When you’re searching the Internet for potential candidates, it’s quite common (and practical) to search for resumes of  people who are likely to be local to your opportunity. The two main ways of doing this are searching by area code and searching by zip code range.

While there are limitations of both approaches (not everyone includes a phone number or address), in this post I want to explore an interesting phenomenon that was brought to my attention not too long ago which clearly demonstrates that even when people DO provide a phone number or address, you may not be able to find them by searching for that information.

Yes, you read that right.

Recently I had a recruiter in my network ask me why he could find a specific resume online using Google, but when he added the area code (which was clearly on the resume) to the search string – the search result disappeared.

When he sent me the screen shots, I investigated – and sure enough, he was right.

What I find especially interesting is the fact that he found the resume at all, because he wasn’t using any location criteria in his search to try and find local people. If his initial searches tried to target local people using an area code or zip code range, he would have never found the resume in the first place.

Moreover – he would never know of it’s existence.

Click on the image below to watch a short video clip of the Google resume search phenomenon:

Fascinating and Frightening

Seeing something like this makes me wonder how many people have resumes and other information online that are indexed by search engines, but are never found due to the search criteria used.

My instincts tell me there are many – but the insidious nature of searching is that all searches “work.” In other words – you get results from your searches, which gives you a false sense of security (and perhaps accomplishment), but you’re not aware of available results that your searches simply didn’t return. You can’t be.

In fact, it never occurs to most people to even wonder about available results they have access to, but are never retrieved.

However, just because you don’t find something – does that mean it doesn’t exist?

Why Does Google Fail to “Find” the Search Result?

I have my ideas as to why that specific resume cannot be found when adding the area code to the search, but I am curious to know your thoughts.

Multiple Search Engines

Of course, you should always employ difference search engines, as they don’t all index the exact same pages/sites. And even of they did index the same pages/sites, it doesn’t necessarily mean that they would return the exact same results for any particular search.

For example, while Google, Bing and Yahoo can find the same result without the area code, only Bing and Yahoo find the result that Google fails to return when adding the area code to the search string.

Beware!

I bring this example to your attention because you should always be aware of the fact that you may have access to people that you simply cannot find using certain search criteria, and it’s not limited only to location searching. Searching by skill, title, company, industry, etc., can be similarly affected.

It’s often simply impractical to NOT search with some location criteria – few people have the time to sort through and review results from all over the world that they cannot use. And location-focused searching doesn’t just apply to resume search, but also for any kind of searching in which you are trying to find people that live relatively close to where the opportunity you are sourcing/recruiting for is located.

Hopefully this post makes you think twice about your strategy and tactics to try and identify local people and helps you uncover previously “unfindable” results.

Here is the expanded slide deck from my SourceCon 2010 Keynote: Resume Sourcing and Matching – Artificial Intelligence vs. Human Cognition. It contains all of the talking points as text so you are not left guessing as to what I spoke to during the live presentation.

You’ll learn about the intrinsic and often overlooked challenges associated with sourcing resumes (it’s deceptively complex), what artificially intelligent semantic search and match applications claim to do and how they actually work, the limits of artificial intelligence, what people can do that semantic search applications cannot, the 5 levels of semantic search,  the 5 levels of talent mining, and what I think is the ideal candidate sourcing solution.

When I run recruiter training classes, I often ask for the trainees to bring me example positions they are having trouble working on to use for live sourcing training.

During one such class (a little over 2 years ago), I had a recruiter bring me an opening for a challenging position at Google that had been open for a while. He had been working this position for a couple of weeks and had failed to produce a single candidate that Google was interested in interviewing. 

Many Had Already Tried and Failed…

As I asked him for a little background on the position, I found out it had been open for 4 months. 

That’s almost always a bad sign to a recruiter, as it had no doubt been thoroughly beaten up by countless other recruiters/vendors to Google. However, he assured me this was not a “black hole” requirement and that Google would indeed interview and hire candidates.

Now, the position this recruiter was working on was a network performance test engineer, which poses some unique searching challenges because most of the Boolean search strings that recruiters will employ will result in many false positives – resumes of candidates that contain all of the search terms, but who are not primarily responsible for the performance testing of networks. A simple game of “buzzword bingo” would not work for this position.

This was also independently verified by Google, as they commented that most of the candidates they were receiving were not appropriately qualified – most were QA/test engineers who had performance tested software and network applications, but not networks and network hardware specifically.

