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Using CV parsing tools: pros and cons

What do recruiters do? Those who know little about the profession might say «hire people.» But if you dig deeper, you will realize that recruiters handle 25 to 30 tasks daily. Such a big workload leads to a constant need to work faster. The result? The shorter attention span for individual tasks. Maybe that is why recruiters spend only 5-7 seconds per resume. Yes, hiring professionals may have an eye for detail. Still, there is a chance some information may be overlooked at that speed, and a great candidate may be neglected. How can you avoid such mishaps without burdening HR professionals? The answer lies in resume parsing tools.

This article discusses CV parsing technology, how it works, and what to search for in such tools.

The real resume parsing tools benefits

First thing first, what is resume parsing? It is an analysis of CVs with the help of special programs to extract applicants’ data. Using collected data points, recruiters can organize and filter them to create special reports. The technology quickly spots the strongest applicants and thus shortens the hiring cycle and reduces related costs.

«A resume parser is a technology that uses Natural Language Processing (NLP) to ‘read’ and convert the text of a resume to language a computer can understand. Resume parsers automatically extract and analyze resume data so the information can be categorized, coded, sorted, and searched over by the recruiter.»

As Christine Watson, Marketing Director at DaXtra Technologies

But why do recruiters incorporate resume parsing tools into their workflow? Here are some of the key benefits:

Time optimization

HR specialists receive many CVs. Unfortunately, not all of them are relevant. Resume parsing saves recruiter time by automatically reviewing the applicants’ key information. This technological tool shortlists only those with the required experiences and skills.

Human error elimination

AI recruitment solutions use a parsing algorithm to quickly scan through every data point in the application and match it to the requirements. This ensures that all worthy candidates are shortlisted, no matter how swamped with work the HR manager is. It also eliminates bias by leaving information such as age, gender, and country of origin out of the resume analysis.


Naturally, hiring managers are greatly involved in the recruitment process. With resume parsing, they always have access to reports on applicants. It helps them to select the most promising ones for the interviews. In turn, recruiters spend less time communicating screening results and receive feedback about candidates much faster.

Social media data

Advanced parsing tools allow HR managers to scan information from the social media profiles of applicants. The parsed data from LinkedIn and other professional networks is easier to analyze. Thus, you get a more detailed profile of each candidate and can make better informed hiring decisions.

Parsing data from different parts of resume

Now that we’ve seen that resume parsing allows companies to speed up recruitment workflows and enhance their quality, let’s look at how to parse resume.

To do so, you simply need to manually or automatically upload received CVs into the special software. Then, set the desired criteria to filter the application. Using the Python Scrapy framework, the tool scans the key parts of CVs. Then, it shortlists the candidates that meet the position requirements.

When setting the criteria, you can define mandatory requirements (need to have) and optional ones (nice to have).

Here are the main parts of resumes that AI automation tools parse information from:

  • Contact information to know how to keep in touch with the candidate;
  • Summary – a snippet telling who the applicant is and what they are looking for;
  • Employment history – information about the companies the applicant worked with, their tasks, and achievements;
  • Education to understand what the candidate majored in and how it is relevant to the open position;
  • Skills, both hard ones (the tools they work with, the knowledge of programming languages, etc.) and soft (communication, team-work, problem-solving, and others);
  • References section to see who provided the feedback and what the references think of their work experience with the applicant.

The principles of data storage and visualization

So you have parsed the resumes. What happens next? An HR automation tool stores collected data so that you can use it later. Let’s take a look at a parser example to see how exactly it is done.

The golden standard is to store all data in the cloud. In most cases, even a free resume parser does it. The main benefit of cloud data storage is accessibility, as any accredited employee can access data from their computer no matter where they are. This has proven to be especially useful in the age of the remote work environment.

Security is another advantage of storing data in the cloud. First, only people with pre-approved access can view candidate information. Two-factor identification is highly recommended. Second, cloud servers can reject access from unauthorized computers. And finally, malware detection protects data from malicious applications.

How do you view your data? You can create reports with special focuses. For example, you can arrange the data according to the skills, designation, projects, and domains. Also, you can integrate received information with your preferred CRM. Simply add applicant data to their records right in the recruitment pipeline.

What to look for in resume parsing software

More and more recruiters and HR managers include HR automation software in their toolset. Many of these solutions offer resume parsing. Such a variety of options begs the question: What should you focus on when choosing such software? We’ve got the answer for you:

Check the supported formats

CVs come in different shapes, sizes, and formats. You must not exclude a candidate from your application pool simply because of the resume format.

According to Caitlin Proctor, Career Expert at ZipJob, «Close to 75% of resumes are rejected after being parsed, and many employers miss out on qualified candidates who don’t have knowledge of Applicant Tracking System.»

Great software will parse resumes not only in TXT, DOC, DOCX, and PDF formats but also in HTML and RTF. Even more, a truly powerful tool allows you to render data from scanned CVs by processing PNG and JPEG files.

Focus on updates

Most of the resume parsing tools create candidate profiles. But how many can update them? The difference between a good professional and a great one is that the latter constantly grows. And if they are motivated to work with you, they will definitely send their CVs repeatedly. Ideally, their recent CVs will include more experience, projects, and skills. Make sure your HR automation software can recognize such improvements and update existing profiles.

Look beyond keywords

It is important to provide the keywords for the resume parser to understand what you are looking for.

However, if keywords are isolated, they can create confusion. Make sure your parsing software takes a step forward and looks into complete phrases rather than merely keywords. For example, you look for professionals who worked in one of the Big Four accounting firms. To do so, you can list the names of the companies as «Nice to have.» The problem here is that you will get a list of different professionals employed by these organizations regardless of their skills and position. A smart tool allows you to create specific searches, such as «Accounting consultant with communication skills at Deloitte.»

Don’t settle for slowness

You use resume parsing to make your hiring faster and more effective. Thus, it’s best to avoid working with slow tools. The top-performing software is powered by machine learning algorithms for maximum speed. As a result, they analyze CVs in bulk in up to 3 seconds, depending on the volume and number of criteria.

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