Examples Of Machine Learning Applications In Recruitment

Nowadays, the competition for top industry talent is fierce and most organizations globally are struggling to get in touch with best-fit candidates owing to their outdated recruitment strategies. Despite yielding any positive business outcomes, human resources professionals are spending their precious hours and enormous amounts of money on cumbersome manual talent acquisition tasks. Nonetheless, new-age recruiters can invest in the latest HR technologies powered by machine learning (ML) to digitize their hiring process. This way they can effortlessly succeed in their quest of identifying and recruiting the right candidates for quickly filling vacant job posts within the company. Since machine learning is still a developing technology, it is likely to exhibit growth and become more advanced with the passage of time. Other than this, it is also essential to note that each business has unique requirements and organizational goals. For this reason, HR professionals must evaluate the advantages of machine learning technologies before implementing the same in their company’s talent acquisition process to ensure that all the business expectations are met in the best manner possible. Here, we shall delve into the top 5 examples of machine learning in recruitment so that you can reap benefits from the same without any trouble.

 

machine learning
machine learning

Use-cases of machine learning in recruitment

The majority of modern-day talent acquisition professionals concur to the fact that traditional recruitment models are not efficient enough for attracting, hiring, and retaining quality job applicants in today’s tough labor market. But the recent advent of machine learning (ML) in the hiring industry has started transforming the recruitment landscape, thereby simplifying the day-to-day job of recruiters and HR managers to a great extent. Taking that into consideration, let us explore the use-cases of machine learning in talent acquisition to facilitate a clearer understanding.

  1. Recruitment automation

It is a well-known fact that multitudes of businesses in different parts of the world are integrating machine learning into their hiring strategies for facilitating recruitment automation. Machine learning algorithms are being trained to enable human resources professionals to streamline the various monotonous parts of the recruitment process to eliminate financial constraints and other similar bottlenecks that are negatively affecting the overall hiring efficiency. To give an instance, machine learning-driven virtual assistants are helping recruiters automate the resume screening process and hence, allowing them to identify ideal job candidates by recognizing regulators in data with the help of pattern recognition. By effectively digitizing most repetitive aspects of talent acquisition, machine learning is making it possible for hiring managers to quickly move well-qualified candidates through the different stages of the company’s recruitment process to ensure that they are hired faster.

  1. Smarter candidate sourcing

In simple words, candidate sourcing is basically the process of identifying and networking with prospective applicants who are likely to get hired for the open job vacancies within the department. The best part about machine learning in recruitment is that it can instantly open doors to smarter and more effective candidate sourcing. Predictive analytics involves the collection of vital recruitment-related data acquired from an array of popular sourcing channels, social media networks etc., to help organizations discover top-notch talent. This way businesses can easily target the most apt job candidates from passive talent pools. The data sources resulting from certain key metrics can also help determine whether a passive candidate will enjoy a company’s workplace atmosphere or not. Keeping everything else aside, predictive analytics can make it a lot easier for HR professionals to make more informed and fact-based recruiting decisions, thereby preparing the way for a more targeted hiring process.

  1. Effective applicant screening

One cannot deny that the manual process of screening applicants requires a great deal of effort, valuable resources, and time simultaneously. This is where advanced virtual chatbot assistants driven by machine learning can come in handy as they make use of ultramodern and sophisticated technologies such as natural language processing (NLP) to pave the way for effective applicant screening with minimal effort. The chatbots assess the candidates’ overall skill set, aptitude, culture fit etc., and ask follow-up questions so as to verify whether the applicant possesses the necessary experience required for the vacant job position he/she has applied to. Since best-fit potential job candidates are filtered based on the essential data collected from multiple sources in the very initial stages of the talent acquisition process, the precious time of HR professionals is saved as they only have to consider the applications of qualified applicants.

  1. Candidate relationship management

Another excellent advantage of machine learning-based chatbots is that they can work wonders for facilitating seamless communication with candidates who have applied for a job role. These AI-powered chatbots keep learning over time while communicating with applicants, answering common questions, and solving the various job-related queries that they pose. Besides streamlining interactions with candidates and keeping them engaged around the clock, chatbots also help HR managers with recruiting tasks such as interview scheduling for boosting hiring efficacy.

  1. Applicants placement prediction

Without a doubt, one of the biggest challenges that being faced by most human resources personnel today is none other than employee turnover and hence, boosting the overall retention rate has become extremely important for them. Recruiters can gather the essential data related to candidates who have successfully made it through the various phases of the talent acquisition process in the past to predict the placement probability of the current applicants to determine whether they will really land the job or not. Certain core machine learning algorithms collected from numerous sources, including employment history, educational qualifications, past experience etc., can be applied to the applicant-related data for shortlisting prospective job candidates swiftly.

Since talent sourcing is emerging as a key challenge for most organizations across the nation, the benefits of ultramodern technologies like machine learning and artificial intelligence are becoming clear to the new breed of recruiters. Best of all, the advanced capabilities of digital recruiting solutions allow HR professionals to make use of data analytics so that they can make evidence-based hiring decisions. Moreover, they also help them to automate manual time-consuming hiring tasks, thereby ensuring a highly efficient workflow throughout the recruitment process. Since machine learning streamlines most aspects of recruitment, recruiters and hiring managers can easily focus their energy on core talent acquisition activities that are vital for the organization’s overall growth. So, see to it that you efficiently supplement your hiring efforts by utilizing a machine learning-based applicant tracking system to drive desired business outcomes in a flash.