CONTENTANALYST_WhitePaper_CandidateMatchingTalentManagement_150416

2015 WHITE PAPER
CANDIDATE MATCHING IN
TALENT MANAGEMENT:
AN ENTIRELY NEW APPROACH
Steven Toole
Vice President, Marketing, Content Analyst Company

1© 2015 Content Analyst, LLC. All rights reserved. Content Analyst, CAAT and the Content Analyst and CAAT logos are registered trademarks of Content Analyst, LLC
in the United States. All other marks are the property of their respective owners.
Introduction
Over the past two decades, hundreds of millions of dollars
have been invested into software products to help improve
efficiency and effectiveness in the online job market.
Various approaches have been attempted with varying
degrees of success and failure.
This paper outlines several of these approaches, asserts
why they did or did not work, and compares a very
different approach borrowed from the US Intelligence
Community.
Challenges of Matching in Talent Management
Since the dawn of online recruiting during the dot com bubble
of the late 90’s, job seekers and employers have struggled to
find each other online. Although hundreds of millions of dollars
have been invested in various technologies to improve the
process, the results have not improved. In fact, they've gotten
worse. According to a recent DICE-DFH Vacancy Measure report,
the time-to-fill is the highest it’s been over the course of the
13 years the study has been conducted, at nearly 25 days. With
hundreds of millions spent making it easier for job seekers and
employers to find each other, why is it harder now than ever?
Job titles are still ambiguous and tell very little about the job
or a candidate’s actual experience. Yet the primary search tool
on every major job board is the job title search. In fact, it’s even
harder with companies and individuals getting cute with job
titles such as “Chief People Officer,” “Marketing Ninja,” “Lead
Guru,” and similar unconventional job titles. Just the same,
a single job title can be used across hundreds of completely
different job descriptions, and the same exact job description
can have hundreds of different job titles. But yet the industry
still uses the job title as a primary search mechanism.

Boolean Strings Attached
To help narrow down search results and provide more relevant
matches, an entire community of Boolean search recruiters
has emerged. One LinkedIn group called Boolean Strings –
The Internet Sourcing Community has more than 28 thousand
members. By comparison, that’s more than three times
larger than the membership of the American Nuclear Society
group on LinkedIn. In other words, there are more recruiters
worried about writing better Boolean search strings than
there are nuclear scientists in this country. A recent post in the
Boolean Strings group sums up the level of complexity and yet
potentially limited effectiveness of this approach:
Notice how the recruiter uses
“President’s Club” as one of his search
criteria. But not every company uses
the term “President’s Club” to define
their top performers. Many companies
don’t even have a President’s Club.
Structured Filters
Structured filters can help narrow down search results. These
filters typically include values such as geography (zip code),
years of experience, salary range, company name, etc. In the
mid-2000’s, a job site startup called Jobfox attempted to apply
structure to all of the skills from a seeker’s resume, enabling a
complex matching algorithm to work better than crude filters
and job title ambiguities. The challenges with that approach
were twofold: 1) expecting a job seeker to spend 45 minutes to
an hour or more answering an exhaustive series of cascading,
multiple-choice questions regarding his or her skills and
experience; 2) expecting employers to spend the same amount
of time for each job, effectively answering the same questions;
and 3) building a taxonomy that captures every name for
every skill in every profession. In the face of these and other
challenges, Jobfox ceased operation.

