Keyword Searches vs. AI Matching: Which Drives Results?

By Martin Matula, vice president of product development at Genesys Talent

In recent years, talent acquisition professionals have been inundated with new technologies, all of which promise to deliver qualified candidates with unprecedented efficiency and ease. Most talkedabout among these game changers is artificial intelligence (AI).

To understand just why AI has captured so much attention, one must first understand what it actually brings to the table. Let's take a look at how Genesys's purpose-built AI compares to those old recruiter standbys of resume keyword searches and Boolean search strings when it comes to finding the best-fit candidates for a job.

Words Have Meanings, Which AI Uncovers

Traditional keyword searches simply identify the presence of certain words in a candidate's resume or profile. On the other hand, Genesys's AI-powered search and match functions can identify the context of a combination of words and phrases based on the proximity of certain words to other words.

Context matters in searches. The manner in which a given set of words are strung together makes a huge difference in the overall meaning of those words.

For example, say you are looking to fill a position for a "Microsoft certified network security specialist." A basic keyword search will certainly return profiles of candidates who fit the bill. However, that search might also return a "security" guard "certified" in CPR, proficient in "Microsoft" Word, and with previous experience working in the lobby of a national cable news "network."

The security guard would never make it through the screening process. A human recruiter would quickly identify that candidate as a bad match, but that identification process is a waste of precious time.

This is where AI comes in. Latent semantic analysis — a machine-learning model for extracting and representing the contextualusage meaning of words through statistical computations applied to a large body of resumes — would also quickly determine the candidate was not a match. The difference in this case is the technology filters the bad match out before it ever gets in front of a human.

Through latent semantic analysis, Genesys's technology greatly reduces the number of false positives returned by traditional Boolean and keyword searches. In doing so, it also minimizes the resources needed to qualify candidates and saves time by eliminating the need for multiple search queries.

AI Offers an Easier Input

Finding the right talent to fill a job opening is a very results-oriented pursuit. Whether you are a recruiter, HR pro, or hiring manager, your ultimate priority is to put the right candidate in the right position.

When comparing searching and matching technologies in the context of staffing, we typically focus on the results of the search. The quality of the matched candidates is the most important thing.

However, the output itself is only part of the equation. To arrive at the desired output, one first needs to enter the right input. AI simplifies the input process, which makes iteasier to reach a high-quality output.

Context matters in searches. The manner in which words are strung together makes a huge difference in the overall meaning of those words."

Keyword searches can be a time-consuming and not very user-friendly way to achieve a desired output. In the past, the most successful recruiters had to master the art of writing extremely comprehensive search strings to uncover the perfect candidate. A typical search string might have looked something like this:

Title = "accountant" OR "CPA" OR "certified public accountant" OR ("cost accountant" OR "staff accountant" OR "financial analyst" OR "accounting manager") AND Profile = ("clerk" OR "assistant") AND ("accounts payable" OR "accounts receivable" OR "general ledger" OR "payables" OR "receivables" or "journal entry") "accounting manager" OR "division controller" OR "company controller"

But AI changes the way we ask questions, making it possible to imagine a search query as simple as, "Genesys, find me a replacement for Bob in accounting. Here is his profile."

What the AI does is use Bob's profile to identify the skills and attributes that made him successful in his role. The AI then applies these skills and attributes to finding a suitable replacement. No other input is necessary.

Compared to old-fashioned Boolean keyword search — which often requires a lot of trial and error to refine the results — AI facilitates a much more intuitive and efficient approach to constructing inputs that produce the desired outputs.

Matching People, Not Job Descriptions

When searching for talent, the goal is to find a person, not another jobdescription that matches the one you've written. That's why Genesys's matching algorithms are based on candidate profile data and resumes, not job descriptions.

Most staffing professionals agree that posted job descriptions and even job titles do not always accurately represent the person required to fill a role. There is often a disconnect between the talent acquisition specialists recruiting for the position and the hiring manager who needs the talent. Hiring managers may not have a good grasp of what they really need in a new hire, nor will they necessarily understand the realities of the current labor market. As a result, job descriptions often lack necessary details or make unrealistic demands. Similarly, job titles do not necessarily translate across employers.

Genesys considered the inherent shortcomings of job descriptions and built its AI around the concept of finding people who closely match other people who have proven they can perform the job. Rather than trying to match people to inadequate job descriptions, Genesys simply passes a resume through its search algorithms to find the profiles of candidates who closely match your ideal.

Say you like Jennifer's skill set but prefer Mary's experience. Genesys can combine that skill set and experience to return matches based on a hybrid of ideal candidates.

Genesys aspires to humanize the process of matching talented people with great jobs. Advancements in AI make it possible to achieve this goal more efficiently and effectively than traditional searching techniques.

And that is why AI has sparked the most conversation — and excitement — of all the new technologies promising to streamline recruitment.

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