Give Me Four Hours to Chop Down a Tree…

When I first accepted the challenge of helping this recruiter, my initial searches did pull many false positives. However, after about 20 minutes of manipulating search strings and observing the corresponding changes in the results, I came up with a handful of Boolean queries that resulted in fewer false positives and a larger percentage of resumes of people who were primarily responsible for the performance testing of networks.

Once I gave these searches to the recruiter and he put them to use, in 2 weeks he called to let me know Google had already hired one of his candidates he had found using the Boolean search strings, and he had an interview request for another.

Where Did He Find the Candidates No One Else Could Find?

So where do you think he found these candidates that no one else had been able to find and submit to Google for the network performance testing positions?

Cold calling? Referral recruiting? Blogs? User groups? LinkedIn? Twitter? Facebook?

Nope – he found them on (drumroll please)…………..Monster.

Yes – this recruiter was able to use a resume database that presumably quite a few (if not all) other vendors to Google (and likely Google’s contract recruiters as well) had access to and most likely used to try and find candidates for these positions for several months.

Interestingly enough, the candidates this recruiter was able to find were not new candidates who just posted their resume – their resumes were over 3 months old, which tells me that they had been in Monster’s resume database ever since Google released their network performance testing positions.

I specifically point this out because I love to continuously disprove the commonly held belief that if many recruiters have access to the same resume database that they will be able to find the same candidates, the best candidates, and all of the appropriately qualified candidates.

Holding onto that belief is as foolish as thinking that if 10 people go fishing in the same lake, that they will all catch the same fish, as well as the biggest fish in the lake.

Job Board Resume Databases Do Have High Quality Talent

This is also a good example of how, contrary to popular belief, you actually CAN find extremely good candidates (Google is notoriously elitist, which I respect) on the job boards. I continue to see well-respected recruiting and staffing thought leaders comment on how the job boards have mostly “mediocre” and declining levels of talent.

This may be subjectively true, but certainly not objectively true. Besides, when’s the last time they ran a search and hired someone from a job board? Nothing bothers me more than people talking about something they have little-to-no direct experience with. 

All Boolean Search Strings “Work”

I am 100% positive MANY recuriters searched Monster in an attempt to find candidates for the network performance testing positions at Google. But there’s a funny thing about Talent Mining – you’re only aware of the candidates you actually find, and conversely, you are not aware of the candidates you didn’t find.

However, that does not mean the candidates you want and need aren’t in the database you’re searching. It just means you weren’t capable of finding them. When most recruiters search any particular database, including their own ATS or LinkedIn, and don’t find the people they’re looking for, they assume the candidates don’t exist.

But they’re there. Trust me.

The Power of Talent Mining with Boolean Search Strings

Here’s the search string that produced one of the candidates who was hired at Google:

Test* and (qa or quality) and (perl or tcl*) and (cisco or rout*) and (lab* or case* or plan* or script*) and (ixia or smartbit* or “smart bit”) and (L2* or LACP or STP or RSTP or VRRP or UDLD) and protocol* and (bgp* or eigrp or rip or ospf or mpls)

Interestingly, most of the search terms in the string above were not in the job description or required skills.

So it took me about 20 minutes of experimenting and refining search strings to come up with that search, from which a recruiter was able to make a hire from less than 10 phone calls on a position that had been worked for 4 months by countless other recruiters who had access to the exact same database.

How’s that for ROI?

That’s the power of effective e-talent discovery.

This is my second post in a series that exposes sourcers and recruiters to the concept and the fact that there are Hidden Talent Pools in every social network, database, ATS, job board, etc. My first post focused on Hidden Talent Pool #1 - the candidates that you can not find . In this post, I will focus on the candidates that you DO NOT FIND.

Most sourcers and recruiters don’t even stop to think about the candidates they don’t find.  Searching for candidates is a lot like fishing – after a day of fishing, most people think about the fish they caught – very few think about all of the fish they COULD have caught, but didn’t. Becoming aware of the fact that there are candidates that you do not find is a significant step on the path towards electronic sourcing and recruiting enlightenment.

Hidden Talent Pool #2 Defined

The Hidden Talent Pool (HTP) of candidates you DO NOT FIND consists of candidates that you do not review because your search returned “too many” results for you to review them entirely.