Cost of Failure to Effectively Match Jobs and
Candidates on Job Boards
Relevance is king. The best Boolean search string is written with
one goal: find the best matches. The burden is on the recruiter
to think of as many related terms as possible to include in the
search string. Boolean search works fine when the person
performing the search knows exactly what he or she is looking
for, and nothing outside the scope of the search string. The
recruiter (or seeker) needs to know what terms to look for in
the first place. The engine may have been previously loaded up
with synonyms, but the user has no real way of knowing what
synonyms, abbreviations, acronyms and misspellings, if any,
have been defined for the engine. Since the talent management
industry has characteristically large numbers of synonyms and
evolving terms for job titles, skills, and experience, Boolean
based approaches can have a high failure/frustration rate for
users. In addition, the requirements for a job typically span more
than just one skill, which only makes the Boolean string more
complex. The result can yield many false positives (results that
are included but shouldn’t be), as well as many false negatives
(results not included but should be).
Structured filters are also designed to narrow down the results
to those that are most relevant. Structured filters can only go as
far as the programming allows, typically broad general filters
such as location, profession, years of experience, possibly a
salary range and education. Without relevant results, users
move on. In fact, according to a recent study by Chitika, 95% of
all web traffic comes through page 1 search results. Web users
are accustomed to look on the first page of search results and
rarely beyond that. Seekers who don’t find relevant jobs on
page one of a job board are just as likely to leave in the absence
of relevant results. Employers looking for relevant candidates
are also less likely to keep looking past page one of any search
results list. Therefore, the cost of not providing relevant results
on page 1 is lost site traffic, lost user confidence, and ultimately,
lost revenue.
Job boards spend millions each month to drive seekers to their
websites. Try doing a Google search using any job title and the
word “jobs” at the end, such as “sales jobs.” Notice all of the paid
ads across the top and down the right side of the page. As of
this writing, the cost per click for the keyword, “life insurance
sales jobs” was $15.80 per click. If the job boards already have
all of the candidates for life insurance sales jobs, why are they
willing to spend nearly $16 just to have new ones click on their
ads?
If job boards had more repeat visitors, they wouldn’t need to
spend as much to drive new visitors to their sites. What drives
visitors away (and onto the next job board) is lack of relevant
jobs within the first minute of the site visit. If job boards are
lucky, they’ll get the seeker’s email address in
order to email more jobs to the seeker in an
attempt to get them to return.
Cost of Failure to Effectively Match
Jobs and Candidates in an ATS and
CRM
Unfortunately, applicant tracking systems were not
designed with sophisticated search capabilities
any more than job boards: typically Boolean
search capabilities and some structured filters
for values such as location for recruiters to apply
against their pools of hundreds of thousands to a
million or more past applicants and candidates.
According to a recent review on ERE, there are still
ATS products with poor search capabilities. This
can lead employers to spend more advertising
jobs and searching outside their own internal
pools of candidates, reducing the value of the ATS
investment. Conversely, the ATS with the best
search/matching capabilities can drive value
by saving employers time and money searching
outside internal candidate pools.

Matching Technologies Defined
Various matching technologies have been attempted within the
talent management community. It’s important to understand
how the various approaches are very different, and have very
different implications. This should help clear things up.
NLP – or Natural Language Processing. The term “NLP” in
computer science circles will include some machine learning
approaches, but for most people, NLP approaches are wordlist-
and rules-based approaches leveraging strong linguistic rules
and synonym lists created and maintain by humans. In this
type of matching, the system learns by being fed lists of similar
terms. In essence, the system really isn’t learning anything, but
rather, it’s being pre-programmed with terms and synonyms
to look for when analyzing a resume or job rec. To be effective
with this approach, a team of linguists must come up with every
term for every skill, including every misspelling, abbreviation,
acronym and possibly even slang terms that define a particular
skill or experience. The challenge is that this is a tremendous
amount of manual work, and needs to be constantly maintained.
On top of that, the same process has to be done for every
language being used. No small task.
Parsing – Also used to describe a type of machine learning
approach in talent management. Parsing is an effective way
to pull key elements from a resume such as the candidate’s
contact details (name, email address, phone number, previous
job titles and employers) and put them into structured data
fields, so that the recruiter can filter based on those attributes.
Sophisticated parsers also look for skills, also using – you
guessed it – NLP, or Natural Language Processing (see previous
paragraph).
LSI-based Machine Learning—LSI, or Latent Semantic Indexing,
is a type of machine learning technology that learns from the
content itself. In other words, no human needs to feed it a list of
synonyms, abbreviations, acronyms, or misspellings. With LSI,
the machine reads the resumes and formulates what’s called a
term space. The term space identifies the relationships between
every term in every resume in the collection of resumes,
whether 10,000, 100,000, 1 million or 100 million resumes and
job descriptions.
The term space is like the human brain. Every term in our brains
has some context, which we learned by hearing each term in
a certain context. So like the human brain, the engine maps
how terms are used interchangeably, and concludes that the
terms are related, or synonymous, with one another – including
misspellings, abbreviations, acronyms, etc. Once it creates
the term space, it organizes the terms into concepts of related
terms. The term “driver” by itself has many meanings, but in
the context of other terms such as business, software, and
truck, the term “driver” takes on very different meanings (e.g.,
truck driver, business driver and software driver). All the other
terms used in each of those contexts only reinforce the unique
meaning. E.g., a truck driver will have other terms on his or her
resume that provide additional context to driving a truck, that
aren’t typically used with software drivers and business drivers.
For example, “tractor,” “pickup,” “diesel-powered,” “flatbed,”
“pallets,” etc.
With resumes and job descriptions, skills and requirements
are represented as concepts. There are many different ways to
express a concept. So using a word search, Boolean keyword,
or even NLP search is limited to just one way to express that
concept. Like humans do, using LSI-based machine learning
allows for the concept to be expressed and understood without
limiting the results to just one expression of the concept.
Machine Learning Defined
Ability of a machine to improve its own
performance through the use of a software that
employs artificial intelligence techniques to mimic
the ways by which humans seem to learn, such as
repetition and experience.

