RecruitNow: Machine learning improves recruitment process

Walvis Participaties, a venture capital fund, has invested in RecruitNow, previously known as Minescape. This capital injection of ‘several millions’ for the company specializing in online recruitment will help it expand faster domestically and abroad. In addition, RecruitNow has announced an increased investment in innovation. Artificial intelligence has become an even more important necessity in the tight labor market.

This investment announcement follows on the recent news that Minescape was renamed as RecruitNow. These two developments are not completely coincidental, explains André Polderman, CEO and founder of RecruitNow.

Efficiency must improve
Polderman worked with COO Paul Wilkens, who joined RecruitNow at the end of 2017, to develop a clear objective: substantial growth in his own country as well as clients with a global perspective. The online recruitment company was assessed on three factors for the capital injection: ‘Disruptiveness, brains and tech, and sexiness.’ Walvis Participaties was able to help with these first two. By putting RecruitNow forward as a brand name, the two want to make a new step, especially for the third factor. “We are not a young start-up,” Polderman notes. “But we have similar local and international ambitions. With a new brand that clarifies our direction, it’s easier to achieve that level of growth.”

RecruitNow expects to respond to developments in the market with the injection of capital and solutions for campaign management and recruitment sites. Due to the labor shortage, the recruitment process must become more efficient to continue its growth, Wilkens explains. To better reach candidates, ‘smart’ recruitment campaigns must align with high-converting websites and have talent pools. Integrating data is also an important part of the solution. “We are already at the forefront of this. But we also want to take the next step. We have a lot of data thanks to the many types of campaigns we run. By investing in artificial intelligence, we can use it to predict better, and then improve results.”

Machine learning makes more accurate predictions
The labor market shortage has forced companies to evolve, including a focus of investing more in latent job seekers. These are people not actively looking for a new job, but with the right communication they are interesting. The data we collect can be used to develop, organize and optimize recruitment campaigns and job sites.

Machine learning offers substantial value by matching vacancies to prospective candidates. The data reveals the kind of people who previously were a good match to a vacancy or identifies mutual connections between target groups.

Paul Wilkens: “That helps us make highly accurate predictions. Where should we be and who is a likely match? These days, employers often have to deal with unusable traffic, which is unfortunate. It’s preferable to get twenty applications with three placements than one hundred interested people and only one match.”

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