HR and talent acquisition leaders regularly distinguish between “active” and “passive” job seekers, those who are actively looking for a job, and those who are gainfully employed but open to new career opportunities.
However, in reality, almost everyone is open to new opportunities. The difference is the amount of effort candidates are willing to invest in exploring a new career move.
Recruiters have historically searched for applicants by posting an ad on job sites or LinkedIn.
However, only 2% of active candidates who find relevant job ads and apply are qualified applicants.
According to LinkedIn, 70% of the workforce consists of passive job seekers and 87% of them are open to new opportunities.
But how do you find and engage with passive job seekers who are not looking in the usual places?
Recruitment is the last standing industry where people sit by computers, enter keywords, sift through listings, and contact passive candidates one by one.
It is an industry that is ripe for digital transformation.
Artificial Intelligence (AI) technology can not only save you hours of work, but it can also help you find more relevant candidates and engage with them in an exponentially better way.
Here are 5 ways to use AI to attract more ‘passive’ job seekers.
Expand your talent pool
LinkedIn has the largest pool of talent information, but it has many limitations.
First, your search is limited by three degrees of separation between you and the people you are connected with.
Secondly, LinkedIn has limited data on specific roles and industries. For example, an engineer is more likely to post important information regarding skills and experience on sites like Github and Stack Overflow.
HR teams can use AI technology to continuously search the web for digital footprints of ‘passive’ job seekers. It then scrapes that data and uses it to build rich and up-to-date profiles from multiple online sources.
Eliminate keyword search
Recruiters have become experts in creating sophisticated Boolean searches to find potential talent using targeted keywords.
But keyword search is a time-consuming and inefficient method to find talent because it only shows you candidates who have the right keywords on their profiles, not the best talent.
Using AI can save hours of trial-and-error searches by using job description text to create a search with hundreds of relevant keywords at once.
For example, it will have all relevant job titles and skill permutations. It can be much more granular than Boolean strings that include parameters such as ‘and’, ‘or’, ‘exclude’, etc. AI can attribute different levels of importance to requirements such as ‘must have,’ ‘important’ and ‘nice to have’.
More importantly, AI can learn from your candidate selection and improve your search with every selection. It can learn about your preferences and priorities and refine the search accordingly.
In addition, AI can help you optimize your search for both quality and pool size. There is usually a tradeoff between the two, but AI can find ways to optimize between them.
Predict missing skills
One of the most amazing capabilities of AI is that it can predict and add missing skills to people’s profiles.
When comparing resumes against actual candidate interviews, it is abundantly clear that many skills go unmentioned in the resume.
This is particularly true for women and minorities, who tend to post 10% to 20% fewer skills on their resumes.
The result is that they may not come up on searches that use keywords. AI can predict missing skills by looking at millions of profiles and identifying similar profiles with skill discrepancies.
Find diverse talent
One of the biggest challenges companies are facing is their ability to find passive and diverse talent. AI can change that.
First, AI can use algorithms to identify diversity by using a combination of multiple data points, such as pictures, names, schools, affiliations, etc.
Secondly, AI can optimize your job description to increase diverse talent participation in the talent pool by recommending minor changes to the requirements to make them more inclusive and effective.
Finding the right passive talent is a big challenge, but engaging with them may be an even bigger one.
LinkedIn allows you to send messages to candidates, but what if they do not respond?
LinkedIn also does not provide additional communication channels like email or phone numbers. AI takes candidate engagement to a new level.
When you select candidates manually, the number of candidates you can engage with is limited.
But AI can automatically select hundreds of qualified candidates to contact instantly for each job. The sheer number of engagement attempts makes a difference in garnering responses.
Secondly, AI can optimize the message content, sequence, and timing. It intelligently develops a message based on the type of candidate and sends it on the best day and time to achieve the highest likelihood of a response.
If the candidate does not respond to the first message, an alternative follow-up message is sent, and so on. If one communication channel (e.g. LinkedIn) does not work, then AI will recommend another one (e.g. email).
In addition, AI can predict the likelihood of candidates leaving their jobs and prioritize them by that criteria. It analyzes data points such as career patterns, average tenure, hiring/firing trends, and social signals so you know when it’s the right time to engage with a candidate before they become active job seekers.
AI can take talent sourcing to a new level of sophistication and efficiency and eliminate hours of labor and time.
Of course, it is not a silver bullet solution, but a combination of several key elements: the ability to find more talent, to search the data more efficiently, to boost diverse talent participation in the pipeline, and to engage with talent more efficiently.
The result – a pipeline of interviews with highly qualified and diverse talent without the need to spend hours posting job listings and reaching out to candidates, one by one.
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