Example – Marketing Manager
To demonstrate this type of matching, we’ll use a job description
for a marketing manager job in Los Angeles. This is an actual
job found on a job board. We could take the entire job description
for this example, but in the interest of space, we’ll just take the
bulleted requirements. This demonstration uses a repository of
96,000 resumes spanning about 10 fairly standard professions,
such as accounting, human resources, engineering, customer
services, administrative, sales, legal and marketing. No other
criteria were used to build this resume collection, other than
the one or two word profession names mentioned above. We
will demonstrate how quickly and easily the LSI-based machine
learning system can find the most relevant, matching candidate
out of the 96,000.
It’s also important to note here that the LSI-based machine
learning system knew nothing about anything beforehand – in
other words, it was not trained on any terms before ingesting
the resumes. No dictionaries were created, no word libraries,
thesauri, or any sort of manual training. The system learned
from the 96,000 resumes exclusively. It learned the context
of every term from the content itself (the resumes). This was
a process that was completed in a matter of hours, not days,
weeks or months.
Step 1 – We select the job description text and place it into the
query box of an LSI-based machine learning engine, powered by
CAAT by Content Analyst Company.
Marketing Manager
The Marketing Manager is responsible for the management
of marketing, communications, and promotional activities to
effectively enhance the position and image of the company
through various sales and business development goals and
objectives.
Qualifications Required:
• A minimum of 2-4 years of experience in a related role
• Associate’s Degree in related business field
• MS Dynamics or similar CRM knowledge
• Adobe InDesign skills
• MS Office proficiency
Qualifications Preferred:
• Bachelor’s Degree in Marketing, Communications or related
business field
• Kentico or comparable CMS familiarity
Functions and Responsibilities:
Sales and Marketing Communications
• Oversee the content and production of collateral materials
including but not limited to case studies, testimonials, sales kits,
advertising and direct mail pieces
• Create and implement sales messaging in various vehicles
including sales campaigns, correspondence and other forms of
communication
• Oversee the content, schedule, production and distribution
of quarterly corporate client newsletter and staff newsletter
(InDesign)
• Oversee website content updates utilizing CMS (Kentico)
JOB DESCRIPTION

Step 2 – We include a filter using one of the structured values
to make sure the matches are in the correct geography. In this
case, we select the Los Angeles area.
Step 3 – The results are interesting – the top 3 candidates have very different job titles
but interestingly similar skills and experience.