For example, if you run a Boolean search and it returns 398 results and you only review the first 100, you DID NOT FIND 298 results. Any result returned by a search, but not reviewed by you is a candidate you did not find.

This is similar to searching for buried treasure on a beach. If you excavated 10 cubic feet of sand on a beach but only sifted through 4 cubic feet of it, you DID NOT FIND any treasure in the other 6 cubic feet of sand, even though you took the time to dig it up. Perhaps you could have found treasure, but you did not, simply because you didn’t even bother to sift through the other 6 cubic feet of sand.

This begs the question – if you only review 100 results out of 398, how can you be certain that the best possible candidates were not within the 298 candidates you did not review? Insightful sourcers and recruiters know you can’t. Simple, broad, and imprecise Boolean searches yield large quantities of imprecise results. You must be aware that no single Boolean search can find ALL qualified candidates, and it is inefficient and impractical to sort through several hundred results.

Proof of Hidden Talent Pool #2

(un)Conscious decisions

It’s easy to prove the existence of the Hidden Talent Pool of candidates you do not find. Any time you run a search and make the conscious or unconscious decision to not review every last result, any result you don’t review is a result you did not find. In other words – your search retrieved more results than you reviewed.

System Limitations

In other cases, the Hidden Talent Pool of candidates you do not find is caused by the maximum number of reviewable search results imposed by the system you are searching.  For example, Google limits you to reviewing 1000 results, regardless of how many results it claims your search has found in excess of 1000.

All major job boards have maximum number of reviewable results, as do most applicant tracking systems. With a free account, LinkedIn limits you to reviewing the only first 100 results of any search.

How to Search the Hidden Talent Pool of Candidates You Did Not Find

So how can you specifically target the candidates most recruiters typically do not find? In other words, how can you try and “cherry pick” well qualified candidates from the depths of result sets too big for you to review completely?

Step 1: Instead of running broad and loose Boolean search strings that will almost guarantee you too many results for you to review, I suggest that your first search should always be a “sniper” search – a very “tight” and narrow search to target and to quickly find and “cherry pick” a small number of highly qualified candidates.

• Add explicitly desired (but not required) skills and experience to your searches. These are typically listed on job descriptions and/or mentioned by the hiring manager.
• Add implicitly desired skills and experience to your searches. These are not specifically mentioned or requested anywhere, but would in fact make for a more ideal candidate. For example: industry-specific terminology, competitor-related terms, related certifications, higher than minimum education, etc.
• Add responsibility-related terminology listed in the job description to your searches (which can help you achieve semantic search)
• Add search terms to specifically find candidates who have performed the exact same type of work in the exact same type of environment as they would be working in if hired
• Search a tighter geographical radius than you would otherwise. For example – if you would typically search in a 30 mile radius, start first by searching a 10-15 mile radius. It will narrow your results to a more manageable number and also solve a critical candidate variable – location/commute.

After running your first “sniper” search, you can systematically loosen your searches using the NOT operator to get mutually exclusive results sets. Creating Boolean search strings is not a simple exercise of throwing in a bunch of required skill terms from a job description into a search and looking though SOME of the results, hoping to find SOME good candidates. Your goal as a sourcer or recruiter is to have a true search strategy, and why do anything other that start with the highest probability of match, trying to target the BEST candidates first, and systematically loosen the search on step at a time?

Search Examples

The search process I will detail below can be applied to ANY hiring profile in ANY industry. For this exercise, let’s say you are searching for a software engineer with 3 required skills (Java, SQL, Oracle) and 2 desired skills (AJAX, XML). Let’s also say that you decide to narrow your first search by adding a certification that is related to the work but not mentioned anywhere in the job order (Sun Certification) and that you also decide to search for candidates with industry specific experience (Telecommunications), because your client is in the Telecom industry.

These principles can be applied to searching any system that supports basic Boolean logic (ATS’s, Monster, LinkedIn, etc.) – but let’s use the Internet and Google as our search engine for this search exercise. Keeping it relatively simple, your first search could look like this:

Java SQL Oracle AJAX XML (”Sun Certified” OR “Certified Sun” OR SCJA OR SCJP) (Sprint OR Verizon OR “AT&T” OR “T-Mobile”) (inurl:resume OR intitle:resume) -job -jobs

Search #1

This first search is what I call a “sniper search” that specifically targets any candidates available that meet all of the required, explicitly desired, and implicitly desired qualifications.

