CONTENTANALYST_WhitePaper_CandidateMatchingTalentManagement_150416

2015 WHITE PAPER

CANDIDATE MATCHING IN TALENT MANAGEMENT: AN ENTIRELY NEW APPROACH

Steven TooleVice 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.

IntroductionOver 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 ManagementSince 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 ManagerTo 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 knowledgeTrack 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 proficiencyMicrosoft 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 ManagementAt 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.

ConclusionLSI-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 [email protected] or call 1-888-349-9442.

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