Looking more closely at the top candidate, we see areas highlighted in blue, indicating a
high degree of conceptual relevance (match) to the original job description:
For simplicity, we’ve mapped the original job requirements
to the verbatim text from the top candidate’s resume. Key
terms in the job requirements are highlighted in yellow, and
the key terms representing the candidate’s matching skills are
highlighted in green.
Some are obvious and probably would have been found in a
Boolean search, such as CRM. Others are not so obvious, looked
at individually:
Adobe InDesign – Notice how the candidate has “Adobe CS6 (In-
Design, Illustrator, Photoshop).” CS6 is Adobe’s suite of graphic
design products. Also note that the candidate hyphenated “In-
Design” but the job description did not. A typical Boolean search
might not have included this candidate. But the LSI-based
matching system understood that these were
comparable terms.
MS Office – Again, the candidate expressed her skill differently.
She spelled out “Microsoft Office” but the original job description
did not. The LSI-based machine learning system identified these
as comparable terms.
Kentico or Comparable CMS familiarity – the candidate has
Wordpress on her resume. Wordpress is one of the world’s most
popular CMS systems. The LSI-based machine learning system
also identified these as comparable terms.
Studying the following table reveals several additional
examples. “Collateral” on the job description, but “product
data sheets” on the resume. “Sales messaging” on the JD, but
“marketing materials” on the resume.

Job Requirements (verbatim job
description)
#1 Candidate Qualifications (verbatim
resume text)
MS Dynamics or similar CRM knowledge
Track leads and market growth through CRM system
Salesforce CRM Database: Cosential, COINS, Salesforce
(Data.com),
Adobe InDesign skills Adobe CS6 (In-Design, Illustrator, Photoshop)
MS Office proficiency
Microsoft Office (Word, PowerPoint, Excel, Visio, Publisher,
Outlook)
Kentico or comparable CMS familiarity
WordPress
* Website: develop company website including site map,
navigation, content, and photography.
Oversee the content and production of collateral materials
including but not limited to case studies, testimonials,
sales kits, advertising and direct mail pieces
* Generate awareness for new and existing products
including promotional videos, product data sheets, blog
posts, print campaigns, email blasts, and lunch and learns
Create and implement sales messaging in various vehicles
including sales campaigns, correspondence and other
forms of communication
Work closely with Sales team to define marketing
materials and programs. Offer support for quarterly sales
goals through development of appropriate tools, materials
and presentations
Oversee the content, schedule, production and distribution
of quarterly corporate client newsletter and staff
newsletter (InDesign)
Communications: Newsletter, email blasts, website,
maintain internal intranet
Develop and manage the planning and execution of events
such as roundtables, seminars, and webinars
Manage events including ground breaking ceremonies,
project completions, client cocktail hour/dinner receptions,
open house, charity

While these examples are impressive when looked at individually, it’s important to
remember how we got here – not through some complex Boolean search string, but
by simply pasting the job description into the query window. It’s the aggregate match
between all eight of these requirements and the skills in the resume, despite the use
of different terminology, that really makes the LSI-based machine learning impressive.
On top of that, recall that the machine “learned” all these similar terms from the
content itself – the 96,000 resumes fed into the system as its basis to learn all of the
terms (skills) and identify the similar uses (such as Kentico vs. Wordpress). No human
needed to “tell” the system, “these are all the different CMS systems,” or “MS Office and
Microsoft Office are the same thing,” or “collateral and data sheets are synonyms.” The
machine learned from the references to these skills based on the context of how they
were used across the 96,000 resumes.
Use Cases for LSI-Based Machine Learning in Matching for
Talent Management
At this point, the intrigued reader may be thinking, “Interesting technology, but how
would I actually use it?” There are many use cases for this technology in talent
management. Content Analyst Company has developed the world’s only LSI-based
machine learning system used for matching in talent management. CAAT® is the
company’s OEM offering, licensed directly to job boards, ATS and CRM product
companies for seamless integration into their products. Here are some of the more
common uses for each.
1. Job Board Integration of CAAT – CAAT is the software developer’s kit (SDK) offering
of Content Analyst’s LSI-powered machine learning engine. Software companies
and websites in talent management and many other verticals integrate CAAT
seamlessly into their products to enable matching capabilities far beyond Boolean
keyword and structured filtering. Here are several use cases for CAAT within job
boards and aggregators.
»» Match Similar Jobs for Seeker – CAAT can use the contents of a job to find more
jobs that are similar. Rather than finding more jobs with the same job title, CAAT
looks at the requirements of the job or jobs that the seeker likes, and matches
them with the most relevant, similar jobs (regardless of the specific job title).
This helps improve seeker satisfaction, page turns, click throughs, and return
rates. CAAT can automatically recommend similar jobs as the seeker’s normal
navigational path. As the seeker clicks around to jobs, CAAT is identifying more
jobs to the seeker that are similar to the ones he or she is clicking. CAAT can
also be used to identify similar jobs to email to the seeker, if the email address
is captured, thus increasing the return rate to the site and reducing acquisition
costs for the job board.
»» Match Jobs from Seeker’s Resume – For job boards and aggregators that
collect resumes, CAAT can use the seeker’s resume contents to match the most
relevant jobs based on the seeker’s skills and experience. This can be done
automatically, the instant the seeker uploads his or her resume to the site. E.g.,
“Thanks for your resume. Here are some jobs that may be a match for your skills
and experience.” Of course, the site can also continue to email new job match
alerts to the seeker based on matches CAAT identifies from the resume contents.