Click here for the results.

10 results – easy to review all 10 in a couple of minutes – fast and efficient. This search essentially enables you to find and contact a small number of potentially highly qualified candidates quickly, and exceed your client’s/manager’s expectations. After “cherry picking” the best candidates available with that super-tight search, you can then run these progressively “looser” searches back to back to systematically yield additional and mutually exclusive results – from highest probability of match to lowest probability of match.

Search #2

In your second search, you could then use the NOT operator (the – sign on Google) and drop the list of 4 major telecom companies, as telecom industry experience was not mentioned by the client, although we can be assured that it certainly can not hurt to target candidates with telecom experience first when our client is a telecom company. Remember that Google does not allow you to apply the NOT/- operator to a parenthetical OR statement – you must use a minus sign with each search term you want to remove.

Java SQL Oracle AJAX XML (”Sun Certified” OR “Certified Sun” OR SCJA OR SCJP) -Sprint -Verizon -”AT&T” -”T-Mobile” (inurl:resume OR intitle:resume) -job -jobs

Click here for the results.

We’ve gone from 10 results from our first search to 199 results with our second search – still a relatively manageable number to review, and every result mentions all required skills and desired skills, as well as Sun Certification!

Search #3

In your third search, you could then add the telecom companies back into your search and use the NOT/- operator and drop the search terms for Sun Certification, as your manager/client never asked for it, although why not see if you can actually find people who are certified first? Top performers always seek to exceed expections, not simply meet them. Remember again that on Google you cannot apply the NOT/- operator to a parenthetical OR statement – you must use a minus sign with each search term you want to remove.

Java SQL Oracle AJAX XML -”Sun Certified” -”Certified Sun” -SCJA -SCJP (Sprint OR Verizon OR “AT&T” OR “T-Mobile”) (inurl:resume OR intitle:resume) -job -jobs

Click here for the results.

Yet again we get another quantity of manageable results – 81. Remember that every result not only mentions all 3 required skills, but also both explicitly desired skills and at least 1 of the 4 major telecom companies we were searching for. Still a very highly qualified group!

Search #4

In your fourth search, you could then remove both the list of telecom companies as well as the Sun Certification, leaving you with a search targeting the 3 required skills and the 2 desired skills from the job description. 

Java SQL Oracle AJAX XML -”Sun Certified” -”Certified Sun” -SCJA -SCJP -Sprint -Verizon -”AT&T” -”T-Mobile” (inurl:resume OR intitle:resume) -job -jobs

Click here for the results.

As you can see, dropping the 4 telecom companies and Sun Certification from the search really opens up the results - over 11,000! No sourcer or recruiter is going to review all of those results, which will leave a pretty big Hidden Talent Pool of candidates they DO NOT FIND.

However, you could run several other permutations of the search by systematically using the NOT/- operator on different combinations of explicitly and implicitly desired skills to try and yield more manageable quantities of results.  For example, in response to the 11,000+ results of search #4, you could decide to throw the 4 major telecom companies back into the search, as well as the Sun Certification and remove AJAX from the search using the NOT/- operator, as AJAX was only a desired, and not a required skill.

Search #5

Java SQL Oracle -AJAX XML (”Sun Certified” OR “Certified Sun” OR SCJA OR SCJP) (Sprint OR Verizon OR “AT&T” OR “T-Mobile”) (inurl:resume OR intitle:resume) -job -jobs

Click here for the results.

A very manageable set of 24 results, all mentioning Sun Certification as well as at least 1 of the 4 major telecom companies we were targeting.

Additional Search Combinations

There are MANY different combinations of that relatively simple search that you can explore. Simplifiying the search string, where A, B, and C are the required skills, D and E are the explicitly desired skills, and F and G are the implicitly desired skills, here are 10 more search combinations you could create and run:

6. A and B and C and D and E and not F and not G
7. A and B and C and D and not E and F and not G
8. A and B and C and not D and E and F and not G
9. A and B and C and D and not E and not F and G
10. A and B and C and not D and E and not F and G
11. A and B and C and not D and not E and F and G
12. A and B and C and not D and not E and not F and G
13. A and B and C and not D and not E and F and not G
14. A and B and C and not D and E and not F and not G
15. A and B and C and not D and not E and not F and not G

If you’re fortunate, you may find so many well qualified candidates from the 2 searches that you may not need to run search #3, let alone search #15 – which would be great! The power of this approach is that you start by making the conscious decision to target the best possible candidates first, then systematically run looser searches to peel away the layers, one at a time, to review manageable quantities of mutually exclusive results, with the last search performed being one that solely targets the minimum qualifications.

