Have you ever found yourself running searches for candidates online or in a resume database and finding a large number of results are of people who are either too senior or too junior? Ever wondered if there was something you could do about it?

Although my first bit of advice to sourcers and recruiters facing this dilemma would be to simply call the candidates anyway (the reasons for this is another post entirely), there is actually something you CAN do when creating Boolean search strings to build in some degree of control over the years of experience of the people who are returned in your search results.

Sound Impossible?

Well, the bad news is – it is impossible. At least for search engines that don’t support proximity or adjacency search operators, such as NEAR.

The good news is there are search engines that DO support proximity search via the NEAR Boolean operator. For example, there is at least one Internet search engine (Exalead) and one major job board (Monster) that support the NEAR Boolean operator. Also, some sourcers and recruiters are fortunate enough to have access to an ATS that uses an integrated text search engine that supports proximity (aka adjacency) searching, such as Sovren or Lucene (Bullhorn uses the latter).

How to Control Years of Experience with the NEAR Boolean Operator

Let’s say you are looking for candidates that have approximately 3 to 5 years of professional experience. One way to go about attempting to specifically target people who may in fact actually have only 3 to 5 years of professional experience is to search for people who graduated from college 3 to 5 years ago.

Using a search engine that supports the NEAR operator (or other proximity/adjacency operator), you can create Boolean search strings that attempt to target people who mention a specific graduation year (or years) in relatively close proximity (e.g., 8-16 words) to a mention of a specific degree. Doing this can increase the probability that you will return candidates who in fact have 3-5 years of post-collegiate, professional experience.

Using Exalead to Search for Internet Resumes

Exalead interprets the NEAR operator as “within 16 words.” In other words (no pun intended), Exalead will take a query such as X NEAR Y and only return results it finds in which the search term X is mentioned within 16 words or fewer of search term Y – regardless of order (before or after).

Let’s try and search the Internet using Exalead to find resumes of people who are likely to have approximately 3 to 5 years of post-Baccalaureate experience.

(inurl:resume OR intitle:resume) AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR (2004 OR 2005 OR 2006) AND NOT job AND NOT jobs

Before taking you to the results, it’s important to understand what’s going on in the search string. I’m specifically looking for a mention of 1 of 5 common ways of expressing a Bachelor’s degree within 16 words of a mention of any of the years of 2004, 2005, or 2006. I’m hoping to zoom in on people who are likely to have graduated high school and then immediately went to college and then started work immediately following college beginning in 2004, 2005, or 2006. That’s where the 3 to 5 years of professional experience should come into play.

Click here for the results

Here’s a snippet of the first page of the results:

Taking a look at the first result, you can see that this particular person does in fact mention a B.A. degree within 16 words of the year 2004 (twice, actually).

And moving on to their experience, you can see their first paid position post-college started in the fall of 2005, yielding them approximately 3.5 years of professional experience.

Using Exalead to X-Ray Search for LinkedIn Profiles

We can exploit Exalead’s NEAR operator in conjunction with the X-Ray search technique to search for LinkedIn profiles of people who are likely to have graduated college with a Bachelor’s degree within a specific range of years.

For example:

site:linkedin.com AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR (2004 OR 2005 OR 2006) AND (inurl:pub OR inurl:in)

Click here for the results

Exploring the results you can see it works relatively well – this person has 2 Bachelor’s degrees in the range of 2004 to 2006:

And looking at his experience, it appears he actually has about 6.5 years of experience – outside of our target range of 3 to 5, but close.  We can see why this happened – he was working while he was in college.

Taking a look at another result, Braden graduated with a BS degree in CS in 2006 (right on target) and has also achieved an MS degree in 2007:

Exploring Braden’s work experience, outside of his intern experience at SPI, he has just about 3 years of experience at Apple. Nice job Exalead!

But Wait – Exalead’s Not Done!

