Where do you screen for female LINE users? Existing customer data can also be regrouped like this

DoWhen screening LINE customers, if the business itself pays more attention to female users, many people’s first reaction is to directly judge the gender from the number. In fact, the safer way is not to guess, but to first check whether there is a gender field in the customer information that the company has accumulated.

Official website registration, member information, event registration, historyIn CRM, if the user has actively filled in their gender, female customers can be sorted out separately, and then further screened for which numbers have LINE account base. The source of the female LINE users obtained in this way is clearer, and it will be easier to further segment them based on age, product interests and recent interactions.

First, find the female tag from the existing customer information.

If the customer table already has fields such as mobile phone number, country, gender, age, source, and products of interest,Screening female LINE users is actually not complicated. The first step is to single out female customers, then check the mobile phone number format and duplicate data, and finally filter the LINE account status.

For example, there was originally50,000 pieces of customer data, 23,000 of which were filled in by users themselves as female. First sort out these 23,000 numbers, and then filter out those with LINE account base. What we finally get is a female LINE customer pool that is more suitable for subsequent operations.

If there is no gender field in the original data, don't guess through avatars, nicknames or names just because you want to filter for women. Those who cannot be confirmed can remain as unknown gender.

If the avatar looks like a female, it cannot be directly regarded as a female user.

The LINE account avatar is not necessarily a real person photo. Some people will use pets, animations, scenery, celebrity pictures or brand logos. Nicknames may also be English names, online names, company names, or even completely irregular characters.

Therefore, it is easy to write wrong labels into the customer database when judging gender based on avatar or nickname alone.

Once the label is wrong, the subsequent marketing content will also be wrong.

For example, putting a user of uncertain gender directly into the promotion list of women's skin care products may not only fail to improve accuracy, but also increase irrelevant contacts.

thereforeIt is best to screen LINE female users based on existing real customer information, rather than relying on appearance guessing.

digital planet onLINE account screening is more appropriate

If the company has obtained customer numbers through normal business channels such as official website, member registration, historical orders, advertising forms, etc., and there is already a gender field in it, it can first extract the female users, then unify the international number format and process duplicate data.

After completing the basic sorting, you can filter these numbers through Digital Planet to see if they haveLINE account basis, and then write the results back to the original customer information.

For example, it can be organized into:

Japan＋Female＋LINE has been activated + follow product A

Taiwan＋Female＋LINE has been activated + historical customers

Thailand＋Female＋LINE has been activated + registration for recent events

Japan＋Female＋LINE status to be confirmed + official website inquiry

Digital Planet is responsible for numbers andAccording to the status of the LINE platform, female labels still come from customer information that companies already have. By combining the two dimensions in this way, the data logic will be clearer.

After you screen out female users, don’t put them all into one marketing pool immediately.

Screening out women is not enough.

Both are womenAmong LINE users, some people just asked about the price yesterday, some people only registered as members two years ago, and some people only participated in one event.

If they all use the same set of content, the value of so-called female screening will still be limited.

A more practical way is to continue to overlay several fields: age group, products of interest, customer source, and most recent business interaction.

For example:

Female +LINE has been opened + 25-34 years old + recent inquiries

Female +LINE has been activated + 35-44 years old + historical purchases

Female +LINE has been activated + age unknown + event registration

Female +LINE has been activated + no interaction for a long time

This way sales and operations know which ones are worth dealing with first and which ones are suitable for long-term maintenance.

Age tags can continue to be subdivided, but don’t break them down too much.

If the age or age group is also saved in the original customer information, you canLINE users continue to be grouped based on each other.

for example18-24 years old, 25-34 years old, 35-44 years old, 45 years old and above.

But how to divide the age depends on the actual product.

If you sell clothing, beauty, and consumer goods, different age groups may indeed correspond to different content.

If the product itself has little to do with age, there is no need to break down a dozen age groups just to look accurate.

More labels are not necessarily better. What is really useful is whether it will change the way of subsequent marketing after distribution.

Product interest is more important than purely female labels

Assuming that it has been screened outThere are 10,000 female LINE users, some of whom are concerned about skin care, some are concerned about clothing, and some are asking about household products.

If we lump them all together just because they are all women, the scope is still too large.

A more suitable combination is:

Female +LINE has been activated + skin care products

Female +LINE has been activated + clothing products

Female +LINE has been activated + maternal and infant products

Female +LINE has been opened + recent inquiries

After grouping in this way, the content and products can be truly targeted.

Therefore, women are a demographic label, and product interest is a label closer to business needs.

Female users who have consulted recently should be placed first

Customer grouping ultimately involves service sales.

Even if female users have been separated, the priority should still be to see if there have been any real actions recently.

For example, a customer just inquired about a product through the official website yesterday.LINE has also been opened, so it should be prioritized.

Another female customer thoughIf your LINE account is normal but has not had any business interaction for a year, it can be placed in the maintenance pool.

Therefore, a piece of really useful data should also include:

female tags;

LINE status;

Customer source;

Pay attention to products;

Last interaction time.

When these fields are put together, the sales order will be clear.

Please do not delete customers whose gender is unknown directly.

There must be people in the actual customer database whose gender is not filled in.

This data does not mean it is without value.

If a user of unknown gender just takes the initiative to consult today, he may still be a high-quality customer.

On the other hand, if a piece of data with a very complete female label has no interaction for two or three years, it may not be worthy of priority.

Therefore, unknown gender can be saved separately. Do not delete it directly because it cannot be classified.

In the future, the user can re-register, update membership information, or add information in the normal course of business, and then update the gender field.

Which businesses are more suitable to doLINE female user screening

LINE female screening is more suitable for businesses with obvious user differences, such as clothing, beauty, care, mother and baby, interest consumption, some education and activity products.

After this type of business separates female users, it is indeed possible to change content recommendations and product mix.

But if it is business such as enterprise software, industrial equipment, logistics services, etc., gender is usually not the first priority. Industry, position, and procurement needs are more important.

Therefore, whether to do female screening should not depend on whether there are functions, but whether the business actions will change after grouping.

Don’t leave only women andLINE two fields

If after the filtering is completed, only the"Female + LINE has been activated", but sales still don't know how to follow up.

More practical customer information can retain country, mobile phone number, gender,LINE status, age group, customer source, products of interest, latest interaction, and current sales status.

For example, a piece of data can be: Japanese female customers,25-34 years old, LINE has been opened, the source is the official website for price inquiry, product A was inquired 3 days ago, and sales follow-up is pending.

The other one is: Taiwanese female customers,LINE has been opened, sourced from historical activities, no interaction for 8 months, normal maintenance.

Both of these belong toLINE female users, but their priorities are completely different.

The truly practical way to screen LINE female users is not to guess who is a female from a bunch of numbers, but to first use the real gender information that the company already has, then supplement the LINE account status through the digital planet, and finally regroup the users based on age, product interests and recent business records. Women are just a layer of customer classification. LINE is a communication channel. What really determines whether it is worth following up first is whether the user has real needs.

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