In any recruitment campaign, ‘active’ candidates seeking employment will apply for your positions. But other suitable candidates may be available, who are not currently looking for new role. Executive search agencies can identify and approach these ‘passive’ candidates to interest them in working for you. Passive sourcing is standard practice but there are two challenges: How do you determine whether or not someone is suited to the role? And how do you know if they’ll want to leave their current employer? Artificial Intelligence (AI) offers a new way to answer both of these questions.
Head-hunting is a complex and laborious process. It involves manually searching social profiles, chatrooms and data points to find suitable candidates—and sending multiple emails, making cold calls, and conducting initial interviews. These searches may successfully identify individuals who potentially have good person-job fit. However, it is very hard to judge whether these people are likely to want to change jobs. To ascertain this, you’ll need to analyse a significant amount of new data.
However, the ability to undertake rapid and in-depth data analysis is one of the wondrous benefits of Artificial Intelligence (AI). Dedicated algorithms that utilise keywords and descriptions can help you to find more and better-suited candidates. This, on its own, is immensely valuable. But what’s even more impressive is that AI can prioritise the candidates who are most likely to be interested in your role, by quickly and easily analysing a candidate’s career history and the circumstances and context of their current employer. For example, a candidate is more likely to be receptive to your new job opportunity if:
There has recently been a significant change in their organisation, such as a merger or acquisition.
A new CEO has been appointed.
The share price of their organisation has fallen substantially.
They have exceeded the average tenure of their organisation. If the average tenure is three years and they’ve been there for longer than that, they may be open to a move.
They’ve exceeded their own personal average tenure. If, on average, they’ve stayed two years in each of their previous roles, they may be open to a move after two years in their present position.
To manually check and review all of these circumstances for every candidate would be expensive and impractical. However, AI systems can quickly and efficiently evaluate and process this kind of data, for hundreds of individuals at a time. As a result, AI makes it much easier to identify which candidates would be more receptive to your approach.
It doesn’t stop there. AI can also predict which ‘channel’ would be the best way to contact each candidate—by email, telephone, or through a personal approach at an industry conference or event. AI systems can automatically create personalised email approaches or telephone scripts that would appeal to each candidate. They can also be programmed to include key questions that would resonate with the individual based on their circumstances. For instance, after a recent merger, the approach might be: Do you feel valued in the new organisation?
This whole process of identifying, contacting and following up passive candidates can be automated using multiple AI systems. With metrics, you can monitor and appraise the process, in exactly the same way that a marketing team would monitor the impact of an online newsletter. For example, you can track the numbers who opened your email, track the ‘click rate’ for any content and monitor the resultant outcomes. By learning what works well—and what doesn’t—you can enhance and improve your passive sourcing.
The real challenge with any selection process is to achieve perfect optimisation. You want all of your recruitment interventions to work together harmoniously. This isn’t easy when you’re partnering with specialist providers. However, it certainly helps if each partner understands and can support the others.
For example, the analytics from your assessments can add value to your passive sourcing. Let’s say you want to recruit a PhD data scientist and you decide to supplement your active sourcing with passive sourcing. Your assessment data may reveal no difference between candidates who have a Masters degree and those with a PhD. In other words, you might find that actual performance in the role does not increase if a candidate has a PhD, compared to someone with a Masters degree. By feeding this insight back to the AI algorithm used for passive sourcing, you can start to include candidates with Masters qualifications in your searches.
The point here is that everything should tie together. Passive sourcing can be enhanced by AI but it is only one part of the whole. Perfect optimisation comes through partnering with specialists who can complement each other, raise each other’s game, and deliver additional value.
This article was first published in The Global Recruiter on 21 August 2018.
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