At a range of 16 words, Exalead’s NEAR operator can get sloppy at times – yielding results in which the search terms are not in the same sentence or section of the resume which can produce false positive results.

However, Exalead supports configurable proximity – you can modify the NEAR operator to control the EXACT distance between your search terms. For example, X NEAR/5 Y will only return results in which search term X is mentioned within 5 or fewer words of search term Y.

If you are finding 16 words to be too great a distance, you can dial it down to, say, 8 words.

For example:

site:linkedin.com AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR/8 (2004 OR 2005 OR 2006) AND (inurl:pub OR inurl:in)

Click here for the results

Using Monster to Search for Resumes

Many people are unaware that Monster supports the NEAR operator. On Monster, the NEAR operator is interpreted at a fixed proximity distance of 10 words. Going for the same target of 3 to 5 years of experience, here’s what you can add to any Boolean search string on Monster:

(BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR (2004 OR 2005 OR 2006)

Here are a few example results, showing how this specific application of the NEAR operator when performing searches on Monster can return results of people who have graduated from college with a Bachelor’s degree in a specific range of years:

Monster Anomaly

Although Monster does support the NEAR operator, and you can definitely yield many good results with the aforementioned search example, Monster sometimes behaves oddly – returning results with positive hits on one of the years we were searching for, but it’s not within 10 words of a mention of a degree. For example:

However, you can rest assured that every search result WILL in fact have at least 1 mention of one of the degree search terms you’ve specified within 10 words of one of the years you’re searching for.

Targeting Specific Degrees

You can take this search technique one step further and target specific degrees attained in specific years.

Here is an example of using Exalead to X-Ray search LinkedIn for people who have attained a Bachelor’s degree in Accounting in the years of 2004-2006:

site:linkedin.com AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR/8 (2004 OR 2005 OR 2006) AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR/6 Accounting AND (inurl:pub OR inurl:in)

Click here for the results

Sample:

Here is what it would look like on in a Monster search:

(BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR (2004 OR 2005 OR 2006) AND (BS OR BA OR “B.S.” OR “B.A.” OR Bachelor*) NEAR Accounting

Search Disclaimer

This search technique – attempting to try and control the years of experience of candidates with a Boolean search string – like ALL search techniques, is not perfect or without limitation.

Here are a few key points to keep in mind:

#1 There are many fantastic candidates who did not graduate from college. However, as long as a college degree is not a hiring requirement, you can also apply the same technique to search for people who graduated from high school in a specific range of years.

#2 Candidates who do graduate from college do not necessarily attend college immediately following high school – and this can definitely affect the effectiveness of this search technique.

#3 There are MANY other variations of Bachelors degrees than the ones I used in my basic examples – such as BBA, BSc, BSBA, etc. I will leave it up to you to add whatever specific degrees you would like to target to your search strings.

#4 You WILL get some false positives.

• Driving through your search results, you can find hits of people who happen to mention the years you’re searching for (e.g., 2004, 2005 or 2006) within 10-16 words of a degree you’re searching for, but the year is in fact not a graduation date.
• Some results can be returned in which people may have in fact graduated in a specific year (e.g., 2005, 2005, or 2006) with one of the specified degrees but they have more than 3 to 5 years of experience.
• You will also find some results in which people have multiple Bachelor’s degrees from different years which may defeat our objective of finding people who specifically have 3 to 5 years of professional experience.
• You can find false positive hits of people who mention both the year they started college as well as they year they actually attained their degree. If you’re targeting people who graduated in 2004, 2005, or 2006, and they mention on their resume something like this: Bachelor of Science in Accounting 2004-2009 – the NEAR operator can pull a false positive of the mention of 2004 within a certain number of words from the mention of the degree. In this case – the person started working towards their degree in 2004 – one of the years we were searching for, but actually graduated in 2009 – which may mean the candidate has little to no professional experience.

Conclusion

You CAN create Boolean search strings that have a higher than average likelihood of returning results of people who have graduated college within a certain range of years, which can help you target people who have a specific number of years of professional experience.

