Technology

Technology has fundamentally changed the functioning of the workers and companies. The technology has fully improved and reinvented the processes and structures of different sectors. Technologies such as applicant tracking systems, online job boards, and professional networking sites, such as IT systems had changed significantly the recruiting process and the job application over the last two decades.

Nevertheless, the accessibility and ease of use of virtual recruitment resources has contributed to an immense rise in applications for candidates, even to jobs for which they might not be eligible. Thus, the recruiters are daunting and the recruiting process causes gridlocks and inefficiencies.

Below is L'Oréal's case-study on using artificial intelligence to improve the hiring process:

L'Oréal Group is the greatest cosmetics business in the world. The self-proclaimed goal is to 'invent beauty' and give people accessibility to the best quality and health of cosmetics. It has a position in 150 nations, with 82,600 members working for 34 multinational brands.

Company Challenge:

 2 million applicants apply to different employment at L'Oréal web each year. Each year the company hires 5,000 employees out of those 2 million unique applicants. A recruiting team consisting of 145 recruiters worldwide carries out the work of choosing the 5,000 applicants out of the specific 2 million CVs.

Obviously, L'Oréal needed a more creative strategy to address the following 3 challenges:

1. Decreasing the turnaround time for recruiting
2. Boosting applicant experience
3. Recruiting the right applicant

Solution:

Partnered with Seedlink Technology, L'Oréal has implemented Machine Learning and Artificial Intelligence in order to stop incorporating non-value responsibilities and concentrate on more value-added hiring activities.

For the application process, L'Oréal implemented Chabot, Mya which supported work searchers such as a human recruiter, screening potential applicants for the business.

Mya, who works on artificial intelligence and natural language processing verify credentials, ask, and answer questions from work candidates about issues such as, policy, business culture, and advantages. After Mya ends asking and checking for qualifications, it will send feedback to the employers to inform them about the "non-fit" and "finest-fit" for the work.

Although Mya took care of the harder factors such as wages, location, qualification, the company felt the obligation to build automatic operation for the evaluation of candidates on the softer factors too.

Therefore, L'Oreal authorized Seedlink IT to utilize their algorithms and build a personalized artificial intelligence method based on L'Oreal competency frame, which asked 3 questions from the applicants who performed the initial contact with Mya. The 3 questions are:

1. Inform us about a project you failed worked on. How have you learned from this experience?
2. Inform us about the project where you worked with a multi-cultural  group and what was your experience?
3. Inform us about a circumstance where you are sure of your concept but your elders were not. How had you persuade them?

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