Artificial Intelligence can transform passive sourcing but the ultimate goal is to achieve perfect optimisation says Richard Justenhoven.
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 traditionally a complex and laborious process. It involves manually searching social profiles, chatrooms and data points to find suitable candidates – and sending multiple emails, conducting cold calls and undertaking initial interviews. These searches may successfully identify individuals who are potentially a good person-job fit. However, it is very hard to judge whether or not these people are likely to want to change jobs. To ascertain this, you’d need to analyse a significant amount of new data. In truth, this is prohibitive to accomplish manually.
However, the ability to undertake rapid and in-depth data analysis is one of the wondrous benefits of Artificial Intelligence. 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.
AI does this 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. For example, 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. For example, they can 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.
Integrating into Your Selection Process
Importantly, whether your candidates are sourced actively or passively, you should still assess their suitability. Yes, passive sourcing can help you to identify good candidates – and, with AI, you can judge how receptive they’ll be to your approach. But you still need to ensure that they’re a good fit for the role and for your organisation. Your assessments and interviews should help you to predict which candidates are likely to be successful.
It’s also important to ensure that your candidates join with the right expectations. Best practice is to ensure that they complete a Realistic Job Preview. This is an interactive, online experience which highlights the demands of the role and the culture of your organisation. Realistic Job Previews are often used for frontline roles. However, they can also be created for senior positions, to give candidates a better understanding of the role requirements and the values of your organisation.
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.
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