»» Match Resumes from Job Description for Employer – The flip side of the above
scenarios are on the employer side. Employer posts a job to the job board,
CAAT reads the job requirements and instantly identifies matching resumes. No
change in employer workflow. Or if the employer doesn’t want to post the job, the
employer can use the job requirements as the search query against the resume
database. As with seekers, CAAT can also identify new matches to alert the
employer.
»» Match Resumes Using Ideal Resumes for Employer – Employers can use the
resume or resumes of star performers as the search query. CAAT will “find
more like this” and identify matching resumes that are most similar to the
star performer’s – in other words, candidates that have similar backgrounds,
skills and experience, indicating that the matching resumes may also be good
candidates for the job.
2. ATS and CRM Integration of CAAT – Applicant Tracking Systems and Candidate
Resource Management systems offer similar use cases to the job boards above.
Those scenarios are as follows:
»» Match Similar Jobs for Seeker – The main difference between this scenario
and the one above is that in the case of the ATS, the seeker is on the employer’s
careers page. Once on the employer’s careers page, the seeker can either
browse jobs and CAAT can find similar jobs based on the contents of the jobs
browsed, or the seeker can upload his or her resume and let CAAT match jobs at
the company based on the seeker’s skills and experience. New job matches can
be emailed to the seeker as they are posted and identified as matches by CAAT.
»» Employer Uses Job Description to Match Past Applicants – To augment an ATS
product’s current keyword search and filtering capabilities, integrating CAAT into
the ATS enables the employer to use the job requirements as the match criteria.
CAAT will find the best candidates out of the company’s candidate pool based
on matching skills and experience. Naturally, CAAT can also identify matching
candidates automatically as the employer posts the job to the ATS.
Conclusion
LSI-based machine learning technology has been proven incredibly effective in highly
sensitive environments such as the US Intelligence Community, and in electronic
discovery for legal matters in all 50 states throughout the US. The culmination of many
factors such as cloud computing, the relative speed, power and low cost of memory and
computing power have all contributed to the recent rise in interest among nearly every
major job board, aggregator, ATS and CRM. Increased pressure from social recruiting
has provided additional incentive for traditional job boards and aggregators to seek new
and innovative technologies to improve results for both seekers and employers.
LSI-based machine learning technology swept through the electronic discovery market
in 2007, giving early adopters a huge leap on their competitors. Today, LSI-based
machine learning technology has become the standard across nearly every electronic
discovery software platform. Talent management may be the next market to experience
such sweeping adoption of the breakthrough technology the industry has struggled to
obtain for the past 10 years or more.

© 2015 Content Analyst Company, LLC. All rights reserved. Content Analyst, CAAT, the Content Analyst and CAAT logos, and Cerebrant are registered trademarks or trademarks of
Content Analyst Company, LLC in the United States. All other marks are the property of their respective owners.
About Content Analyst Company
We provide powerful and proven Advanced Analytics that exponentially reduce the time
needed to discern relevant information from unstructured content. CAAT, our dynamic suite
of text analytics technologies, delivers significant value wherever knowledge workers need
to extract insights from large amounts of unstructured content.
For more information visit www.contentanalyst.com, email info@contentanalyst.com or call 1-888-349-9442.

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