Applications of Predictive Analytics in your Recruitment Journey
I am a product marketing associate. I am passionate about creating, managing, and marketing content.
Elanor is an HR executive at Unicorn marketer. She’s been involved in the recruitment process for six years now. Every year they do a campus drive at the most prestigious college in Chicago. They’re always on the look for a promising candidate for a challenging role as a Digital Marketer. Elanor has been maintaining a spreadsheet of rejected candidates for the same post and logging the reasons for rejection as well.
The reasons read as follows - lack of analytical and writing skills, poor communication, showed no scope for management abilities, and so on.
So, the above scenario is a representation of how there’s enough scope for the HR department at Unicorn marketer to make data-backed decisions with predictive analytics. Before we jump into predictive analytics in hiring, let’s quickly define predictive analytics in general.
Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modeling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events. - Wikipedia.
Let’s get back to the example we began with. For instance, let’s say, 31 candidates, have been rejected in the last six years - future hiring process can certainly be streamlined by taking the right steps to attract the right talent possibly by mentioning it in their hiring announcements - posters, display ads, first calls, interactions, and so on. How do we conclude so? It’s by adopting predictive analytics in recruitment process i.e utilizing historical data to make predictions about the future.
4 Ways you can leverage Predictive Analytics in Recruitment
1. Sourcing the right talent
“A study by WCN solutions 52% of recruiters say finding the right candidates from a large applicant pool is the toughest part of their job. “
Predictive HR analytics can play a crucial role right from the time of sourcing a candidate. So. let’s say Elanor has recruited qualified digital marketers in the past. A year after their joining, there was a performance evaluation. Some of them were tagged to be good performers and some, mediocre. So let’s say in the past six years, the digital marketing head has data about the reasons for mediocrity in performance and based on predictive analytics, they identify a trend.
The trend clearly indicates that the common reasons were the inability to strategize based on analytical data. The digital marketers who were tagged as mediocre performers were not able to rethink and realign strategies to suit the insights inferred from marketing data.
So, this gets communicated to Elanor, to ensure that she incorporates this into her recruitment process - possibly test the candidates on such skills to ensure that the right talent is being attracted - because trust me, no one wants mediocrity.
The data sets are analyzed for seasonality and variability. Also, the data sets are evaluated to identify any errors in terms of structure, recency, accuracy, and more. The statistical experts deploy tools to identify patterns and as more data is fed, the model trains better.
Sourcing of talent is not just limited to predictive analytics based on performance in the past as mentioned in the example, it covers various other factors some of which are - data on attrition, employee life cycle, age, gender years in current role, and employee survey feedback. Similarly, the volumes of data involved could also be hundreds and thousands in order to make accurate predictions and this is usually adopted by huge tech companies.
Another side of the sourcing story:
In the digital age, sourcing the candidate does not confine just to face-to-face interviews at the office premises or college campuses where candidates make direct applications. Employers in the present day do employee hunting from numerous online job boards and portals. Based on historical data of what has worked for the company that is identified through recruitment analytics, accurate predictions can be made on which source to rely upon and what skills are required for a particular profile based on in-depth scrutiny. Let’s see that in detail now.
2. Targeted hiring process
Company culture is increasingly becoming an important criterion to consider working for an organization. With the millennial lifestyle getting stressed every day, people are on the lookout for additional perks like the good working environment, recreation, and other facilities - let alone monetary compensation.
This gets communicated to the potential candidates through a strong social media presence through a separate corporate handle - you must be familiar with “life at Google” and “life at Spotify”. These social media handles, of course, have postings about job vacancies, but they establish brand value and aura through engaging posts about the work environment.
While social media is just one channel, there are several others like job adverts, online job sites, direct Linkedin searches, skills recommender engines, and more. HR professionals focus on all such channels equally to get the right application. But, it so happens that you might not be able to do this equally efficient across all the channels.
Job adverts: In order to get better responses to the job postings, job adverts can be optimized. Data analytics in recruitment can help employers through recommendations on how to get prompt responses based on data around location, industry, and more.
3. Prevention of Employee turnover
Employee retention is a critical goal for an HR department as it can have serious repercussions in case of poor turnover rates. It not only is expensive but also consumes a lot of time in hiring and training new employees. HR professionals have tried to overcome the issue of employees leaving their organizations by strategizing activities based on employee surveys.
But in this day and age, employee surveys alone does not make the cut. For one main reason being, you can’t trust the data. There is a huge possibility of employees being dishonest even if they are anonymous surveys. So, now on the other hand we have predictive analytics.
Just like how a Netflix algorithm works, suggesting the next best show to watch based on your history (shows watched), similarly, it is possible to detect an employee’s risk of churn based on data that you already have.
The HR department has a ton of employee information at their disposal which could be the marital status of the employee, application for maternity leaves, performance reviews, income, sick leaves taken, and a lot more. Sometimes, data on the employee access cards can be analyzed to see the trend in who takes breaks together and identify who gets along. This can come in handy in seating arrangement so that employees do not feel stressed and pressured.
Application of some processes and statistical methods upon this data - logistic regression, neural networks, tree analysis, and discriminant analysis based on which trends and patterns are analyzed.
The insights will give the propensity to leave.
4. Improve training
Corporate training is increasingly moving towards e-learning methods that predominantly function based on predictive analytics. Based on past performance indicators in evaluations, these training modules are personalized depending on the need of the employee - the skills they need to be trained on, gaps and goals are identified, their strengths and weaknesses are spotted and the courses are streamlined to suit their requirements.
This kind of personalized training is required as an organization has thousands of employees who have different skills, qualification, and belong at different bands and designations. So, a generic training doesn't work as there is no one size fits all. Also, depending on the career movements which could be promotions or moving across to an other designation, the training based on predictive analytics.
These training models offer certain additional information that aids employee retention and identifying an employee who’s likely to stay. If they are invested in the training process to continually upgrade their skills it is an indicator that they are likely to up-skill and it is worth investing in the employee.
If certain employees seem to be laid back with their course goals, it is an indicator that they’ve been swamped or pressurized with a lot of work and they need to be taking more breaks and utilize time productively for training.
Here’s an interesting example of how Jetblue Airlines improved their training process through predictive analytics:
Jetblue Airlines focused on ‘niceness’ as the most important attribute for flight attendants. Wharton Business School, conducted customer data analysis and found that their customers preferred “helpful”rather than “niceness”and can even make up for people being not so nice. They utilized this analysis in their hiring and training process.
Hiring is an art. But, in the present day, you don’t have to use instincts or rely on gut feeling to recruit the best fit for your organization. Predictive analytics for human resources should be deployed and preferred any day for its accuracy.
Make predictions about a candidate based on data aggregated from job boards and portals, social media sites, networks, and career sites, etc.combined with data available with the employer to detect trends and provide insights on future possibilities.
X-tract.io can aid you in the hiring process by providing the right data and insights at every stage of the hiring process to help you streamline the process and ensure a successful and long-term relationship with your employees.
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