How to select tags for Zalo loan customer screening: actual usage order of equipment type and age and gender

DoDuring the Zalo loan project, many teams will encounter a problem in the customer screening stage: there are many tags, but they don’t know which one to use first. In particular, the two labels "device type" and "age and gender" are often placed on the same level for judgment, resulting in a decrease in screening efficiency.

Both dimensions are valid in their own right, but serve completely different purposes. If the order is used in the wrong order, it will not only affect the screening results, but also directly affect the subsequent conversion efficiency.

Device type label, which solves"Basic Judgment" Issue

device type (iOS or Android) is essentially a "pre-judgment label", which is usually used for the first level of screening rather than the final decision.

In loan scenarios, equipment types often indirectly reflect:

l User’s spending power range

l Device usage habits

l User structure distribution in certain regions

For example, in some markets,iOS users as a whole are more concentrated in the mid-to-high consumption range, while Android user base is larger and more widely distributed.

The purpose of this type of label is not to determine whether a transaction is completed, but to quickly"Rough screening of the crowd".

Age and gender labels, closer"Conversion Judgment"

Age and gender are more biased than device type"Behavioral labels" directly affect communication and conversion.

Common patterns include:

l Users aged 25-35 are more receptive to loan products and respond faster to communication

l Users aged 30-45 pay more attention to quota and stability

l There are significant differences between genders in loan purposes and decision-making pace

These factors will directly affect:

l Are you willing to continue communicating?

l Are you willing to submit information?

l Whether to enter the transaction stage

Therefore, age and gender are more suitable to be used in"Fine screening" and "stratified operation" rather than the first step of filtration.

Why do many teams use the order in reverse?

In actual operation, we often see this process:

Filter by age first → Filter by gender → Look at the device last

The problem with this approach is that it enters segmentation at the beginning but does not do basic filtering, resulting in:

l Invalid numbers get mixed into the screening process

l Low-value users are included too early

l Subsequent communication costs are magnified

The result is that the screening is very"Fine", but very inefficient.

A more practical set of filtering sequences

existIn the Zalo loan project, a more stable screening process can be:

Step 1: Determine whether the number is available

First filter out empty numbers, unactivated accounts, and abnormal status.

Step 2: Make basic stratification by device type

Quickly differentiate between different consumption abilities and population structures

Step 3: Precise screening based on age and gender

Target more suitable groups of people based on business needs

Step 4: Determine reach priority based on active status

Focus resources on people who are more likely to respond

The core logic of this sequence is: first ensure"Effectiveness", then judge "value".

At different budget stages, screening strategies also need to be adjusted.

Depending on the budget, the depth of screening should also be different.

When budget is limited

Prioritize the first two steps (availability+ equipment), control waste first

When the budget is medium

Add age and gender stratification to improve conversion rate

When the budget is sufficient

You can add more tags, such as activity and behavioral data, to perform more segmented operations.

If you do full-dimensional screening from the beginning, it is easy to fall into"Complex but inefficient" state.

Common screening mistakes

In loan projects, these errors are relatively common:

l Only look at age, not number status

l Only look at gender, not device structure

l Use all tags at once, no priority

l Ignore active status, resulting in low reach rate

These questions are essentially"Wrong order", not a labeling issue.

How to turn screening into a fixed process instead of a temporary action

Many team screening numbers are"Just do it when you think of it", but a more effective way is to turn it into a fixed process.

For example:

l Before data is imported, a unified test is performed

l Before each round of marketing, another round of active users is screened

l Data from different channels are processed uniformly using the same set of standards.

This makes every contact more consistent, rather than relying on luck.

Use tools to sift through the basic data first, and then talk about refinement.

If the amount of data increases, manual judgment is basically unfeasible. At this time, tools are needed to unify the screening criteria.

In actual operation, you can first use Digital Planet to do screen number detection, and thenRun through the basic status of Zalo numbers, first filter out invalid and abnormal data, and then layer the device type and age and gender labels. This can not only control early waste, but also make subsequent conversions more focused. Digital Planet supports free trial screening test.

The core of screening is not the number of tags, but the order of the

Device type and age and gender are not a matter of choice, but a matter of order of use. First use the device to make basic judgments, then use age and gender for precise screening, and finally combine the activity status to determine the reach rhythm, so that the entire screening link will be more stable.

When the order is straightened out, you will find that the conversion efficiency of the same batch of data will be completely different.

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