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Should You Use Resume Parsing? Here Are the Pros and Cons

July 29, 2020


When your company posts an available job, it’s always a good feeling to see tons of potential candidates take interest and apply.

It can also be overwhelming to know that you’ll have to sift through and read every resume and cover letter that comes in. Thankfully, you don’t have to spend hours of your valuable time trying to find an application that fits the job description. Instead, you can take advantage of a resume parser.  

As a recruiter or HR professional, it’s common to use a resume parsing software, usually a part of most Applicant Tracking Systems software (ATS), as a step in the hiring process to create an easier, convenient, and more efficient experience for all candidates. 

Not only will doing so allow you to electronically gather, store, and organize all of the information found within resumes and cover letters but also be able to hire the right candidate faster than you would without using a parser.

How does resume parsing work?

It’s the job of the resume parser to locate and extract the key elements of a resume or cover letter, like a candidate’s name, email address, contact information, their degree(s) and certifications, relevant skills, current company name, and past work experience. 

Certain elements of a resume or cover letter make this more difficult. If an applicant chooses to use soft font colors, fancy typefaces, or headings that have been created in external programs like Photoshop, it can skew how a resume parser can read the text.

As an example, large headings, unusual character spacing, and abnormal font choice can result in the resume parser reading my name, Mara Calvello, like:

  • MaraCalvello
  • Mmaarraa Ccaallvveelloo
  • Mar aCalvello

Once the parsing tool is finished with a document, you’ll be able to easily search the resume data for specific keywords or phrases, thanks to advancements in machine learning. The program used can also search for these terms and works to bring relevant resumes and applicants front and center as you search for the right candidate. 

It’s also possible for you to tailor the fields and forms to gather specific information that isn’t always included in traditional resumes or cover letters, like languages, community service, or references. 


is the average number of resumes that an open job will receive.

Source: Glassdoor

Types of resume parsers

When it comes to resume parsing, there are three types of approaches that you can use, all with varying levels of accuracy and features.

Keyword-based parsing

A keyword-based resume parser will identify words, patterns, and phrases in the text of the resume or cover letter. It’ll use its own algorithm to find text around those words to read it correctly. This type is the simplest, but also the least accurate type of parser. 

In terms of accuracy, it’s likely you won’t have higher than a 70% accuracy rate because it’s not able to extra information or data that isn’t surrounded by a specific keyword. If you’re dealing with an ambiguous keyword, like “writer,” they can guess incorrectly as it interprets it.  

An example of how a keyword parser works would be scanning for something like a zip code and assuming the surrounding words are an address. Or it would scan for a date range and assume the text around it is an employment timeline. 

Grammar-based parsing

When understanding the context of a resume or cover letter, a grammar-based parsing tool will use a large number of grammatical rules. They will combine specific words and phrases together to make complex structures as a way to capture the exact meaning of every sentence within a resume or cover letter. 

With a grammar-based parsing tool, it’s possible to achieve up to 90% accuracy. However, they need a good amount of manual encoding by a skilled language engineer to get right. They’re generally more complicated than keyword parsers, while also being able to capture more detail. They also can easily distinguish between different meanings of words and phrases to better understand the context of the resume. 

Statistical parsing

A statistical parser will apply numerical models of text to identify the structure of a resume or cover letter. Similar to a grammar-based parser, these work by distinguishing between contexts of the same word or phrase as a way to capture specific elements, like an address or a timeline.

In terms of accuracy, these are better than a keyword parser but not as accurate as a grammar parser.

Benefits of using resume parsing

No matter if you’re a recruiter or hiring manager, as you parse resumes and cover letters, you’ll find that there are some clear and straightforward advantages to doing so. 

  • Saves time: By identifying and organizing applications with relevant skills and information, and eliminating those without, resume parsing can save hiring managers hours spent manually reading through each resume and cover letter that comes across their desk.
  • Multiple formats: Most parsers accept cover letters and resumes in a variety of forms, meaning you won’t have to turn anything away. Most accept documents in formats like PDF, TXT, DOC, and DOCX.  
  • Smarter hiring: Using a resume parser increases the likelihood of finding various qualified candidates that match the job descriptions of open positions at your company. Because of this, chances are you’ll hire the applicant best fit for the role based on their unique candidate experience.
  • Integration with ATS: Since most resume parsers come integrated with Applicant Tracking System solutions, you’ll be able to access everything you need about a candidate in one place.
  • Eliminate bias: Because you can customize a resume parser to omit specific information, you can eliminate unintentional biases when looking at a resume or cover letter. For instance, you can disable fields like age, gender, school or university name, a candidate image or headshot, and their date of birth.
  • Social media parsing: With improvements in technology, it’s possible for resume parsers and ATS software to also parse a candidate’s social media page, like their LinkedIn page, into a usable format. 

Challenges of using resume parsing

Even with all of the advantages of using a resume parser on all of the job applications your company receives, there are some downsides that you’ll want to keep in mind, too.

  • Differences in language: Interpreting language can be complicated, especially when you consider how many different ways it is to write the same thing. For instance, there is more than one way to indicate a date range or a specific job title. The software you use needs to be able to understand these nuances.
  • Potential to overlook a candidate: When using a resume parser, it’s possible that you could miss an extremely qualified candidate. While most applicants will have excellent and well-written resumes and cover letters, some may be missing something and the perfect candidate could fall through the cracks.
  • Cost: Depending on the resume parser you choose to go with, your company could pay between $50-$200 a month. 
  • Possible keyword stuffing: Resume parsers can sometimes be open to manipulation by certain candidates who can “play the system.” If a candidate were to stuff their resume with the “right” keywords they could make themselves appear to be the better fit for the job.

How to choose a resume parser

If you’ve decided you’d like to use a resume parser at your company, there are specific characteristics and features you should look out for when selecting.

For starters, make sure it can parse resumes and cover letters in all of the popular formats, like HTML, DOC, DOCX, and PDF, so you don’t miss out on a potential qualified candidate's information. Like the popular formats, it’s in your best interest to use a parser that supports parsing in multiple languages.

The most advanced resume parsers on the market utilize artificial intelligence, deep learning, and machine learning to create a unique algorithm for improved data extraction and smarter identification of this data for better search results as you make your way through candidate applications. 

As previously stated, you’ll want to be sure that your resume parsing solution can easily integrate with other existing software applications within your tech stack, like your ATS.

Not only will this help recruiters and HR managers alike in reducing the time spent reading resumes and screening potential candidates, but also storing application information in one unified location. It then will create an executive summary for each candidate, making it easier for you to evaluate their skills and see if they fit the job description.

The proof is in the parser

The right candidate is out there, and a resume parsing tool can help you find them faster. No matter what data is being parsed or the type of job that needs to be filled, the sense of satisfaction of finding the best resume and the most qualified application is just around the corner.

Once a seemingly perfect resume catches your eye, consider implementing pre-employment testing as a part of the interview process.

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