Not only can this technique help you identify people who are more likely to NOT be under- or over-qualified and therefore reduce false positives, it can also help you target people who are more likely to accept a specific compensation range. For example – if you have a maximum target compensation for a particular position you are recruiting for – you can try and target professionals who are more likely to have a specific number of years of experience who are likely to find your position’s pay range to be acceptable. In many professions, years of experience is at the very least loosely related to compensation.

While not perfect, this search technique can help you create Boolean search strings to achieve what most people think to be impossible. Give it a try – and let me know what you think.

Have you ever wanted or needed to be able to search for people that have a grade point average in a specific range? Searching for the term “laude” will certainly pull results of any person who specifically makes mention of graduating cum laude, magna cum laude, and summa cum laude, but not everyone with a decent GPA graduates with one of those statuses.

Beyond searching for “laude,” you CAN create Boolean search strings to target the actual GPAs. Any search engine that supports proximity searching gives you the power to target numeric GPA values.

Let’s first take a look at an Internet search engine everyone has access to: EXALEAD

Here is a basic search example:

(intitle:resume | inurl:resume) AND “engineer” AND (BS OR BA OR Bachelor* OR “B.A.” OR “B.S.”) AND (GPA OR “G.P.A.” OR “grade point”) NEAR (3* OR “4.0″) AND NOT sample*

As you can see, most of the results are resumes of people who have a GPA over 3.0.

LINKEDIN VIA EXALEAD

site:linkedin.com AND “developer” AND (GPA OR “G.P.A.” OR “grade point”) NEAR (3* OR “4.0″) AND NOT intitle:directory

Unfortunately, Exalead does not handle searching around punctuation very well, so trying to run something like this: (GPA OR “G.P.A.” OR “grade point”) NEAR (3.2* OR 3.3* OR 3.4*…) does not work.

MONSTER

Monster is the only major online job board resume database to support proximity search through the NEAR operator.  Unlike Exalead, Monster’s search engine can handle searching around punctuation, so we can take the search use used on Exalead a step further:

Accountant and (GPA or “G.P.A.” or “grade point”) NEAR (3.2* or 3.3* or 3.4* or 3.5* or 3.6* or 3.7* or 3.8* or 3.9* or “4.0″)

One thing you will find when you try this trick on Monster is that you will also see all manner of non-GPA numbers highlighted throughout the resume – however, each resume will always be a specific GPA mentioned that meets your search criteria.

Happy GPA hunting!

This post will cover graphic examples of how to achieve semantic search using the NEAR Boolean operator on Monster and on the Internet via Exalead using Accounting and Information Technology hiring profiles.

First, if you have not done so already, be sure to read these 2 posts that throroughly explain the concepts of user-defined semantic search for sourcing and recruiting: Semantic Search for Sourcers and Recruiters, and Semantic Search for Sourcers and Recruiters Round 2. 

Second, if you’re not already familiar with the NEAR operator, I highly recommend you read about it in this post: Extended Boolean – Proximity and Weighting (look for it in the middle of the post under “Fixed Proximity”) before proceeding any further.

The NEAR operator on Monster

Monster is the only major job board resume database that recognizes and supports the NEAR Boolean operator. According to Monster’s documentation, the NEAR operator has a maximum proximity of 10 words. For example, the search string software NEAR programmer returns ONLY those resumes that have software and programmer within 10 words of each other. 

Let’s look at a couple of resume snippets that I used from Monster when I wrote a post on Boolean Search Strings for a Sales Tax Manager. What we’re going to do is go beyond the individual words themselves and look for how the candidates wrote sentences describing specifically what they’ve been responsible for doing. Then we will use the NEAR operator to create Boolean search strings that go beyond simply trying to match the words themselves and attempting to delve into the meaning implied by the words by targeting sentences describing responsibilities.

