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In-Depth Guide Into Applicant Tracking Systems (ATS) in 2024

In-Depth Guide Into Applicant Tracking Systems (ATS) in 2024In-Depth Guide Into Applicant Tracking Systems (ATS) in 2024

Applicant tracking systems act like CRM systems for managing candidate prioritization and communication. With advances in machine learning, ATS can intelligently filter applicants and manage communication with them via conversational AI. AI-powered applicant tracking software enables businesses to improve the recruitment process. Get your ATS-related questions answered:

What is an Applicant Tracking System (ATS)?

Applicant Tracking System (ATS) software provides recruiting tools that help recruiters find qualified candidates by filtering, organizing, and streamlining job applications. Eliminating unqualified candidates when resumes arrive is the main purpose of ATS so that recruiters spend their time on candidates who are more likely to fit the position.

ATS could be defined as:

An applicant tracking system (ATS) is a software application that enables the electronic handling of recruitment and hiring needs. An ATS is very similar to customer relationship management (CRM) systems, but are designed for recruitment tracking purposes. In many cases, they filter applications automatically based on given criteria such as keywords, skills, former employers, years of experience and schools attended.

What are the important capabilities of an ATS?

Applicant tracking systems automate multiple HR processes including:

Job Posting

ATS enables recruiters to post an open position to various free and paid job boards, such as the company’s social media accounts and referee networks, job portals, corporate employment sites, social media pages, slack groups for common interest groups, etc. Organizations can reach out to a wide range of applicants through these channels.

Resume screening

Applicant Tracking System (ATS) software helps resume screening. Resume screening is the process of matching candidates with job requirements based on qualifications in candidates’ resume. Qualifications that recruiters focus on are work experience, education, skills and knowledge, personality traits and competencies. Depending on qualifications, ATS can automatically filter applicants. For the remaining applications, recruiters can decide whether to call the candidate for the next interview or to reject the application.

Pre-interview testing

Resumes include limited data about applicants. Some companies rely on other methods to assess candidates further before committing the team’s time for interviews:

  • Qualitative and quantitative tests
  • Automated interviews with questions approved by the HR team. ATS software can also provide sample interview questions and a scoring system that ranks candidates automatically based on their responses.

ATS can support running such processes with ease.

Interview Planning

Arranging for a suitable time that works for each team member and the candidate can be challenging. ATS software is connected to recruiters’ Outlook or Gmail calendar and allows recruiters to arrange appointments with both candidates and team members without a constant exchange of emails or phone calls.

Interview Evaluation

The norm for hiring discussions tends to be haphazard discussions after interviews. This can be structured better with ATS by providing interviewers with structured feedback forms.

Why is it important now?

Companies are pushing for leaner and faster processes, and the same applies to the recruiting process as well. Therefore applicant tracking systems have already engaged the attention of businesses, 90% of Fortune 500 Companies are using ATS software to bring the best talent to their companies.

Time is of the essence in recruiting. Due to the competitiveness in the talent market, highly demanded individuals only stay on the market for a limited time. One of the vendors in the space estimates this time to be 10 days, but of course, this duration depends on the type of position, etc.

A corporate open position posting receives 250 applications on average and 88% of these resumes are unqualified. This high volume of resumes requires a high volume of resources and time. According to Gartner, recruiters spend one-quarter of their time with resume screening.

Along with the volume of applications, human bias is also another reason why organizations should prefer ATS for hiring. Recruiters may have an unconscious bias against a certain race, gender, etc. When a recruiter doesn’t scan all resumes in the same manner, qualified applicants may get rejected. With an applicant tracking system, recruiters can spend less time on resume screening and have a better chance of removing bias from their decisions.

How did ATS traditionally screen resumes?

ATS aims to extract stated qualifications in the resume and compare them against minimum qualifications submitted by the hiring manager at the start of hire. ATS can also run keyword-based filtering. These are error-prone approaches that are being complemented by machine learning as we explain below.

During the hiring process, ATS enables recruiters to submit feedback and notes about a candidate. These notes are used to filter applications later in the process.

What are the challenges of ATS?

Application filtering is one of the most challenging aspects of an ATS since it is a hard problem. Even two HR professionals looking at the same CV can have different opinions about whether that CV fits the job description or not. Application filtering challenges include:

  • False Negatives: Too strict rejection criteria will lead candidates who would otherwise be hired to be rejected.
  • False Positives: Similarly, setting too lax criteria will leave the HR team with too many unqualified candidate CVs to review.
  • Keyword-based application filtering systems can be easily tricked: Candidates who know about ATS settings can include specific keywords while preparing their resumes.  This can lead to recruiters calling unqualified candidates to interviews. Similarly, keyword-based tests can reject qualified candidates

How does the rise of machine learning impact ATS?

Conversational AI interfaces can make candidate communication more fluid.

Machine learning models can be trained on accepted candidates, their job success, and the data they provided in their application. Such models can help filter applications much more effectively than simplistic keyword-based approaches.

 Besides candidate filtering, AI-powered applicant tracking software provides candidate ranking features that enable businesses to identify the most relevant candidates.

According to John Bersin, only 47% of companies have HR software that is less than seven years old. Advances in machine learning can encourage these companies to update/upgrade their software to increase productivity and speed up the recruiting process.

We’ve written about how AI augments recruiting. For more information about AI-powered resume screening, feel free to check out our article.

What are the benefits of using AI-powered resume screening software?

  • Reduced time-to-hire: According to vendors in the space, The average time to fill a position is more than 40 days. Resume screening software can reduce this time by eliminating the manual screening time of the HR team.
  • Better candidate experience: As a result of reduced time-to-hire,  recruiters get back to qualified candidates faster.
  • Employees that better match the roles they are hired for: AI-powered resume screening software can reduce false negatives and false positives.
  • Cost-saving: Organizations don’t need to spend their resources on resume screening or other automatable parts of recruiting thanks to ATS.

What are the leading ATS software?

  • Newton
  • Ideal
  • RecruiterBox
  • AirCTO
  • Freshteam
  • Big Biller
  • Hiring Room

If you want to learn more about HR processes, we’ve written about a few recruiting solutions:

On-demand Recruiting: First step to modernizing HR

Recruiting AI: Guide to augmenting the hiring team

If you still have questions about applicant tracking systems, we would like to help:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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