Maximum Vs. Minimum Qualifications

Essentially, this search strategy starts with targeting the “maximum” qualifications. Most sourcers and recruiters run one search, maybe two, typically only searching for the minimum qualifications. But isn’t the goal of recruiting to find the best candidates? 

For example, many sourcers and recruiters would likely start with search string #4 for the Java software engineer example - but that returned nearly 12,000 results! Most sourcers and recruiters would dive into that massive number of results and begin reviewing them one at a time, perhaps reviewing as many as 50-100 resumes.

I certainly cannot say they would not find some very good candidates in the first 50 to 100 results, but that search approach would leave them with a Hidden Talent Pool of over 11,000 candidates that they DID NOT FIND. How could they know whether or not the best candidates were among the large number they did not review? Let’s also remember that Google limits you to 1000 results, so even if they wanted to, sourcers and recruiters could not review any result past 1000.

Google is not alone in limiting search results – remember that most systems have a maximum number of reviewable results, including the major job boards, applicant tracking systems, and LinkedIn (which is 100 with a free account!!!). Basic and imprecise Boolean search strings will often run into these limits because basic and imprecise searches get a large number of broad and imprecise results – typically too many to review, automatically building the Hidden Talent Pool of candidates they don’t find.

Conculsion

Don’t be what I call a “lowest common denominator” sourcer or recruiter that creates Boolean search strings targeting only minimum qualifications. By design, this approach does not give you a very high probability of finding the BEST candidates and it automatically builds the Hidden Talent Pool of candidates you DO NOT FIND by returning too many results for you to review.

Running a search that returns 300 results will almost guarantee you there are some great candidates in results #100 through #300. However, many sourcers and recruiters don’t even get past reviewing the first 50 to 100 results, leaving many great candidates behind in the process.

Different search strategies can be like the the difference between a shotgun and a sniper rifle – but your goal should not be just to hit the target, but to hit the target in the bulls-eye with as few shots as possible.

You may be surprised to learn that there are Hidden Talent Pools in every social network, database, ATS, job board, etc. – they contain candidates that #1 you can not find and #2 you do not find. In this post, I will focus on the candidates that you can not find.

You may ask, “So, why can’t I find them?”  That’s a very good question – most sourcers and recruiters don’t even know that that there are candidates they can’t find with their Boolean search strings, so simply being aware that there are candidates that you can not find is a significant step on the path towards electronic sourcing and recruiting enlightenment.

Hidden Talent Pool #1 Defined

The Hidden Talent Pool (HTP) of candidates you can not find consists of candidates who do not mention the specific search terms that you have included in your Boolean search string in their resume or social network profile. 

For example – if you are working on an open position for a business analyst and you create a search string that only contains the title of “business analyst,” you CAN NOT FIND candidates who do not have that title but who are qualified for your position. For example, there may be many well qualified candidates who do not have the specific title of “business analyst,” and instead have other titles such as systems analyst, business systems analyst, requirements analyst, functional analyst, or even just analyst. However, your search targeting the title of “business analyst” CAN NOT FIND those candidates, even though they are readily available. For all that you know, they don’t exist because they don’t show up in your search results. But they do exist.

Proof of Hidden Talent Pool #1

Let’s say you are working on an open position for a business analyst, and you’re going to search for resumes on the Internet using Google.

SEARCH #1 - Targeting only the title of “business analyst”

(inurl:resume | intitle:resume) “business analyst” (requirement | requirements) -~job -~jobs

Click here for the results - Nearly 4,000 results, and as expected – most of the results are of business analysts.

SEARCH #2 – Eliminating the title of “business analyst” and targeting alternate titles

On Google we can use the minus sign as the Boolean NOT operator in front of the title of “business analyst” to get results that do NOT contain that title.

