The importance of matching candidates with job roles
Today, the primary source of competitive advantage between firms is their quality of talent. However, just procuring high-quality talent is not enough. They must also be placed in the correct positions to maximise their abilities. Studies by McKinsey show that organizations that allocate talent in suitable roles outperform competitors 2-to-1. Assigning talent to roles that complement their skills ensures they remain self-motivated and engaged. As a result, organizations can meet their business objectives while reducing turnover and increasing talent retention. Studies by the University of Warwick have shown that employers who make the most of their employee’s skills see an increase in productivity and economic growth.
In the past, job matching was a tedious process for recruiters, involving manually keeping track of a candidate’s qualifications and cross-referencing them with job requirements. This results in increased time-to-hire and a decrease in hiring accuracy due to recruiter biases. However, with the help of end-to-end recruitment platforms, recruiters can leverage technologies such as artificial intelligence, machine learning, and natural language processing to obtain data-driven insights about a candidate’s fit. impress.ai’s end-to-end recruitment platform automatically matches and recommends the best job positions for each candidate, enabling recruiters to maximise candidate conversions and improve the efficiency of the recruitment process.
Job Matching and Recommendation: The role of artificial intelligence, machine learning and NLP
Traditional recruitment platforms rely solely on keyword-based searches to match candidates with open positions. As a result, unqualified candidates may get shortlisted and a recruiter may onboard low-quality candidates.
Instead of surface-level analyses such as keyword screening, impress.ai’s end-to-end recruitment platform comprehensively evaluates candidates using factors such as cumulative assessment scores and past employment patterns. The job matching and recommendation feature allow talent acquisition professionals to automate the matching and recommendation of job positions. Using artificial intelligence, machine learning and natural language processing, jobs are matched and recommended based on candidate assessment scores and data from resumes. Candidates are also assigned a confidence rating based on their estimated fit for a position.
Job matching enables recruiters to quickly evaluate the suitability of candidates for multiple positions. Recruiters can sort and rank the best options for open roles, allowing them to quickly identify and focus on high-value candidates while finding the perfect fit for their requirements. Recruiters can hence skip the initial shortlisting stages, resulting in more efficient hiring. This translates to a noticeable improvement in an organization’s performance and efficiency.
Our proprietary AI/ML models continuously learn and re-assess candidates when recruiters update their job listings. Candidates can subsequently be re-targeted easily for positions that may better suit their skillset, allowing recruiters to focus on other strategic activities. Each time recruiters log in to our platform, they can source current and previous candidates that are a recommended fit for open roles and reach out to them with a few clicks.
Leveraging AI-powered platforms to match and recommend candidates with open roles is an essential part of the future of talent acquisition. Features such as job matching and recommendations are a must-have in any end-to-end recruitment platform for recruiters to maximise recruitment efficiency. These features enable recruiters to quickly shortlist candidates, remove bias from the hiring process and reduce candidate sourcing time. It also frees up time for recruiters to build strategic relationships with their candidates. Lastly, features like these also provide candidates with an opportunity to find suitable positions for themselves, which can be helpful for candidates looking to make a mid-career switch.