Machine learning – it’s emerged as something that makes most people either afraid or doubtful. What is it? Why does it matter? Is it anything more than a buzzword that will fade out in a few years? While it’s fair to say that the hype around machine learning (ML) has been considerable, it’s demonstrably true that ML is every bit as transformative as people claim it is.
This is especially true when it comes to staffing and recruitment. In these sectors incorporating ML functionality has the potential to increase speed, promote simplicity, and improve cost-effectiveness in an area of business that has traditionally been slow, unwieldy, and difficult.
Here are the implications machine learning has on recruitment, and three reasons that recruitment and staffing decision-makers should consider integrating the technology this year:
1. Better Candidate Relationship Management
Machine learning has a massive effect on candidate relationship management, specifically where chatbots are concerned. According to recent estimates, more than 67% of global consumers have interacted with a customer support chatbot in the last year. By 2020, chatbots are projected to handle 85% of all customer interactions.
While the ML that powers chatbots isn’t meant to reduce human-to-human communication, it does help streamline customer service interactions and ensure would-be hires are getting what they need from your company, in a timely and efficient manner.
2. Easier Shortlisting
The process of sifting through resumes and LinkedIn profiles has historically been cumbersome and time-consuming. Machine learning based selection algorithms have the potential to reduce the time investment associated with this process.
By filtering candidate skills and other key metrics, ML makes it possible for recruiters to spend time only with viable, well-qualified candidates. Plus, because ML uses controllable and auditable algorithm-based sorting mechanisms, it’s less prone to discrimination based on sex, race, orientation, or other factors than human recruiters.
3. More Accurate Placement Probability Analysis
One of ML’s most significant benefits is that it offers an easy way for companies to measure the “placement probability” of a given candidate. By using the data of previous candidates that have been successful in a company, it can project the compatibility of current candidates and help teams understand how likely an applicant is to get the job. This provides companies with more accurate data and makes for easier decision-making.
The Case for ML in Recruiting
As ML tools adapt and their algorithms are refined, they offer a user experience that is continually improving. Designed to take over many of the tedious processes associated with traditional recruitment, machine learning algorithms can identify flaws in a system, correct them, and make both recruiters, and companies as a whole, much better able to function in today’s competitive world.
By promoting more effective communication and more accurate candidate assessments, machine learning helps corporations improve their recruiting efforts and secure the best possible candidates.
Focus KW: Machine Learning (ML), Artificial Intelligence (AI), Intelligence Analytics, Database, Deep Learning