Automated interviewing improving recruiter efficiency
Automated interviewing is significantly improving recruiting
Automated interviewing is significantly improving recruiting, talent evaluation and experience workflows for recruiter and candidates
Advancements in automated interviewing workflows have significantly improved the experience for both candidates and recruiters.
There are three areas where we have seen a significant impact in HR with the use of machine learning and automated responses. These features have been added to our platform to benefit out customers and enhance efficiency and the candidate experience.
1. Virtual assistants and support for candidates
Today the industry needs more advanced screening processes and recruiting new applicants. They will be context-dependent and take into account each candidate’s individual traits. The increase in interaction between AI and human-machine has given a solution for us. The latest developments have shown us the potential of chatbots to assist humans in a wide variety of applications.
By combining an HR chatbot with an employer’s website, more interested applicants can be drawn by involving them in a conversation. A chatbot can also test applicants initially, by asking them about their previous experience, interests etc. and complete the pre-screening stage. With the use of Machine learning-driven chatbots on the employer’s website to deliver onboarding candidates, the early stages of interviewing will become much simpler.
Chatbots also build a win-win situation that benefits both applicants and HR professionals. Chatbots help remove the routine part of the HR work. They also make the recruiting process more enjoyable for the applicants and less stressful.
In addition to this, chatbots can provide the candidates with effective 24/7 support and try to solve their problems right away, and saving the human support team a lot of time by answering most of the questions that usually get from candidates.
2. Language-based assessment and natural language understanding as a method for automated candidate assessment and scoring
Natural Language Understanding (NLU) is an algorithm that is trained to interpret and assess competencies in human language by breaking down the elemental pieces of speech. While speech recognition captures spoken language in real-time, transcribes it, and returns text, NLU goes beyond recognition to determine context and intent. Every candidate is different and speaks in different languages, dialects and employs different phraseology. Natural language machine learning models use semantic knowledge to recognize subject, syntax, context, language patterns, unique definitions, and intent.
In our automated assessment flow, when the candidate completes the video or audio interview, a text transcript is generated from the audio portion of the interview and then is assessed by the custom trained NLU algorithm. No visual data is used in the assessment. The model evaluates the transcript based on language used and phrases spoken by the candidate. The entire process is automated which generates and presents scores to recruiters presented with the candidate profile. This can be used for data-assisted, human review decision making or can be fully automated to move candidates on to the next interview stage based on a pass or fail threshold.
3. Automated workflow and self-scheduling
Machine learning platforms can be used to screen the overwhelming amount of applications employers receive. Automation of workflows was one of the first machine learning applications in HR. In general, scheduling is a frustrating and time-consuming task. If it is optimizing onboarding, arranging interviews and follow-ups, performance evaluations and managing the more basic and routine HR questions, machine learning will take away much of this boring work from HR personnel and help reduce repetitive tasks and allow HR professionals to concentrate on the bigger problems at hand.
When it comes to our platform, it’s very flexible and easy to plug-in as many tests or interviews employers want to and they can also set a threshold value between these tests to make everything automated. Candidates go through N number rounds of interviews and tests based on NLU before the first meeting with a person for the final interview. During these tests the machine learning model analyses the candidate responses using the custom trained Natural language understanding algorithm and generates the score based on phrases that the candidate uses during the interview. Also, the NLU model automatically moves the candidate to the next stage of the interview process based on the threshold value set by an employer at each stage. This type of automation results in significant time savings in the recruiter workflow.