In the past three seasons of gold, silver and silver, many domestic and foreign companies have used all kinds of solutions to do various kinds of promotion. For a time, they competed and took the lead, spending a lot of manpower, material resources and financial resources to find employees.However, the demand gap between employers and applicants often plagues corporate HR and many applicants.Today, we will give some clues from the data level, and use it for reference and discussion. With JMP's visual analysis, we can help both the supply and demand sides make decisions faster.
The author of this article is Xue Xue, JMP Special columnist, date analyzing Lovers.
Candidates VS Employer: How to wipe out " Love ” The spark?
How do applicants and employers often spark each other?I believe that everyone has their own methods, headhunting?Friend introduction?Campus Recruiting?Boss group?Vibrato? Regardless of the type of recruitment, we need a macro analysis to evaluate the results to guide future work.But boring reports and text are often easy to get lost, especially for some non-professionals.In addition, how fragmented information is presented is also a major obstacle to people's rapid understanding of information. If you can find the right tools and methods to integrate as much information as possible, you can often get twice the result with half the effort.
As a data analysis enthusiast, we are not hesitating to use JMP to analyze it here to see if there are any unexpected discoveries.
do research - collect data
We have selected relevant information from an authoritative recruitment website from March to August for applicants who have successfully submitted resumes for a single position in an enterprise in Shaanxi.
According to the account province, the headers are Submit Date, Province (accounting province), and N (delivery).Next, we will use simple data processing to target the effective target data we need to analyze.Open the JMP - Table menu and select the Missing data pattern option:
Then, select the province column in the data sheet:
You can quickly locate the incomplete rows in the data table (limited to the missing input information of the deliverer), and then easily delete the missing rows in the original table.
Don't underestimate this small step: when you have a lot of missing data, you can appreciate the power of this feature.Especially when we deal with the censor data in the reliability data.After the above simple steps, the number of valid delivery is 173, as shown in the following table:
Do analysis - Based on geographic location
In the JMP software, open the Graph - Graph Builder, drag the Province column in the data table to the panel according to the blue arrow, then pay attention to the Show on the left side. Missing option to present the outline of the map of China as a whole.
Then drag the N (delivery person) column in the data sheet to the right color column by following the red arrow.At this point, JMP will help color the province and automatically generate a legend (the legend) to the right of the graph.Of course, the province must have a numerical value, otherwise it will still show empty white.
It is very convenient to use the JMP graphics generator to automatically map into geographic locations by province name.From the geographical location, it can be clearly seen that the applicants are mainly in the surrounding provinces, but the delivery of the purple color provinces is not high, but this is precisely the opportunity of other provinces' small partners, think about the 'blue ocean strategy'.We have noticed that some of the information in the work is difficult to observe through traditional histograms, and in many cases it is inevitable that people will ignore some key information.This feature of JMP just makes up for this.
In addition, the JMP graphics generator can also support the creation of maps by yourself. Interested friends can further decompose according to the city name or place name, or view the articles we have pushed in the past: 
minutes of play data analysis> series of video (10): Easily create JMP administrative map For reference.You can also use this feature to plan your own travel plans, and you can synthesize customized maps according to your own hobbies.....
Do analysis - Based on geographic location + Dynamic timeline
In addition to the above simple visualization, we can continue to explore and discover more.Open the red arrow to the left of the Graph builder and select Local Data Filter:
At this point, the Local Data Filter option will appear on the left, select "Submit Date" and click Add:
JMP will automatically split into months by month, or click the small red arrow at the top left of the local data filter:
At this time, choose Animation, JMP will automatically loop through each month (this is very useful for users who are not familiar with the loop syntax).
Using JMP's dynamic local filtering program will further reveal the law of time according to the month. It can be seen that the number of people who delivered in the colleges and universities in June and July is the highest, while the relative delivery in other months is relatively small.JMP's data filtering program can quickly and effectively present data in front of everyone according to certain attributes.This seemingly simple point is precisely the traditional chart.In addition, switching between different attribute values can be displayed dynamically, eliminating the need for programming time (except for professionals).We have always thought that powerful and easy-to-use tools can inspire the creativity of data analysts, who are not willing to spend a long time just to implement one of their own ideas.This is like when the musicians are inspired, but there is no good paper and pen to record for him.
The dynamic generation diagram is as follows:
Do analysis - Geographic location + Dynamic timeline + More auxiliary information
In addition, we can also use the dynamic bubble chart provided by JMP to make the following effects, with the job or internship of each position as the horizontal axis, with each province as the vertical axis, blue and red for men and women, and then for the month. As a time node, the size of the bubble represents the number of deliverers.
Organize the data sheet as follows (due to the length of the relationship, only part of the table is intercepted to show the header part):
Then select the Bubble plot below the Graph and place the five different columns in the Y-axis, X-axis, ID, Time timeline and Color.
The generated bubble map can also be saved in an interactive HTML format for viewing in an environment without JMP installation.
From the bubble chart you can easily find:
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With the arrival of the graduation season, the number of students who have delivered students has increased relatively, and the number of experienced off-site deliverers is basically the same at this time. Therefore, for the socially experienced deliverers, the peak delivery can be considered to improve the delivery efficiency.
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In addition, there are fewer deliverers in coastal cities. In the context of the policy of one belt and one road, inland cities are developing very rapidly. Those who have the courage to apply may wish to focus on some inland cities with development potential.
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Ending Eggs - It can also be found from the color. The candidates from different places are mostly men with work experience. It may be that male friends are more willing to try different places to develop their careers for career development and quality of life.
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summary
Through the above layer analysis, we can easily find that for the applicant:
- Based on the principles of the Blue Ocean Strategy, due attention should be paid to the development of some inland cities, so as to avoid the dilemma of thousands of horses crossing the single-wood bridge.
- For experienced applicants, you can avoid the graduation season as appropriate, so that you have more opportunities to enter the interview.
For employers:
- A more diversified approach can be taken to target the delivery of non-graduate seasons.
- According to the geographical location to improve the delivery volume of other provinces, of course, you can also increase the province's GDP and other information in the analysis to further find opportunities.
Of course, the above suggestions are based only on the clues provided by the data level investigated in this article. They are only for reference by both the supply and demand sides, so that they can be applied and recruited more specifically. For example, they can actively prepare for the next “Golden September and Silver 10”.
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