The Dawn of e-Headhunting

by David Barrett, Chief Commercial Officer of global assessment specialist cut-e.

Intelligent personal assistants, such as Amazon Echo, provide relevant answers to specific questions.
Soon, recruiters will have their own ‘intelligent assistants’ which will use predictive algorithms to trawl through ‘reactive’ and ‘non-reactive’ data to provide manageable and meaningful information about job candidates.

Reactive data occurs when an individual reacts or responds to something. Assessments are a good example. You set a psychometric test, the candidate responds to it and you gain data as a result. Non-reactive data is information that you can obtain about a candidate, without them taking any action. Much of this data is publicly available online, for example on social media sites, and via the ‘digital footprints’ that we all leave behind us when we conduct online activity.

Through artificial intelligence and machine learning, algorithms can analyse everything from a candidate’s choice of words, their gestures and the emotional tone of their social media posts. All of these details can be compiled and combined with reactive assessment data – by your intelligent assistant – to form a psychological profile of each candidate. In other words, you gain a more holistic view of your candidates – and their suitability for your roles. By reviewing these profiles in the early stages of your selection process, you can narrow down your candidate pool.

Effectively, your intelligent assistants will act as e-headhunters. For example, you could ask your intelligent assistant to find you a good systems engineer. The algorithm would understand the skills and traits that are required and it would source a shortlist of possible candidates who could be approached. It would provide you with an initial report of the insights that it has obtained about each candidate, from their publicly-available data.

This won’t include their browser history. A person’s browser history is linked to their IP address (the ‘address’ assigned to the device on which they access the Internet). A history of your browsing sessions will be stored within your Internet browser. This means that it isn’t currently possible to know ‘who’ is browsing your careers site. However, you’ll know which other sites that individual has visited before they came to you. So you should be able to make some assumptions about them, using these insights. The benefit of this is that you could highlight different jobs to different people when they view your careers page, based on what you’ve deduced about their interests.

There will undoubtedly be concerns about privacy issues and the legal aspects of uncovering publicly available data about candidates. However, companies already conduct pre-hire background screening checks on prospective employees. These often cover sensitive details such as education credentials, employment verification, credit/financial history checks and criminal record checks. Companies justify and defend these actions by saying they are protecting their work environment, their brand and their reputation. This is no different to trying to improve the quality of your hires by analysing a candidate’s publicly-available data.

It’s important to emphasise that non-reactive data should not be used in isolation. It should only be used in conjunction with reactive data to provide a clearer picture of your candidates. Direct ‘measurement’ of candidates through assessment should still be undertaken. For example, if I want to know the weight of a person, I’d put them on some bathroom scales. That ‘measurement’ would be accurate. But if I supplement this with additional data – such as the fact that they regularly order a large pizza delivery – I can then start to make assumptions about what their weight might be in the future. The more ‘measurement points’ you have, the more accurately you’ll be able to predict future scenarios.

When recruiting talent, you want as much information as you can get, to help you make the right decisions. Already, too much information is publicly available for any human to process. And the quantity of available data is rapidly increasing. This has created a market for intelligent assistants that can collate and interpret relevant insights into manageable reports, so that a ‘human’ decision can be made about which candidate is best-suited to the role and the organisation. As yet, no one has developed an assistant who can do all of this. When they do, the world will beat a path to their door.