(inurl:resume | intitle:resume) -”business analyst” (”business systems analyst” | “requirements analyst” | “functional analyst” | “systems analyst”) (requirement | requirements) -~job -~jobs

Click here for the results- Around 2400 results, none of them with the title of “business analyst,” yet most of them are resumes of people who perform the role of a “traditional” business analyst – working with users, gathering requirements, etc.

It’s important to realize that the results of Search #1 and Search #2 are mutually exclusive - no results from Search #2 are in the results of Search #1 and vice versa. This means that Search #2 represents a Hidden Talent Pool of 2400 candidates that sourcers and recruiters who run a search like search #1 CAN NOT FIND.

The Cardinal Rule of Boolean Search

Sourcers and recruiters creating searches that can only find candidates that mention the specific title of “business analyst” can not find those additional 2400 candidates unless they take into consideration the Cardinal Rule of Creating Boolean Search Strings. The Cardinal Rule of Creating Boolean Search Strings states that for every skill, title, technology, etc., that you are thinking about including in your Boolean search – take a moment to think of #1 whether or not every person would make explicit mention of it in their resume, and #2 if they would, how many possible alternate ways can it be expressed?

For example – if there are 5 different titles for the same type of role, or 5 different ways of referencing a specific responsibility or technology and you only think of 2 of them, you simply CAN NOT FIND the candidates who mention the other 3 titles or ways of expressing the same thing.

So, if a recruiter heeded the Cardinal Rule of Creating Boolean Search Strings and took some time to think about all of the various titles that business analysts can have before they created their Boolean search string for a business analyst position, they could increase their relevant results by 60% by tapping into the Hidden Talent Pool #1 of an additional 2400 candidates!

Another Example

If you were searching for an accountant and the hiring manager specifically requested experience with GAAP, FASB, and SOX, you may be inclined to add those terms to your Boolean search string. However, if you add all three of those terms to your Boolean search string, you CAN NOT FIND candidates who actually DO have experience with GAAP, FASB, and SOX, but do not explicitly mention those terms in their resume or profile.

These candidates ARE in the social networks you’re searching, your ATS, on the job boards, even on the Internet. These candidates have the experience with GAAP, FASB and SOX that you’re looking for – but your Boolean searches are actually preventing you from finding them – a graphic example of the Hidden Talent Pool of candidates you can not find. If you search for a term or terms that candidates don’t mention in their resume or profile, you simply can not find them.

Challenge Your Assumptions

Sourcers and recruiters commonly make the mistake of assuming that all candidates will always mention every last skill and buzzword they have experience with. In fact, more often than not, candidates don’t.

First – you must realize that whoever is writing the resume you’re searching for is not a resume writing expert – they are whatever you’re searching for – an accountant, a project manager, a scientist, a network engineer, etc.

Why anyone expects “perfect” and completely detailed resumes is beyond me. Writing a “complete” resume that specifically mentions every thing someone has ever done and done it with is nearly impossible. Depending on length of career, many candidates have only had to write 2-5 resumes in their career. Doing something 2-5 times does not an expert make!

Some of the things that sourcers and recruiters think that every candidate would “obviously” know to mention in their resumes are things that candidates don’t – and this is often because these “obvious” and “marquee” skills and/or technologies are so rooted in the candidates’ day to day resposibilities that it slips their mind to mention it.

The other classic situation is when candidates assume recruiters and hiring managers would automatically know what they have done and done it with based on other details in their resumes – this is quite common. I’ve personally heard that from too many candidates to count.

Conclusion

Be aware of the fact that your Boolean searches may actually be creating Hidden Talent Pools of candidates you can not find. Before creating searches for any position, heed the Cardinal Rule of Creating Boolean Search Strings and take time to carefully think about all of the various ways that the titles, skills, and technologies could possibly be mentioned, or even if they would be mentioned at all. By doing so, you can find more qualified candidates for your positions – sometimes by as much as 60%! – and perhaps even more importantly, find candidates that your competitors can not find.

How’s that for a competitive advantage?

Once people become familiar with my theory of Hidden Talent Pools, they often become curious to see examples of candidates that most recruiters don’t find.

Photo by utrechtman

Not too long ago, a recruiter requested me to assist them in locating a solid MySQL DBA for a position at a well-known social networking entity. If you’ve ever worked this type of role, you would know that one of the major challenges to running Boolean searches in a resume database or online is that you run into a large number of “false positives” – results of candidates that mention the words in your search, but they are not a functional or technical match to what you’re actually looking for. In this case, some examples of false positives would be Oracle or SQL Server DBA’s who happen to mention MySQL somewhere in their resume – either in their skills summary or their career history.

