There is a lot of cross-border traffic flow data, how to filter out low-quality customers who will not close

When doing cross-border traffic attraction, many teams will go through a stage: more and more data, but transactions do not increase simultaneously. It seems that there are enough leads in the background, and there are even new ones every day, but once you get to the follow-up stage, you will find that a large number of users cannot be reached, barely respond, or do not meet the conversion conditions at all.

If this kind of problem is not dealt with, the team can easily fall intoThe cycle of “keep adding more but no results”. The key is not to expand channels, but to filter out low-quality customers first, so that the back end can only face "people who are likely to make deals."

More data does not mean more customers. The problem lies in the pre-screening process.

The data sources for cross-border traffic are usually complex, including advertising forms, social media traffic, third-party data, historical customer accumulation, etc. The more sources there are, the more confusing the structure becomes. Data from different countries, different platforms, and different qualities are mixed together, making it difficult to use directly.

If there is no screening mechanism, all leads will enter the same follow-up link. The result is: customer service time is diluted, sales energy is dispersed, and the follow-up pace of truly high-value customers is slowed down.

So the first step is not to optimize conversions, but to convert the data"Tear it down".

What are the common distinguishing characteristics of low-quality customers?

In actual operations, low-quality customers often have some common characteristics, and these characteristics can be used as the first level of screening criteria:

l The contact information is unavailable, such as wrong number or unopened account.

l Long-term inactivity, almost no use

l There are obvious abnormalities in the data, such as confusing nicknames and missing information.

l Total mismatch with target market or product

Even if these customers enter the follow-up process, it is difficult to generate actual value, and continuing to invest will only lower the overall efficiency.

Screen first for invalid results, then screen for lower interest, and the order cannot be reversed.

Many teams will start directly fromStart with "intent judgment", such as seeing if the user is interested, but if there is a problem with the contact information itself, this step is meaningless.

A more reasonable sequence should be two steps:

Step 1: Filter out invalid data

Exclude empty accounts, unopened accounts, and obviously abnormal data first. What this step solves is"Can we communicate?"

Step 2: Screen out low-intent customers

From the available data, we can then judge whether it is worthy of priority follow-up based on activity, information, and behavior. This step solves"Is it worth communicating?"

Once the order is reversed, judgment costs will be wasted on wrong data.

Data from different platforms have different screening priorities.

Cross-border traffic diversion usually involves multiple platforms, and the screening logic of each platform cannot be exactly the same and needs to be focused.

WhatsApp data

It pays more attention to whether it is activated and active status. If the user has not activated it or has not used it for a long time, there is basically no conversion condition.

Telegram data

The focus is on long-term usage traces, not short-term online use. Many accounts come online occasionally but do not participate in interactions. Such users have limited value.

Line and Zalo data

It is more suitable to judge based on data completeness and activity. Local users are usually easier to convert if their data is complete and their usage is stable.

It is more effective to process data from different platforms separately than to filter them uniformly.

Before data enters the system, a screening test must be done

A key point that is easily overlooked is the availability of the contact details themselves.

If there are a large number of unavailable numbers or abnormal accounts in the data, the effect will be affected no matter how you layer it later. Therefore, it is recommended to complete a round of basic testing before importing the system.

In actual operation, Digital Planet can be used for screening number detection to screen out invalid numbers, unopened accounts and low-quality data in cross-border traffic data, and then enter the remaining data into the stratification and follow-up process. Digital Planet supports free trial screening test.

This ensures that the backend is facing a batch of“Reachable” users, not uncertain data.

Filtered data needs to be redistributed for use

After filtering is completed, the data should no longer be processed uniformly, but used stratified according to quality.

high quality customers

With availability, activity and matching, priority is assigned to sales for one-to-one follow-up.

medium quality customers

Nurture can be done through content, automated messages, or light touches to gradually increase intent.

low quality customers

Reduce the frequency of contact and exist as supplementary data without occupying main resources.

This allocation method can significantly improve overall operational efficiency.

Common misunderstanding: reluctant to delete data

Many teams have a mentality when screening: data is hard to come by and they are reluctant to delete it. But in fact, not deleting low-quality data is the most costly approach.

Low-quality customers will bring three kinds of consumption:

l Customer service time is occupied

l System resources are wasted

l High-value customers are delayed in following up

Screening is not about loss, but about focusing resources where they are more likely to produce results.

The goal of screening is not to reduce the number, but to become more accurate.

Finally, one thing needs to be made clear: filtering out low-quality customers is not to reduce the data, but to make the data more"Available".

when you putThe judgment of "whether the transaction can be completed" is advanced to the data stage, and you will find:

l Follow-up rhythm is clearer

l Conversion path is smoother

l Overall costs more controllable

The real difficulty in attracting cross-border traffic is not to obtain data, but to find people worthy of investment from the data. When the filtering order is correct, many back-end problems will disappear naturally.

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