- Do not use graphics or tables
- Follow the formatting rules
- Include unique keywords
Sound familiar? These are some of the tips which can help resumes get through an application tracking system (ATS) and eventually land them in front of an HR manager. However, when it comes to new AI technology used in the hiring process, these tips can no longer guarantee the resume getting past the ATS.
What is an ATS and how does it work?
Over 98% of fortune 500 companies, as well as an increasing number of small to mid-sized businesses, are using application tracking systems to filter resumes.  The ATS screens through a large number of resumes and passes the most qualified candidates on to the hiring managers. The principal at HR consulting firm Bersin by Deloitte says, “Most companies have thousands of resumes sitting in a database that they have never looked at.” Actually, 75 % of resumes get lost somewhere in the database and are never looked at by a human. 
When applicants apply for online jobs, their personal information, work experiences, skills, education and other relevant information is uploaded to the database. The ATS assists human resource personnel in managing the candidates throughout the whole hiring process, including sending applicants automated messages to let them know their applications have been received, giving online tests, scheduling interviews and sending rejection letters. 
The drawbacks of ATS
The biggest drawback of ATS is that many of the earlier systems are designed to look for specific keywords and titles in resumes that match with the advertised positions. Even though some ATS providers claim their system has AI capabilities, the search and match results are still very disappointing. This means that if a good candidate, who is switching careers, has a very similar skill set to the one required for the new position but doesn’t have the exact job title in their resume, the system would miss the candidate.
Sometimes recruiters search for candidates by combining multiple keywords, such as job titles, important skill sets and experiences. Even so, a keyword-based system is not capable of finding adequate candidates with an acceptable degree of accuracy and precision. Moreover, the majority of all searches look for terms that are common, such as “Java”, “Project Manager” or “MS Excel”. Unfortunately, this is not the right approach, for with keyword searching, the more trivial the keyword, the less effective the search and the broader the results.
Other drawbacks of an ATS are that it may not understand all abbreviations and that it can only read a certain format. According to a joint survey by CareerArc and Future Workplace, in 62% of companies using ATS, “some qualified candidates are likely being automatically filtered out of the vetting process by mistake.” 
The new technology to upgrade your current ATS
It would be pointless to discuss how to optimize resumes in order to “beat” the ATS. Instead, companies should implement the newest AI technology to optimize their application tracking system for a more efficient and accurate hiring process. JANZZ.technology offers the semantic technology which structures occupations, skills, experiences, functions and many more logically interlinked concepts, which deliver relevant search and match results to hiring managers.
With a semantic ATS, you will never miss talents simply because of wording. For example, when searching for a Chinese coach (e.g. for executive mangers who are going to China regularly to meet clients), a semantically powered system will show results including applicants whose job titles aren’t identical but related, such as, Chinese language tutor, Chinese instructor, Chinese teacher or language tutor specialized in Chinese and Japanese.
A semantic matching engine like JANZZsme! has the most comprehensive, multilingual knowledge graph of occupations and skills at its disposal. When the semantic matching engine does a query expansion, searches or matches job ads and resumes, it accesses the ontology concepts, lexical terms and synonyms, which may appear in CVs and job vacancies in up to 40 languages.
For instance, CEOs (US English) will match with Geschäftsführer (German), 首席执行官 (Chinese) and Managing Directors (UK English). Carpenters will be fully or partly matched with joiners and kitchen unit makers. Design illustrators, animation artists and film animation designers are all fully or partly connected.
Taking programing language as another example; let’s say you are looking for programmers to develop .NET. If programmer A has C# on his resume and programmer B knows Python, the smart matching engine JANZZsme! will successfully match programmer A to your open position, because it knows that C# is a programming language of .NET. This is achieved through the interlinked relationship of the concepts stored in JANZZon!.
Precision in matching is achieved through structure and context. However, neither CVs nor job offers are structured efficiently or consistently, which makes it difficult for a keyword search engine to identify the right data type. A matching engine such as JANZZsme! looks at the type of sought-for data and uses deep learning techniques to identify the correct match while disqualifying matches that are the wrong data type.
CV and job description keyword-based search systems and current CV Parsing technology do not have the same capability to produce high occurrences of accurate matches that contextualized semantic searching and matching has. While the results from a keyword-based search overwhelm hiring managers, a semantic matching engine produces a manageable volume of results, letting hiring managers focus on scanning questionable or unclear data and making the final decision. Thus, radically reducing the amount of needed time and effort.
Do you feel limited by your current ATS (e.g. Oracle Taleo, SAP or IBM Kenexa)? Do you want to optimize it with semantic technology and enjoy more advanced capabilities when searching for candidates, matching open positions and conducting skill gap analyses? To find out how to do so, please write now to firstname.lastname@example.org
 Jon Shields. 2017. Over 98% of fortune 500 companies use application tracking systems (ATS). URL: https://www.jobscan.co/blog/fortune-500-use-applicant-tracking-systems/
 Terena Bell. 2017. The secrets to beating an applicant tracking system (ATS). URL: https://www.cio.com/article/2398753/careers-staffing-5-insider-secrets-for-beating-applicant-tracking-systems.html
 Alison Doyle. 2019. How employers use application tracking systems (ATS). URL: https://www.thebalancecareers.com/what-is-an-applicant-tracking-systems-ats-2061926
 CareerArc. 2016. 23 surprising stats on candidate experience-infographic. URL: http://www.careerarc.com/blog/2016/06/candidate-experience-study-infographic/