Resume Parsing Definition

Resume parsing is the process of converting an applicant’s resume into a structured and readable format that recruiting software can analyze. The automation technology extracts key details like employment history, education background, and competencies, making it easier for talent scouts to find the right candidates swiftly. This technology aims to reduce the manual review of resumes, thereby making recruitment more efficient.

Resume parsing involves the automatic extraction of the main features from resumes for ease of examination by employers and Applicant Tracking Systems (ATS). The program scans and identifies personal contact information, abilities, training, and job experiences, arranging them in a standardized framework. This standardized data allows for easier comparison of applicants. The information is organized and categorized based on preset criteria to simplify the shortlisting process, ensuring equitable evaluations against job specifications. A uniform structure eliminates bias and errors, giving recruiters the opportunity to conduct meaningful engagements with competent job seekers.

How Does Resume Parsing Work?

Resume parsing automates the process of analyzing applicant resumes by converting unstructured data into organized candidate profiles. With this technology, recruiters efficiently sift through applications to identify top talent while accurately extracting and categorizing pertinent information.

1. Resume upload

Candidates submit resumes in various file formats such as PDF, DOCX, or RTF through online portals or directly into an applicant tracking system (ATS). Raw resume data is fed into the parsing software during this step. For instance, if a candidate uploads a DOCX resume via a job board, the parser picks up this document for analysis.

2. Text extraction

Key sections like contact details and employment history are identified, and the uploaded documents are scanned before being broken down by the parsing tool. The software then extracts sections such as “Contact Information,” “Skills,” and “Work Experience” from a project manager’s resume, for example.

3. Information classification

Once extracted, data is classified into predefined fields like “Name,” “Address,” “Qualifications,” and “Skills.” The software ensures extracted data is placed in accurate categories. For instance, education details are assigned to the “Education” field, while skills like “Data Analysis” are stored under “Skills.”

4. Data mapping

The software maps this classified data into an ATS or database, converting it into a structured format for retrieval and comparison. For instance, education details of a candidate are mapped under “Education,” while work experience is placed in “Work History.” This mapping allows recruiters to obtain detailed profiles of candidates.

5. Profile creation

Lastly, a formatted candidate profile is created from the parsed information. This profile helps narrow down resumes based on specific requirements. If a recruiter needs candidates with Python programming skills and five years of experience, they filter resumes to arrange those that meet these criteria first.

Resume parsing follows these steps to provide recruiters with standardized candidate profiles that make hiring more efficient.

What are the Benefits of Resume Parsing?

The process of resume parsing enables the rapid, automated extraction and organization of candidate data from resumes. It improves efficiency and accuracy while aligning candidates with specific job requirements, making it indispensable for modern recruitment.

1. Save time

The initial review of resumes is largely automated, significantly reducing manual work. This technology swiftly categorizes applicants based on their skills, experience, or educational background, expediting the screening process. For instance, at a tech firm, this technology saves countless hours by immediately filtering out candidates who possess specific programming skills needed in software development, rather than manually sifting through resumes.

2. Accuracy improvement

Resume parsing eliminates human errors that occur when handling resumes manually by automating data extraction and categorization. As a result, candidates receive accurate and consistent evaluations. Parsing software distinguishes educational background sections from professional experience in a CV, ensuring proper classification and minimizing misinterpretations or omissions.

3. Better candidate matching

Resume parsing tools match applicants’ qualifications, skills, and experience with job requirements. This precise matching helps recruiters quickly find the right people for positions. For example, parsing tools easily identify individuals with agile methodologies experience, filtering out others lacking relevant skills to minimize mismatching.

4. Savings on costs

Organizations reduce costs linked to manual resume reviewing and lengthy hiring processes by implementing resume parsers early in the recruitment process. Shortened processing times lead to lower labor expenses and reduced time-to-hire, which are crucial indicators in measuring overall cost per hire (CPH) reduction. A small organization saves money that would otherwise be spent on hiring additional HR staff or outsourcing resume screening by automating initial hiring processes.

5. Data management

A resume parser helps efficiently manage candidate data in one location. Recruiters maintain a comprehensive, searchable database of job seekers’ profiles, accessible for current and future recruitment. This feature is valuable when building talent pools for industries expanding rapidly, as the need for qualified candidates is constant. For instance, recruiting officers at fast-growing tech companies retrieve previously parsed resumes from an ATS to fill new positions without starting the process from scratch.

What are the Best Resume Parsing Tools?

  • ATS solutions: Some Applicant Tracking Systems (ATS), such as Workday, Bullhorn, and Lever, have integrated resume parsing features to facilitate seamless resume handling. These systems allow recruiters to efficiently manage applications by automatically extracting and categorizing resume data. For instance, Workday ATS parses resumes uploaded by applicants, filling fields like education and work history in the candidate’s profile. Recruiters can then review and manage these profiles within the platform.
  • Dedicated parsers: Tools like Sovren, Rchilli, and Hiretual specialize in resume parsing with features extending beyond standard ATS functionality. They easily integrate with existing recruitment software to enhance their capabilities. For example, Rchilli provides a comprehensive solution for parsing resumes from sources such as emails and job boards, in addition to direct uploads.
  • Customized solutions: Organizations with unique hiring needs benefit from custom-built parsers. This is useful for industries with specialized jargon or qualifications required for certain roles. For instance, a tech company needs a parser that recognizes not only standard coding skills but also emerging technologies or methodologies specific to its niche. A good custom parser will distinguish specialized knowledge for proper classification during the hiring process.