In North American launch, whether filtering iPhone user data has more conversion

Launching in the North American market, whether it is cross-border e-commerce,Whether it is APP promotion or subscription products, device type has always been an important variable that has been repeatedly verified. Among them, iPhone user data is often considered to be a high-value group, but many people will have doubts in actual operations: Is it really easier to convert iOS users by screening them, or is it just a superficial understanding.

To answer this question, the key is to understand the user structure behind the device, and how to combine device dimensions with screen numbers and user filtering.

1. Basic situation of user equipment distribution in North America

North American market, especially the United States and Canada,iOS devices account for a relatively high proportion, especially among middle and high-end consumer groups. In contrast, Android users have a wider coverage and are distributed at different income levels.

This forms an obvious feature:Although the overall number of iPhone users does not necessarily have an absolute advantage, their performance is more outstanding in terms of per capita consumption power and payment habits. This is why many delivery teams give priority to iOS users when filtering data.

However, it should be noted that equipment is only an external feature. It reflects the user structure and does not directly equal high conversion.

two,Why iPhone users are more likely to convert

From the actual data,iOS users have advantages in multiple dimensions.

The first is consumption capacity,iPhone users are generally more willing to pay for products or services, whether they are e-commerce goods or virtual products. Secondly, payment habits are more mature and people are more receptive to online transactions. Furthermore, they have higher requirements for product experience, so it is easier to make decisions when encountering products that match their needs.

existIn WhatsApp marketing scenarios, such users are often more likely to interact and enter the transaction process, which is also the core value of screening iPhone users.

3. How to identify in dataiPhone users

A practical problem is that inDuring the WhatsApp screen or number detection process, device type information will not be returned directly. Therefore, the identification of iPhone users relies more on indirect methods.

You can start from several angles, such as data sources. If some channels themselves areiOS users are mainly users, so the data obtained is naturally biased towards iPhone users. In addition, user behavior data, such as conversion history, interaction habits, etc., can also be combined to make reverse judgments.

In some self-owned traffic scenarios, the device type can be directly identified through device environment collection, but in pure number filtering scenarios, inference usually needs to be made through a data model.

Four,How iOS user filtering is used in WhatsApp marketing

In actual applications, device filtering does not exist independently, but is used in combination with other filtering conditions.

You can pass firstWhatsApp Screener completes basic screening, such as activation detection, active user screening, and filters out invalid data. Then combined with the device dimension, the users are further stratified, for example, possible iOS users are treated separately as high-value groups.

When launching, you can use products with higher unit price per customer or a more direct conversion strategy for this type of users, thereby improving the overallROI.

In this process, basic steps such as number detection and active screening can be completed through Digital Planet to make the data usable, and then the device dimension can be added for optimization.

5. Common misunderstandings: equipment≠Absolutely high value

AlthoughiPhone users convert better overall, but that doesn’t mean all iOS users are high-value users.

If you ignore user attributes and only look at devices, screening bias can easily occur. For example, some low activity or no demandiOS users still won’t convert. Similarly, if some Android users have clear needs, it may be easier to make a deal.

Therefore, device filtering can only be used as an auxiliary dimension and cannot replace activity filtering and user attribute filtering.

6. How to integrate the equipment dimension into the complete screening system

A more reasonable approach is to use equipment type as a part of the screening system instead of using it alone.

You can complete basic screening first, includingWhatsApp activation detection, active user screening, invalid number filtering, and then multi-dimensional screening combined with device inference, user tags, and behavioral data. Through this combination, the accessibility and conversion potential of data can be guaranteed at the same time.

When the device dimension is combined with activity and user attributes, the filtered data will be more accurate and more suitable for efficient delivery in the North American market.

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