The most practical method for AI-assisted overseas customer acquisition is to sort out active user tags first and then generate content

existAI can already generate phrases, copywriting, pictures and even videos in batches. When many teams are trying to acquire overseas customers, their first reaction is to directly let AI generate content. However, after running for a period of time, you will find a problem. The content production efficiency has indeed improved, but the conversion has not improved simultaneously, and sometimes it is even not as stable as before. The reason is usually not the AI's ability, but the wrong order. A more practical approach is to sort out active user labels first, and then let AI generate content based on people, instead of generating content first and then finding people.

This step sequence change seems to be just a process adjustment, but it actually affects the entire customer acquisition link. Because no matter how much content there is, if the audience is not right, the essence is still ineffective reaching. especially doIn scenarios such as WhatsApp’s precise reach, Telegram’s traffic, and Instagram’s private domain operations, the importance of user tags has been significantly higher than the quantity of content.

WhyWhen using AI to acquire overseas customers, it is more important to sort out labels first.

The advantage of AI lies in its ability to generate, but the prerequisite for generation is that the input is accurate enough. If the input is a pan-population, the content generated by AI can only be pan-population content. On the other hand, if active user labels are sorted out first, such as filtering out highly active users, distinguishing users over 25 years old from users over 35 years old, and distinguishing male users from female users, then the content generated by AI will be closer to the crowd itself.

This is why many teams initially felt thatThe AI ​​is easy to use, but the effects behind it are unstable. The problem is not with the AI, but with the input layer. If the labels are not clear, it will be meaningless no matter how precise the content is.

Judging from the actual process, label sorting has changed from an auxiliary action to a prerequisite action.

What specific problems can active user tags solve?

The active user label most directly solves the problem of user status judgment. Whether a user has recent usage behavior will directly affect his response probability to information. If high-active users and low-active users are mixed together, no matter how accurate the content is, the overall performance will be dragged down.

In addition, active tags can help prioritize reach. for example3-day active users are more suitable for instant conversion, while 7-day active users are more suitable for continuous contact. If no distinction is made, these two types of users will be covered by the same set of content, and efficiency will naturally decrease.

In actual applications, active user screening is often done together with user data cleaning and abnormal account filtering, so that the resulting user pool will be cleaner and more suitable for entry.AI content generation link.

What tags should be sorted out before AI generates content?

From a practical perspective, at least three types of labels should be organized.

The first category is basic status labels, such as number activation status and whether the account is available. This step is the lowest level of data cleaning.

The second category is active tags, including3-day active, 7-day active, etc. are used to determine the user's current usage status.

The third category is crowd tags, such as gender filtering, age stratification, and device identification. These tags determine the direction of content.

If it is more detailed, abnormal account identification and risk account filtering can also be added to further improve data quality.

After these labels are organized, letAI generates words or content, and the matching degree will be significantly higher.

Why is it more practical to filter tags first and then generate content?

The core reason is to reduce invalid tests.

If you directly generate content and then match it with the crowd, you often need to continue trial and error, because you don’t know whether the problem lies in the content or the crowd. And if the user tags are distinguished first and then the content is generated, the test dimensions will be clearer.

In addition, labeling is done first, which is more suitable for batch processes. Because once a label is determined, it can be reused, and the content can continue to change based on the label, so the overall efficiency is higher.

This method makes it easier to form a stable process when doing batch customer acquisition and automated contact.

Which overseas customer acquisition scenarios is this process suitable for?

existIn the customer acquisition scenario of WhatsApp, first screening highly active users and then generating corresponding phrases can significantly increase the response rate.

existWhen attracting traffic to Telegram, active users and real users are screened first, and then AI is used to generate guidance content. The quality of the group will be more stable.

existIn Instagram or Facebook private domain operations, gender screening and age stratification are performed first, and then different styles of content are generated to reduce invalid exposure.

What these scenarios have in common is that user matching is more important than the content itself.

Digital Planet’s place in the process

In actual operations, many teams will connect Digital Planet at the forefront to organize user tags. Through Digital Planet, we first complete number detection, active user screening, gender and age stratification, and abnormal account filtering, and then submit the compiled user tag data toAI content generation is much more stable than using raw data directly. Digital Planet supports free trial screen number detection, and is more suitable for batch screen number and multi-dimensional user tag combinations. If this step is handled cleanly, the subsequent AI content will have a foundation.

WhyThe ultimate competition for AI customer acquisition is data rather than content

From the results,The gap in AI's ability to generate content will not be too big, but the gap in data screening capabilities will become increasingly obvious. Because content can be copied, but user structure is difficult to copy.

If two sets of content are of similar quality, but one is generated based on high-active user screening and the other is generated based on mixed users, the end result will usually be far different.

That's why we do it nowWhen acquiring overseas customers, AI increasingly emphasizes user screening first and then content generation.

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