What is the difference between Android and iOS model detection? Detailed explanation of the joint screening method of equipment system and model

In the process of batch screening social accounts, filtering invalid users, and establishing a high-quality private domain pool,"Equipment testing" is a very important part. In all device detection logic, the difference between Android and iOS models is an important basis that operators must understand. The system ecology, model structure, and hardware distribution of the two are completely different, and their impact on account quality, activity, and risk control risks are also different.

In order to achieve truly accurate screening, it is necessary to combine Android andThe model detection of iOS is merged to establish a joint screening model of "system + model". This article will explain the core differences and the best screening methods between the two from a practical operational perspective.

Android andFundamental differences in iOS model detection

1. Differences in the number of models lead to different screening logic

lThe number of iOS models is small and the structure is fixed.

Apple only releases a small number of models each year to facilitate the establishment of a stable model library and activity model.

lThere are thousands of Android models and their distribution is extremely scattered.

Each brand, each region, and each supply chain have different models, and the model complexity is much higher thaniOS.

therefore,iOS is more suitable for "accurate identification", and Android is more suitable for "batch filtering".

2. System ecological differences directly affect activity judgment

liOS ecosystem is closed, device information is simple and controllable

The iPhone model is clear and can be checked, and system version updates are relatively concentrated, so the activity judgment of iOS devices is relatively accurate.

lThe Android ecosystem is open, but the system version is confusing and inconsistent

Many Android devices stay on the old system and are not updated or upgraded, making it more difficult to judge activity and making it easier for them to be used for registration or batch operations.

3. Risk control risks are completely different

liOS devices have strong consistency and are easier to identify real users

Same modelThe iPhone is highly consistent in system performance.

lAndroid model andROMs vary greatly and risk signals are more complex

unofficialROM, customized system, and modified system may cause the account to trigger risk control.

The core of joint screening: system+ model + behavior three linkage

If you want to compare Android andFor truly effective iOS screening, it is impossible to rely solely on "model identification" and must adopt a "three-linkage strategy."

The first level: system type identification (Android/iOS)

This is the basis, and the system type directly determines the subsequent screening strategy.

liOS → Do "fine screening": identify model + system version

lAndroid → Do a "rough screening": first screen the brand, then screen the model, and then check the system

Second level: model identification (brand+ model + year)

iOS decision logic:

liPhone 13/14/15 → High value

liPhone 11/12 → Mid-value

liPhone 6/7/8 → Risk number, low activity device

Android decision logic:

lSamsung S series, Xiaomi digital series → high value

lOPPO/Vivo/Huawei → medium value

lLow-priced machine brands (such asTecno, Itel, Coolpad) → High risk

The third layer: Behavior tag identification (active/inactive/suspicious)

Different models of equipment should be combined with behavioral judgments:

lwhetherLaunch within 30 days

lIs there address book synchronization?

lWhether to bind multiple platforms

lAre there traces of batch operations?

lIs the system version abnormal (such asAndroid 6 recurring)

Only by combining the system, model, and behavior can we truly screen out meaningful accounts.

How to actually use Android withiOS model detection?

With the help of Digital Planet’s equipment detection system, you can achieve:

1. Import mobile phone numbers in batches

Process tens of thousands of data at one time.

2. Automatically identify system type

Accurately distinguishAndroid and iOS, no interference from disguise.

3. Return to brand, model, and system version

Structured recognition includes:

iPhone 13 Pro Max/iOS 17

Samsung S21/Android 13

4. Automatic labeling

Such as: high activity devices, low activity devices, high risk devices (Android), abnormal devices, etc.

5. Output the filtered list

Enterprises can directly transfer the screenedThe "High Value Device List" is used for placement or registration on WhatsApp, Telegram, LINE and other platforms.

Why make Android+ iOS joint screening?

Because the filtering of a single system cannot support real business scenarios.

lregisterWhen it comes to WhatsApp accounts, Android risks are far greater than iOS

lDoWhen sending group messages on Telegram, iOS users have a higher open rate

lWhen making outbound calls, old Android devices may cause unstable connection rates.

lWhen running multiple environments, some Android devicesROM will trigger risk control

lWhen doing behavioral portraits,iOS data is cleaner and identification is more accurate

Joint screening allows companies to truly:

lReduce the risk of account ban

lImprove reach efficiency

lFilter out non-transformable equipment

lEnhance account pool quality

lReduce operational waste

In the era of batch operating accounts and building global private domain pools, device model is no longer a dispensable parameter, but an important basis for determining the authenticity, stability and value of the account. Android andThe model detection logic of iOS is different, but only by integrating the two into a unified screening system can your marketing list be truly valuable.

If you are conducting batch operation of accounts or preparing to build a private domain pool, you can use Digital Planet to complete all device model identification, system version detection and risk filtering, laying the most solid foundation for subsequent delivery and reach.

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