During the recent peak recruitment season of March and April, numerous domestic and international companies pulled out all the stops for promotion, creating a fiercely competitive market where everyone was vying for attention and investing significant human, material, and financial resources in finding employees. However, the gap between employer and job seeker needs often perplexes both HR departments and applicants. Today, we'll offer some clues from a data perspective to stimulate discussion and provide insights, leveraging JMP's visual analytics to help both sides make faster decisions.
This article is written by Gun Xue.JMPSpecial columnist,Data AnalysisEnthusiasts.
How does your company recruit?
How do job seekers and employers typically connect? Everyone has their own methods: headhunters? Referrals? Campus recruitment? Business partner groups? TikTok? Regardless of the recruitment method, we need a macro-level analysis to evaluate its effectiveness and guide future work. However, dry, rigid reports and texts can be confusing, especially for non-professionals. Furthermore, how to present fragmented information is a major obstacle to quick comprehension. Finding appropriate tools and methods to integrate as much information as possible can often achieve twice the result with half the effort.
As data analysis enthusiasts, we are ready to use JMP to analyze the data and see if we can find anything unexpected.
Conduct a survey—collectmaterial
We selected relevant information from a reputable recruitment website from March to August regarding applicants who proactively submitted their resumes for a single position at a company in Shaanxi.
The table headers, organized by province of residence, are Submit Date, Province, and N (number of submitters). Next, we will perform simple data processing to identify the valid target data we need for analysis. Open JMP—Table, and select the Missing data pattern option:
图1 missing Data pattern.png
Then, select the province column in the data table:
Figure 2 Provinces.png
This allows you to quickly locate rows in the data table that are incomplete (due to missing information from the sender), and then easily delete the missing province rows from the original table.
Figure 3. Censored Data.png
Don't underestimate this small step: you'll appreciate its power when you have a lot of missing data, especially when dealing with censored data in reliability data. After these simple steps, a total of 173 valid submissions were received, as shown in the table below:
Original data (173 records).png
Do analysis—Based on geolocation
In JMP software, open Graph -> Graph Builder. Drag the Province column from the data table to the panel following the blue arrow. At this point, be sure to select the Show missing option in the lower left corner to display the overall map outline of mainland China.
Figure 4. Graphic Generator 1.png
Next, drag the N (number of deliveries) column from the data table to the Color column on the right, following the red arrow. JMP will then help color the provinces and automatically generate a legend to display on the right side of the graph. Of course, the provinces must have numerical values; otherwise, they will remain blank white.
Using JMP's graph generator, you can automatically map geographical locations by province, which is very convenient. The geographical location clearly shows that applicants are mainly from surrounding provinces, but the number of applications from provinces marked in purple is low. However, this presents an opportunity for applicants from other provinces – consider a 'blue ocean strategy'. We've noticed that some information is difficult to observe using traditional bar charts, often leading to the overlooking of key information. JMP's feature precisely addresses this issue.
Figure 5.jpg
In addition, the JMP graphics generator also supports creating your own maps. Those interested can further break it down by city or place name, or refer to our previous article: 
Minutes to Master Data Analysis> Video Series (10): Easily Create JMP Administrative Maps . You can also use this function to plan your travel itinerary and create customized maps according to your preferences…
Do analysis—Based on geolocation+Dynamic Timeline
Beyond the simple visualizations described above, we can continue exploring and discover more. Open the red arrow on the left side of Graph builder, and then select Local Data Filter:
Figure 6.jpg
At this point, the Local Data Filter option will appear on the left. Select "Submit Date" and click Add:
Figure 7.png
JMP will automatically split the data into month-by-month counts. You can also click the small red arrow in the upper left corner of the local data filter:
Figure 8 Select Month.png
If you select Animation at this point, JMP will automatically perform a loop traversal according to each month (this is very useful for users who are not familiar with loop syntax).
Figure 9 Animation.png
JMP's dynamic local filtering program further reveals patterns by month, showing that June and July are the peak months for university graduation applications, while other months have relatively fewer applications. JMP's data filtering program can quickly and effectively present data based on a specific attribute. This seemingly simple feature is precisely what traditional charts cannot achieve. Furthermore, the switching between different attribute values can be dynamically displayed, saving programming time (except for professionals). We have always believed that powerful yet easy-to-use tools inspire the creativity of data analysts; no one wants to spend a considerable amount of time just implementing a single idea. It's like a musician having inspiration but no good paper and pen to record it.
The dynamically generated image is shown below:
Map-2.gif
Do analysis—Geographical location+Dynamic Timeline+More auxiliary information
In addition, we can use the dynamic bubble chart provided by JMP to create the following effect, with the length of work or internship of each job applicant as the horizontal axis, each province as the vertical axis, blue and red representing male and female respectively, and months as time nodes, where the size of the bubble represents the number of applicants.
The data is organized as follows (due to space limitations, only a portion of the table is shown, including the header):
Data sheet for another position.png
Then select Bubble plot under Graph and place the 5 different columns into the Y-axis, X-axis, ID, Time axis, and Color respectively.
Figure 9 Bubble Chart.jpg
The generated bubble chart can also be saved as an interactive HTML format so that it can be viewed even without JMP installed.
Bubble Chart-1.gif
It can be easily seen from the bubble chart:
| 1 |
With the arrival of graduation season, the number of job postings from recent graduates increases, while the number of experienced delivery personnel from other regions remains roughly the same. Therefore, delivery personnel with work experience can consider delivering during off-peak hours to improve delivery efficiency. |
| 2 |
In addition, there are fewer applicants in coastal cities. Under the Belt and Road Initiative, inland cities are developing very rapidly. Applicants with courage may want to focus on some inland cities with development potential. |
| 3 |
Bonus detail at the end - the colors also reveal that most of the applicants from other locations are men with work experience. This may be because male friends are more willing to try working in other places to expand their careers for the sake of career development and quality of life. |
summary
Through the above analysis, it is not difficult to see that, for applicants:
- Based on the principle of the Blue Ocean Strategy, we should pay appropriate attention to the development of some inland cities, which can help us avoid the predicament of everyone trying to cross a single narrow bridge.
- For experienced applicants, it may be advisable to avoid the graduation season, as this will increase their chances of getting to the interview stage.
For employers:
- More diversified approaches can be adopted to increase the number of applications submitted outside of graduation season.
- Increase delivery volume to other provinces based on geographical location. Of course, you can also add information such as provincial GDP to the analysis to further explore opportunities.
Of course, the above suggestions are based solely on the clues provided by the data surveyed in this article and are intended for reference by both supply and demand sides, so as to make more targeted applications and recruitment. For example, they can help prepare for the upcoming "golden September and silver October" recruitment season.
If you'd like to experience JMP visual analysis, feel free to click here to download a 30-day free trial.
