WhatsApp gender and age detection, how to use these two tags more appropriately when stratifying customers?

The more WhatsApp users do, the list of only mobile phone numbers and countries is often not enough. Clothing, beauty, consumer goods, education, event promotion and other businesses often pay attention to age and gender, hoping to operate different users separately.

A WhatsApp number by itself cannot reliably tell you the user's true age and gender. If the company's own registration form, member information,CRM, historical orders or event registrations already contain age, age group and gender information proactively provided by users. Combining this with WhatsApp account status is a truly practical method of customer stratification.

First check whether the original customer information has age and gender.

ready to doBefore WhatsApp gender and age screening, the first step is not to directly detect the number, but to check the data in hand. For example, if a customer list already has mobile phone number, country, gender, age group, source, and products of interest, then the subsequent processing will be very simple. First unify the international number format, clean up duplicate data, and then fill in the WhatsApp activation status. Finally, you can form groups such as men + WhatsApp activated, women + WhatsApp activated, 25-34 years old + WhatsApp activated, etc.

If the original data only has mobile phone numbers and no age and gender fields, do not force each number to be male, female, or a certain age group. Missing information can be completely left as unknown, and will be updated after subsequent users add their own information.

Don’t guess your gender based on your avatar and nickname.

WhatsApp users may use photos of real people, landscapes, pets, company logos, cartoons, or no avatar at all. It is easy to make mistakes in determining gender based on avatars alone. Nicknames are equally unreliable. English names, abbreviations, company names, and online names are all common, and some names themselves cannot stably correspond to a certain gender.

If customers have actively selected their gender when registering on the official website, membership information or event registration, this field is more suitable for subsequent grouping. The company's ownIf the CRM has already recorded customer information through normal business processes, it can continue to be used. The key point is that the source of the label should be clear, and do not regard the guessed results as real customer information.

Age filtering is more suitable for existing age groups

The same logic applies to age. aloneA WhatsApp number cannot directly tell a person's real age. If the customer has filled in their own birthday, age, or age range, they can continue to use these existing fields.

In actual operation, there is no need to break down the age in particular details. Many business usesThe ranges of 18-24, 25-34, 35-44, and over 45 years old are sufficient. The specific classification should be determined according to the product audience.

If you are selling a product that young people are more concerned about, you can focus on the corresponding age group; if the product itself covers a wide range, there is no need to break down a dozen ranges for age screening. The purpose of age labels is to help reduce irrelevant touches, not to make the customer table look more complex.

digital planet takes chargeWhatsApp status, age and gender are from original customer information

After you have a batch of customer numbers obtained through normal business channels, you can first clean and deduplicate the numbers, and then filter them through Digital PlanetWhatsApp activation status. After completion, merge the test results with the original fields such as age, gender, source, and product interest.

For example, the original data is:

America＋Female＋25-34 years old + pay attention to product A

UK+Male+35-44 years old + historical customers

France + Female + Unknown Age + Official Website Inquiry

Add moreAfter that, the WhatsApp status can become:

America＋Female＋25-34 years old + WhatsApp has been activated + follow product A

UK+Male+35-44 years old + WhatsApp has been activated + historical customers

France＋Female＋Unknown age＋WhatsApp has been activated + recent inquiry

Digital Planet solves the problem of numbers andWhatsApp platform status, age and gender still come from customer data that companies already have. Combining the two types of information is the data that can truly be used for customer stratification.

After men and women are separated, we still need to continue to look at product demand.

The separation of male users and female users does not mean that the customers have been accurate. For example, among the same female users, some pay attention to beauty, some pay attention to clothing, and some have only participated in one event. If sales were tied solely by gender, there would still be a lot of irrelevant content.

A more practical combination should be gender + product interest +WhatsApp status. For example, women + WhatsApp has been activated + pay attention to skin care products, men + WhatsApp has been activated + pay attention to outdoor products. This grouping is easier to use directly than a simple list of men and women.

in the case ofIn B2B software, industrial equipment, logistics services and other businesses, gender may not be an important field at all, but industry, position, company and procurement needs are more valuable. Whether male and female screening is required depends on whether this label will change the subsequent marketing content.

Age and gender are best not alone in determining customer priority

oneA 25-year-old WhatsApp user is not necessarily more worthy of development than a 45-year-old user; female customers are not necessarily easier to make deals than male customers. What really determines sales priority is whether the customer has recent needs.

For example, aA 40-year-old customer just took the initiative to inquire about prices yesterday, and another 25-year-old customer left information two years ago and has never interacted with her again. Obviously, the former should be dealt with first.

Therefore, age and gender are more suitable for classifying content and people, rather than directly judging customer value. When actually arranging the sales order, you must continue to look at recent inquiries, replies, orders, customer service interactions and other business information.

Unknown gender and unknown age can both be left alone

It is impossible for everyone in the actual customer database to have complete information. Some users only fill in their mobile phone number, some fill in their age but no gender, and some fill in neither.

This situation does not require forced completion.

You can directly retain the status of male, female, unknown, as well as known age group and unknown age. In the future, users can resubmit information, update member information or supplement information during normal business communication, and then update the customer label.

Although there will be unknown items in the table this way, the data will be more realistic and more useful than filling in wrong labels from the beginning.

Which businesses are more suitable for age and gender grouping?

For businesses such as clothing, beauty, care, some consumer goods, education courses, and interest activities, age and gender may indeed affect content selection. At this time, grouping makes more sense. For example, different product portfolios are displayed for different age groups, and users of different genders see more relevant activity content.

But if you are selling a universal typeSaaS, cross-border logistics, industrial equipment, age and gender are usually not the first priority. The customer's industry, company size, position, and purchase time may be more important.

The method to judge whether a tag is worth retaining is simple: if the marketing method will change after grouping, it will be useful; if the same content is posted regardless of gender or age, the tag does not need to be placed too heavily.

It is enough to keep a few core fields in the customer table at the end

After the WhatsApp gender and age screening is completed, there is no need to make the form particularly complicated. Country, mobile phone number, WhatsApp status, gender, age group, customer source, products of interest, latest interaction, and current sales status are basically enough.

For example, a piece of data is American women,25-34 years old, WhatsApp has been activated, inquiry comes from the official website, and followed product A 3 days ago. This customer information is relatively complete. Although the other item also contains age and gender, it comes from ordinary activities two years ago and has no interaction for a long time, so the priority can be lowered.

The truly useful way to detect gender and age in WhatsApp is not to use a string of mobile phone numbers to guess who the user is, but to recombine the real age, gender information and WhatsApp account status that the company already has.Age and gender are responsible for customer grouping,WhatsApp is responsible for the communication channel, and recent demand and business behavior determine who sales will contact first.

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