Data-driven HR by Bernard Marr

Author:Bernard Marr
Language: eng
Format: epub
Publisher: Kogan Page Limited
Published: 2018-02-28T16:22:28+00:00

Machine learning and AI in recruitment

There are many AI-driven tools that can be used to narrow down, test and assess candidates by, for example, asking them common questions about the job. But some assessments go way beyond this to create immersive gamification experiences, a bit like the Marriott game discussed earlier in the chapter. Using machine learning and AI techniques, the process of attracting, sourcing, matching, screening and assessing candidates is automated using data from all sorts of sources, including the employer’s own data, social-media data, employee history etc. Machine learning and AI algorithms like those offered by HR software company Restless Bandit help companies to filter out much of the ‘noise’ when it comes to selecting candidates. While this used to be a slow process of humans reading every CV and sorting them into piles of ‘yes’, ‘no’ and ‘maybe’, now algorithms can model patterns of hiring to find exactly the right people quickly and easily. Crucially, this process removes the biases that humans inevitably bring to the recruitment process, meaning candidates hopefully will no longer have to hope that they will not be overlooked because the interviewer does not like the tie they are wearing, or because they went to the wrong school.

Connectifier is one company aiming to revolutionize the recruitment process through AI technology, and build up more than 300 million online profiles to help employers find the perfect candidate.12 The company – which was founded by ex-Google workers – collates data from across the Internet to help find people who are ideal for specific jobs, whether those people are currently searching for jobs or not. Connectifier’s platform sorts and combines data to create a complete profile of potential candidates, then lets employers feed in the types of skills and expertise they are looking for to find perfect matches based on that information.

JetBlue Airlines gives us a great example of data analytics being used to boost recruitment processes.13 Previously, the company had focused on ‘niceness’ as the most important attribute for flight attendants. Then, after carrying out some customer data analysis with the Wharton Business School, JetBlue was interested to find that, in the eyes of its customers, being helpful is actually more important than being nice, and can even make up for people being not so nice. The company was then able to use this information to narrow down candidates more effectively. This just goes to show that you do not have to be a data powerhouse like Google to use talent analytics to boost your recruitment processes.

Of course, there are a lot of costs involved in hiring an employee, including advertising and the selection process itself. And when an employee turns out not to have the required skills or to be a poor fit with the company’s culture, this can be an incredibly expensive mistake (not to mention a waste of time). While there is obviously a cost involved in implementing machine learning systems, over time they are likely to save money, effort and resources,

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