Zalo’s precise user positioning, what data should Zalo look at for customer screening and potential user stratification?

Zalo's precise user positioning means that you can automatically know the customer's age, income, occupation and purchase intention by simply throwing in their mobile phone number.

A more practical approach is to first add the existing Vietnamese number toZalo account status is screened out, and then combined with the region, gender, product label, customer source and other information that you originally have to continue grouping. Account filtering solves "Is there Zalo?" and user positioning solves "Which category does this data belong to?"

Accurate user positioning, the first step is to clarify who you are looking for

many people doWhen Zalo customer screening, I want to add a lot of conditions from the beginning.

In fact, a simpler way is to first determine a few core conditions.

For example, if you want to make certain products in Vietnam, you can first look at:

ltarget area;

lAlready has a customer gender tag;

lproduct classification;

lCustomer source;

lHas it been activated?Zalo.

The clearer these conditions are, the easier subsequent screening will be.

If you don’t even determine who the target users are, you just screen out a large number ofZalo has opened a number, but the final result is still just a list of accounts.

Zalo account status solves the communication portal

A Vietnamese mobile phone number has been openedZalo, indicating that this number has the corresponding account base.

This is important for the follow-upIt is important to organize Zalo customer data, because a normal mobile phone number does not necessarily mean that you are registered with Zalo.

Digital Planet can process existing Vietnamese numbersZalo related batch screening, sort out the data with Zalo account opening status first.

For example, there used to beThere are 100,000 Vietnamese mobile phone numbers. You can first sort out the numbers and then filter out the data among which Zalo has been activated. In this way, when you need to classify Zalo users later, you don't have to start over from all numbers.

After the account channel is determined, overlay the existing customer labels on it.

Zalo's precise user positioning really starts to become practical, often after account screening is completed.

If there are some tags in the original customer information, you can continue to work withZalo status combined.

for example:

Women Tags + Ho Chi Minh City +Zalo has been activated

Male Tags + Hanoi +Zalo has been activated

A certain product label＋Zalo has been activated

Historical customers +Zalo has been activated

The data obtained in this way are better than simply"Zalo registered users" are easier to continue classifying.

It should be noted here that information such as gender, region, product, etc. should come from existing data.Zalo account detection itself does not mean automatically identifying these customer attributes.

The more conditions, it does not mean that the user will be more accurate.

It is easy to make a misunderstanding in precise screening, which is to continuously add conditions.

Region, gender, product, account status, age, occupation...In the end, less and less data was filtered out, which seemed very accurate. However, if some of the labels themselves did not have reliable sources, the results could easily be distorted.

soZalo’s precise user positioning is more suitable for capturing the truly useful layers:

Number basics

First make sure there are no obvious problems with the Vietnamese mobile phone number format and data itself.

Zalo account status

Confirm which numbers haveZalo account basics.

Already have a customer tag

Continue segmentation based on original information such as region, gender, product category, etc.

The three layers can be matched up and can already meet the data organization needs of many Vietnamese customers.

Region tags are very useful in Vietnamese user filtering

The Vietnamese market itself has obvious regional differences.

If the original data already has province, city or region labels, you canKeep it after Zalo filtering.

For example, data from different regions such as Ho Chi Minh City, Hanoi, and Da Nang can be organized separately and do not need to be mixed together.Zalo user files.

This grouping will be more convenient for subsequent regional promotions, local activities, or customer management in different cities.

Digital Planet is responsible forZalo account status is screened clearly, and the original region information continues to follow the data. After the two parts are combined, the user classification will be more detailed.

If you already have a gender tag, you can also use it withUse together with Zalo status

If the customer information already has reliable male and female labels, you can continue to interact with them.Zalo account status combination.

For example:

Female users +Zalo has been activated;

Male users +Zalo has been activated;

Gender unknown +Zalo is activated.

This is safer than guessing a man or woman based on their profile picture or nickname.

If the original data does not have gender information, keep"Unknown" is totally fine too. There is no need to forcefully judge the gender of each account in order to make the label look complete.

Different data can be split into multiple small lists as needed

Zalo’s precise user positioning does not necessarily have to be a “large and comprehensive” list.

Sometimes breaking it into several simple groups is more convenient to use.

for example:

Vietnamese womenZalo user;

Ho Chi Minh CityZalo user;

Related to a productZalo user;

Historical customersZalo user;

Recently rearrangedZalo number.

Each group only solves a clear problem. Which type of data is needed later, just use the corresponding grouping directly.

This is also the value of batch filtering: instead of making labels more and more complex, it makes the originally mixed data gradually become clearer.

The core of Zalo's precise user positioning is "account status + existing tags"

Digital Planet can do itZalo number related batch screening, sorting out the account activation status of existing Vietnamese mobile phone numbers. Tags such as region, gender, product category, etc. that already exist in the original data can continue to be combined with the filtering results.

Made this wayZalo user data will be easier to classify and use than a single list of activated numbers.

Accurate user positioning does not need to pursue dozens of conditions. First, the number,Zalo account status corresponds well with existing customer tags, and many Vietnamese users’ screening needs can be handled relatively clearly.

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