Contextual resume screening and resume parsing are different methods used in the recruitment screening stage of the recruitment process to extract information from resumes. However, they differ in their approach, by way of interpretation, presentation, accuracy & speed of the data
Conventionally recruiters have been screening through the resumes manually to come up with a shortlisted set of candidates. Screening each resume takes approximately 10 minutes per resume at the minimum for a mid-level position with about 5-8 years of experience. Now since this is a manual process, it is highly prone to errors, biases, and duplication, along with hindrances in the speed and accuracy of delivering the right shortlists. With the shortage of talent in the market, this process is no longer acceptable in this competitive scenario where TATs are continuously under pressure due to overall organisation KPIs getting impacted. This has given rise to the situation where the right candidates need to be identified with speed and accuracy to put them through the recruitment funnel.
Recruiters then moved on to using the CTRL F function to match skills to the JDs and extract data manually from the resume to prepare a shortlist of the candidates. This process still involved manual interventions and was not able to keep pace with the increasing demand for talent to keep pace with technological development across industries. Hence the need for an assistive technology that could aid the recruiter to achieve better results with shorter TATs is sought after.
This gave rise to resume parsing which is, in layman’s terms, an algorithm that replicates & automates the CTRL F function. Resume parsing is a process that extracts specific information from a resume, such as the candidate’s name, contact information, work experience, education, and skills. It uses a combination of algorithms to identify and extract required data from the resume, but without having the relevancy of the parameters being put into context. This has helped speed up the shortlisting process and has helped reduce TATs. But it still requires manual intervention to read the data wherein the algorithm only screens the resumes based on parameters/keywords set in the JD. It still needs to be validated manually to match the skills to the relevant no. of years, companies, locations etc.
On the other hand, contextual resume screening is the advanced method that has come into existence which uses machine learning algorithms to analyze the context of the entire resume, rather than just extracting specific pieces of information. It takes into account the overall structure of the resume, the language used, and the candidate’s experience and skills to determine whether they are a good fit for the job.
Contextual resume screening can also identify patterns and relationships between different pieces of information in the resume, such as the candidate’s job history and the skills they have developed over time across projects. This can provide a more comprehensive view of the candidate’s qualifications and help recruiters make more informed hiring decisions.
This AI-Powered solution was conceptualized and developed by FeedFront Technologies for their flagship platform – RiteHire Recruit based on the current need for recruitment professionals to demonstrate better TAT without compromising on the speed and accuracy of the deliverables. Contextual Screening is a state-of-the-art, patent-pending NLP Algorithm, designed to read through candidate resumes like a human being does by reading through the entire document line-by-line and then mapping the findings with the JD to decide on the shortlisting – all of this in just under a few seconds.It then converts the unstructured data on the resume into a structured format. The resultant structured data is further classified into personal, skills, employment, education & other details of the candidate as required and customised to the recruiter’s needs. The algorithm recommends whether a candidate is shortlisted or not for the job interview, based on the match percentage with a JD. This structured classification of the candidate details facilitates a faster and more accurate mapping of the candidate with the Job Description in focus. The data picked up as a result of the contextual analysis is accurate to above 90%,
The TATs of HR depts will now be reduced from hundreds of Manhours to mere Manminutes with an accuracy of 90%.
If you are a recruiter reading this and wondering what are the steps involved. It’s really very simple
1. Create a JD on the RiteHire Recruit platform
2. upload resumes/choose resume repository.
3. RiteHire Recruit will screen through each of the resumes and does the shortlisting of the right candidates within a few minutes – with 90% accuracy.
4. Pick the shortlisted RiteHire candidate & kick off your interview process
In summary, while resume parsing focuses on extracting specific pieces of information from a resume, contextual resume screening takes a more holistic approach by analyzing the entire resume and providing a more detailed assessment of the candidate’s qualifications. All HR leaders and HR professionals will now be able to significantly contribute to the overall organizational KPIs with demonstrable impact on time & cost by deploying RiteHire Recruit solutions from Feedfront technologies.