Notice in both resume snippets, we see verbs like prepared, supervised, managed in close proximity to words like tax, statements, returns, workpapers, schedules, month and quarter end, personnel, staff, etc.

Let’s use the NEAR operator to specifically target sentences where candidates are talking about the types of responsibilites we need them to have had experience with:

prepar* NEAR (tax* or statement* or return*) and (manag* or supervis*) NEAR (personnel or accountants or staff) and tax NEAR (manager or supervisor) and (state or local or federal) and (sales or use)

Breaking down that search – here is exactly what’s going on with the use of the NEAR operators: 

#1 prepar* NEAR (tax* or statement* or return*)

This will require the results to have ANY word starting with the root of tax, statement, or return within 10 words of any word beginning with the root of prepar*

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     sentences mentioning responsibilities such as being responsible for the preparation of tax returns, statements, and returns 

#2 (manag* or supervis*) NEAR (personnel or accountants or staff)

This will require the results to have ANY mention of any word starting with the root of manag or supervis within 10 words of personnel, staff, or accountants

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     sentences mentioning responsibilities such as managing and supervising personnel, staff, or accountants  

#3 tax NEAR (manager or supervisor)

This will require ANY mention of the word tax to be within 10 words of the words manager and/or supervisor

Semantic analysis:
This aspect of the search will be highly likely to return results of resumes that have     titles of tax manager or tax supervisor, as well as resumes with sentences mentioning responsibilities such as managing and supervising tax-related work and/or personnel 

Here are a few examples of the results returned by the above search that clearly demonstrate the NEAR operator hard and effectively at work:

If you’re very observant, you’ll see that the second snippet has a title of “Section Manager – Income Taxes.” The reason I highlight this is that a traditional title search for titles such as “Tax Manager” or “Manager of Tax” could not return that result – a nice example of a candidate that most sourcers and recruiters would not be able to find based on a common title search approach, and proof that hidden talent pools really do exist.

However, using the NEAR operator gives us a handy alternative to specific title searching, and by design, allows us to find candidates with not-as-common titles such as “Section Manager – Income taxes.” Cool.

Now, while the first 50 results of that search had it’s fair share of false positives – such as candidates who are now controllers, CFO’s, and such, but were at some point in their careers responsible for managing tax reporting and personnel, I want to point something out:

Using the NEAR operator, that search pulled 10 direct hits among the first 50 results that had titles of Tax Manager, Supervisor, or Director.

Then, out of curiosity, I took out the NEAR operators and simply replaced them with ANDs:

prepar* and (tax* or statement* or return*) and (manag* or supervis*) and (personnel or accountants or staff) and tax and (manager or supervisor) and (state or local or federal) and (sales or use)

Only 4 out of the first 50 results happened to be direct hits of candidates who had titles of Tax Manager, Supervisor, or Director. So we were able to more than DOUBLE our highly relevant matches among the first 50 results alone by employing the NEAR operator. Sweet.

Now let’s show some love to Information Technology sourcers and recruiters

Let’s say you’re looking for software engineers that, among other things, have deep Java experience as well as specific experience designing portals. This one is easy.

Java and (design* or develop*) and portal* NEAR (design* or develop*)

The double mention of the phrase (design* or develop*) is not redundant.  The first mention is to find any mention of any word starting with the roots design or develop.  The second mention is in conjunction with the NEAR operator, and REQUIRES all results to also have any mention of portal or portals to be within 10 words of any word starting with the root of design or develop.

Semantic analysis:
Using the NEAR operator to ensure that any mention of the words portal or portals is within 10 words of design* or develop* increases the probability that they are mentioned in the same sentence – and if they are mentioned in the same sentence – it’s highly likely that the person is talking about being responsible for designing/developing portals. Which is exactly what we’re looking for.

This is specifically different from just throwing all of the words together and HOPING we get some people who have been responsible for portal design/development. Hope is not a strategy. So we’re using the NEAR operator to target people who ARE responsible for portal design and development, by the design of our Boolean search string.