When your client is looking for someone whose primary focus as a DBA has been the administration of MySQL databases, we need to come up with a method of reducing false positives and increasing the percentage of results that reflect the essence of what we’re looking for – in this case, MySQL DBA’s.

Most recruiters and sourcers tend to shoot for titles when looking for DBA’s. For example:

MySQL and (DBA or “Database Administrator”)

However – that kind of search does nothing to reduce false positives. In fact – it welcomes them with open arms. That search string pulls tons of non-MySQL DBA’s (Oracle, SQL Server, DB2, etc.) who happen to mention MySQL somewhere in their resume, but it’s never been their primary responsibility. Reviewing the results of this kind of search would be a time drain and extremely low yield.

Another approach some recruiters and sourcers would take would be to be more specific with titles in an attempt to reduce false positives. For example:

(”MySQL DBA” or “MySQL Database Administrator” or “MySQL Administrator”)

While that search will do an excellent job of reducing false positives and will limit results largely to those of people who have had administering MySQL and their primary responsibility – the MAJOR flaw in this kind of search is that the vast majority of appropriately qualified candidates don’t actually have those phrases on their resume. Which means while this kind of search will find a few well matched candidates, it will in fact NOT find the majority of them – they will be stuck in the Hidden Talent Pool of candidates you can not find, because your search actually prevents them from being in the results.

To take a different approach, when I was experimenting with Boolean search strings in an attempt to assist the aforementioned recruiter, I when to Monster first to take advantage of their support of the NEAR proximity operator.  Here is a snippet of one of the Boolean search strings I used to reduce false positives and increase the percentage of results of candidates who have been responsible for administering MySQL databases.

MySQL NEAR (database* or DBA or admin*) and admin* and MySQL

What’s going on in this search string is that, in addition to any mention of all words beginning with the root of admin and MySQL, all results also have to have MySQL mentioned within 1 to 20 words (according to Monster) of any mention of any word beginning with database, admin, or the term DBA.

While this search string will still pull some false positives (any good search will, but more on that later), they are reduced and you’ll also notice a higher percentage of results of candidates who have administering MySQL as a primary responsibility at some point.

Here’s a snippet of a resume that I found with the above search. As a search/resume forensics aficionado, I find this to be a fascinating specimen retrieved from the Hidden Talent Pool on Monster.

• MySQL/Oracle Production/Development DBA (Linux, Solaris, Windows)
• Senior Database and Unix Administrator
• DBA duties for MySQL 5.018 databases running on RedHat EL4 WS for (Java) infrastructure license managment system
• DBA Tools: MySQL command-line interface, MySQL-Migration-Workbench, Mysql-Admin
• Database and Network Administrator
• DBA duties for MySQL database running on RedHat EL3 for multiple websites.

This person appears to be responsible for administering MySQL and Oracle databases, as well as the UNIX systems they reside on. What I find really interesting is this – notice how many words are between MySQL and DBA, and how many words are between database and administrator. In fact, they are never mentioned directly beside each other – they are always separated by 2-4 words. Because of this, most recruiters and sourcers would never find this resume at all – and if they did, it would be by pure luck. However, the Boolean search I used found this person by design.

Using the NEAR operator allowed me to not be dependent on a specific title (e.g., MySQL DBA) and also allowed me to search semantically to find people who talked about administering MySQL databases, requiring only that the search terms were adjacent and not necessarily contiguous, as most other people’s searches would. In other words, I was searching for sentences/bullet points in which candidates talked about being responsible for MySQL administration, without requiring any exact phrase. Essentially, any sentence in a resume that mentions MySQL as well as any word beginning with the root of admin is of interest to me, as it’s highly likely that it is some expression of being responsible for administering MySQL, regardless of word order or how many words are between the two terms.

However, Monster’s NEAR operator’s range of up to 20 words can get sloppy – in general, the farther words are mentioned from each other, the less likely it is that they are semantically related. In fact, at 15-20 words distant, the search terms are often in separate and unrelated sentences and bullet points. But I’ll take NEAR over no proximity operator at all. I have no clue why the other major job boards don’t support any form of proximity searching.

