How to classify LinkedIn user age detection results more clearly

Users on LinkedIn are at different career stages and pay different attention to content. People who have just entered the workplace may pay more attention to skills, tools and job search information; people with many years of work experience may pay more attention to industry resources, management efficiency and cooperation opportunities.

if allPutting LinkedIn numbers in the same group makes it easy to write content that is too broad. Digital Planet supports LinkedIn age filtering, which can detect user age information from opened accounts and then establish group classifications according to different age ranges.

Don’t break down the age range too much

FinishAfter detecting the age of LinkedIn users, it is not recommended to create a group for each age group. If the classification is too detailed, the number of files will be large, and it will be inconvenient to actually prepare the content.

A more practical way is to set age ranges according to career stages. For example, it can be divided into categories such as those who are new to the workplace, those in the career development stage, and those who are senior practitioners. Each group covers a larger age range, making it easy to view and arrange different content.

digital planet completedAfter LinkedIn age screening, the corresponding range can be selected and exported based on the actual detection results. When you need to adjust the age range, you can directly modify the filtering conditions without reorganizing the entire set of data.

Basic content suitable for those who are new to the workplace

youngerLinkedIn users may be in the process of applying for a job, internship, or starting a career. This group of people is usually more likely to pay attention to tool tutorials, work efficiency, resume optimization, job information and basic skills.

After filtering out the corresponding age groups through Digital Planet, you can prepare content that is easier to understand and reduce excessive industry jargon. For example, when introducing a certain tool, you can start from the specific functions and practical operations; when publishing course information, you can first explain what kind of people are suitable for learning.

The content does not need to be covered too deeply at once. Solving a clear problem first makes it easier to see the actual responses to different topics.

Career development period pays more attention to practical value

After entering the career development stage,LinkedIn users have usually accumulated a certain amount of experience, and their focus will gradually shift to professional capabilities, business efficiency, industry resources and career improvement.

digital planet completedAfter LinkedIn age detection, these users can be sorted separately, and then combined with previous consultation content to arrange articles, cases or product introductions.

If the position, industry or consulting direction is saved in the original data, you can continue to segment based on age screening. For example, within the same age range, people who focus on marketing tools and people who focus on team management can receive different content respectively.

Age is only the first level of classification. After adding real needs, the content will be closer to the problems that users are dealing with.

More complete information for senior groups

Those who are older and have longer work experienceLinkedIn users usually pay more attention to whether the solution is mature, the information is complete, and the long-term use value.

For this group of people, the content can reduce the introduction of basic concepts and focus on overall solutions, efficiency changes, cooperation processes and actual cases. The page structure can also be made clearer, allowing users to quickly find the parts they care about.

After using Digital Planet to screen out the corresponding age groups, you can individually test case-based content, industry reports or product comparisons, and then adjust the next round of arrangements based on clicks and responses.

Age results can be paired with other information

After the LinkedIn age screening is completed, it can also be combined with the account opening status, activity status and original consultation records.

For example, from the activatedFilter out a specific age range from LinkedIn numbers, and then view recent active status; you can also group them by age first, and then prepare different content based on positions and industries.

A more practical combination is:

lAge ranges are used to distinguish career stages;

lThe active state is used to arrange the processing sequence;

lIndustry and job title are used to determine content topics;

lHistorical consultation records are used to select specific expressions.

Through the combination of multiple pieces of information, data that originally only had mobile phone numbers can gradually form a clearer classification of people.

Use test results to adjust age groups

When setting an age range for the first time, there is no need to make the criteria too complicated. It can be done through Digital Planet firstLinkedIn age detection, select several main ranges to test content.

After observing the clicks, replies and consultations of different age groups, adjust the range. If two adjacent age groups have similar responses to content, they can be combined for processing; if there are large differences within the same age group, they can be further subdivided by combining positions or industries.

Digital Planet can be completed in batchesLinkedIn account activation detection and age screening, and then export the results according to the selected range. Combining age with career stage, content direction and historical needs, the classification of LinkedIn users will be clearer than simply sorting by number source.

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