A growing
number of jobseekers are aware of the Applicant
Tracking Systems, often referred to by its acronym ATS. The academic
community has used the automated system for years, but its popularity with
mainstream employers continues to rise.
The
tracking system is not without flaws. Efforts continue to reduce imperfections—especially
by those attempting to game the software. Employers realized that applicants
were copying their advertised job descriptions into their résumés to achieve a
perfect match. Today, those maneuvers are less effective.
In
non-technical terms, here is how ATS appears to perform. The automated process involves
a five-step process, and mimics artificial intelligence (AI) behavior. The goal
is to reduce the hours required to screen myriad applicants efficiently, and
identify those presumed most qualified. The last element leans more toward alchemy.
1. You enter the website designed to accept and electronically
process applications and résumés. Applicants are asked to create an account
with a user ID and password. The system puts you through a detailed
application, and requires you to identify a specific opening. Once completed, applicants
are directed to an “attachment” section.
·
The
attachment section is where you upload your cleverly designed résumé, and
sometimes an e-cover, along with other attachments if requested.
2. Once uploaded, the ATS runs the résumé
through a parser process. To
accomplish this, the parser software literally strips away the entire layout and fancy format. What remains is plain-Jane
text, sans formatting. All the colorful lines, elaborate artwork and other
creative elements get removed.
3. The parser then sorts the data into major categories.
The common ones include:
(a) A contact information section, i.e., phone, email, zip code etcetera.
(b) Major Skillsets as they relate to the opening.
(c) Experience, with emphasis on job titles, employers and employment length.
(d) Education, including degrees, certifications and licensures.
Depending
on preprogrammed preferences, additional categories can be included when the
employer has deemed it essential. Foreign
languages, publications, research
and professional memberships can be isolated
and taken into consideration for added points.
ATS
can identify and assess your travel time, the quality of your employers,
positions held, date ranges, degree(s) and assess core experience. It can grab
your zip code, and calculate your socioeconomic status based on US Census data.
Each element is quantified by assigning the item a point value.
4. Next, the software attempts to analyze keywords. Semantics aside, this is where the IA
portion encounters difficulty. Applicants with well-concealed flaws can slip
through. (These often do not surface until background checks are conducted.) At
the opposite extreme, creative ingenuity often gets short changed as well.
·
Applicants
attempt to game the system overload their
material with buzzwords in an effort to outwit AI. Newer versions of ATS appear
to be savvy to these antics, and the AI algorism attempts to spot and flag such
behavior. If red flagged, the applicant loses points.
This
requires the AI software to perform counter-intuitive analysis. You could state,
for example, that you are a creative
genius. Will the software accept it as a statement of fact? Probably not. A list of US Patents would
carry more weight.
5. Once the analysis finishes, the system totals
the raw subsets statistics, and assigns an overall value—usually number with a
decimal. (10.0, 9.5, 8.2 etc.) The applicants are then ranked
from highest on down. The ATS can be set to select a predetermined quantity,
such as the top ten applicants.
How can you
beat such a clever system?
Broadly speaking, you can’t. ATS
performs well in eliminating blatantly unqualified applications. The system can
easily catch illogical discrepancies, as well as assess the non-relevant. Short
employment stints and glaring gaps can be tagged as red-flag items.
ATS
gets preprogrammed to look for keywords, euphemistically dubbed buzzwords. There is a difference: The
context in which words appear can shift contextual meanings. The system’s
ability to spot nuances poses an ongoing challenge. Over usage of keywords can be
counterproductive when the system is programmed to track such behavior.
While
the parser portion of the software performs efficiently, ATS appears to come up
short in analyzing and assessing creativity.
So far, that portion has been left for interviewers to assess, and this is
where the best candidates get shortchanged.
My
approach is to play to the algorithm’s strengths. To accomplish that, I use clean
layouts that allow the parser software to perform better. Thought is given to
items I know will be categorized: Those items are grouped accordingly.
I
have also discovered that excessive verbiage—especially the removal of adverbs
and qualifiers seems to improve applicant ranking. Items that can be identified
as red flag issues are removed. What remains is a concise, factual presentation
the parser portion can easily group and the AI portion more accurately assess.
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here. Copyrighted © 2015 by Robert James