Now, if you’re fortunate enough to have access to a resume database that allows for configurable proximity searching, such as one that uses Lucene or dtSearch for text search, you could really take control over the precise distance between search terms. Here is an example search using Lucene’s and dtSearch’s syntax respectively:

“MySQL (database* OR DBA OR admin*)”~5 

MySQL w/5 (database* or DBA or admin*)

Running that search is a thing of beauty – requiring all results to have MySQL mentioned in 5 words or fewer words of any word starting with the roots of database or admin, or the term DBA, drastically reduces false positives and significantly drives up the percentage of results of candidates who have had administering MySQL databases as a primary responsibility.

Here are a few samples from several different resumes that were retrieved from the above search. Pay special attention to the exact verbiage – in each case, there is something interesting about the order and proximity/distance/separation of the words. These examples serve as proof that there are candidates hiding in Hidden Talent Pools – the Boolean searches most recruiters use simply don’t find these resumes by design. 

• Responsible for MySQL and Apache Administration.
• Assumed all DBA duties (Sybase and MySQL).
• Over 5 years of database administration experience using MySQL
• Certified MySQL 5.0 Database Administrator
• Database Administrator: MySQL 4.1.x – 5.0.x
• Designed and implemented database in MySQL
• Installed, configured, updated and was responsible for troubleshooting production MySQL 5. Successfully solved Database system/performance issues during project implementation and support phase.
• Senior DBA for MySQL Development/Testing environments.
• Configuration and administration of MySQL master-master and master-slave replication servers.
• User Management and Database Management using phpmyadmin and MySQL command line.
• Scripts for import/export and recovery of MySQL/Oracle databases.

And now you have seen some proof of the existence of Hidden Talent Pools, where basic and “conventional” Boolean searches seldom or simply cannot delve into.  Yet, with the proper approach and creative use of proximity searching, an accomplished Talent Miner can actually target and exploit these Hidden Talent Pools by finding candidates that most other recruiters and sourcers do not or can not find, or find by chance alone.

There is a Hidden Talent Pool (HTP) in every source of candidates – the Internet, job board resume databases, Social Networks, your ATS, etc., and it contains candidates that you assume aren’t there because you never see them. However - just because you never see them doesn’t mean they aren’t actually there. Because they are. Years ago, I became aware of these Hidden Talent Pools and I have figured out how to tap into them using creative search strategies and common Boolean operators. I estimate that HTP’s account for about 30-40% of the candidates in each talent respository (Internet, ATS, job board, etc.). 

Each Hiddent Talent Pool is comprised of two distinct groups of candidates:
#1 Those that you do not find
#2 Those that you can not find

Sounds like a play on words, right? I assure you it’s not. Here’s the difference:

Candidates you do not find

If you run a Boolean search and it returns several hundred (or more) results – you are not likely to examine each result due to the quantity, typically due to 2 reasons: #1 time constraints, and #2 you find “enough” candidates from the first 50 to 150 results, you don’t “need” to look through the rest.  Any candidate that was retreived by your Boolean search that you did not actually examine or review is a candidate you did not find. It would be the same if you were searching for buried treasure and you excavated 10 cubic feet of sand on a beach.  If you only sifted through 6 cubic feet of sand and you were either too tired to sift through the rest or had “found enough” treasure in the first 6 cubic feet, you did not find any treasure in the remaining 4 cubic feet of sand.

Candidates you can not find

When you run Boolean searches, any candidate that mentions a skill, technology, or responsibility in a way that you did not include in your Boolean query, you can not find the candidate. Unless you think of and account for every possible way a candidate can mention a thing that you are searching for (which is nearly impossible), you simply can not find every qualified candidate. Further exacerbating this issue is that for various reasons, there are many candidates that simply do not explicitly mention all of their skills and experience in their resume (or profile, or web page, etc.). And if they don’t mention it, you can not create a Boolean search string to retreive them, and thus you can not find them.

Simple and basic Boolean search strings will net you some candidates, and typically the same candidates that your competitors can find. As a recruiter or sourcer, it should be your goal to not only be able to find the candidates that your competitors can find, but more importantly, to be able to find the candidates that your competitors can not find. 

The first step on the path of sourcing enlightenment is becoming aware of the Hiddent Talent Pools. Stay tuned for some tips and tricks that you can employ to begin to tap into these Hidden Talent Pools.