete data, but to divide the structure:

lEffectively accessibleiOS users

lSuspected to be valid but requires user verification

lClearly invalid or unreachable to users

The advantage of this is that the data will not be wasted, but will enter different operational paths.

Why iOS data must be pre-processed

Many teams are used to leaving problems to be solved after launch, butThe iOS data is just the opposite:

The sooner it is processed, the lower the loss.

becauseiOS users often participate in high-cost advertising. Once they enter the wrong group of people, the waste will be amplified.

digital planet inHow to apply invalid number filtering in iOS

In actual data processing processes, Digital Planet is usually used asUse the iOS data cleaning portal.

When the user imports theAfter the iOS device information and mobile phone number data are collected, the system will first perform infrastructure verification, and then perform the first round of filtering on the data based on the number status recognition capability.

Three types of structure results will then be output:

lready for deliveryiOS valid users

lPotential users who require secondary verification

lClearly invalid or unreachable to users

More importantly, after this layer of filtering, the data will not stay in"Device Dimension" and will continue to overlay behavioral labels, such as:

lWhether to activateWhatsApp

lexistsTelegram account

lRegister or notFacebook or Twitter

lDo you have online consumption behavior?

soiOS tags are no longer the only standard, but just a basic dimension.

What iOS filtering really improves is the system’s ability to judge

Many people think that filtering just reduces the amount of data, but the real change is:

System starts"Closer to real user structure".

When invalid data is eliminated in advance:

lAdvertising system learning is more stable

lUser portraits are more accurate

lConversion path is clearer

lLess cost fluctuations

This is what filtering really means.

iOS is not a premium label, but a high-density data area

iOS users are not naturally high-quality, but a "high-density user area".

High density means:

lMany high-quality users

lThere are also many noisy users

The essence of iOS invalid number filtering is to accurately divert traffic in high-density areas.

When the diversion is completed, delivery truly begins to become controllable.

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