Here are 3 resume snippets that demonstrate the NEAR operator working its magic:

As you can see, using the NEAR operator worked quite nicely – we targeted the semantics of talking about being responsible for designing/developing portals – and we got it.

The NEAR Operator on Exalead

I realize that not everyone has access to Monster, so let’s take a look at an Internet search engine that everyone DOES have access to – Exalead.

Exalead is the only decent-sized Internet search engine to support proximity searching in the form of the NEAR operator. I say “decent-sized” because it’s not a major search engine in my opinion – mostly because it does not appear to index near as many pages as Google, Yahoo, Live, or Ask, and this is especially and painfully evident when you do back to back searches using the site: command to x-ray into LinkedIn. More on that in another post (here’s a quickie – feel free to run just site:linkedin.com on both Exalead and Google and you will find 10X the total results on Google).

Let’s take a swing at the Java/portal developer search we used above on Monster and point it towards the Internet via Exalead.

(intitle:resume OR inurl:resume) AND java AND (design* OR develop*) NEAR portal* AND NOT job*

Before we look at the results, be aware that unlike Monster, Exalead’s NEAR distance is 16 words – which is getting a little “out there” in terms of proximty.  Also – see how I was able to actually use the asterisk for root word/stemming?  Man that feels good to be able to do that on the Internet. Anyone from Google reading this?

Click here for the results.

Pretty nice results, right? If you check the results, all of them mention portal or portals within 16 words of design*/develop*, in most cases resulting in sentences where the candidate specifically talks about being responsible for/performing portal design/development. Which is exactly what we’re looking for – NOT just people who happen to mention those words somewhere in their resume. We’ve leveraged semantics in our search approach rather than reosrting to the ”buzzword bingo” game.

But wait – there’s more! Exalead also supports configurable proximity. So if 16 words is too far of a gap for you and leads to less relevant results, we can tighen that range.

For example, let’s limit the distance between any mention of design*/develop* and portal* to a maximum of 5 words.

(intitle:resume OR inurl:resume) AND java AND (design* OR develop*) NEAR/5 portal* AND NOT job*

Click here for the results.

We’ve managed to cut the total number of results down significantly, and we’ve also increased the relevance while reducing false positives. Look at how tight those results are! It’s because every single result HAS to mention design*/develop* within 5 words or less of portal*.  We are leveraging semantics heavily here because most mentions of those words in such close proximity are in fact referencing responsibility – we’ve blown past word matching to nail people talking about doing what we need them to have experience with! Am I the only person excited about this?

However, as exciting as this is and as tight as those results are, we have to be cognizant of the fact that there ARE relevant results we just eliminated.  Yup – anyone who mentioned design*/develop* at a range of 6-16 words from portal* was wiped away and we did not see them.  Which is okay, as long as you are aware of this and know how to go back and get them.

As I’ve discussed in many posts, I think the best approach to secondary sourcing is to start tight and highly focused on the most relevant results rather than starting broad and beginning a search buy sifting through tons of false postitives. In other words, if I were target shooting – I would start with a sniper rifle and try to hit the bullseye, rather than start with a shotgun and just be happy to hit the target. 

Applying NEAR to LinkedIn

Let’s use the site: command to use Exalead to x-ray into LinkedIn and exploit Exalead’s configurable proximity search to find Java developers who have been responsible for designing/developing portals.

site:linkedin.com AND java AND (design* OR develop*) NEAR/8 portal* AND (inurl:pub OR inurl:in) -intitle:directory

Click here for the results.

Yup, NEAR works there as well. Every result mentions some mention of design*/develop* within 8 words of portal or portals.  

Also – did you notice how I did not use the -/minus sign coupled with 4 to 6 things I was trying to avoid like jobs, answers, and such? I have found that using (inurl:pub OR inurl:in) is a little cleaner - it simply targets public profiles and all I need to NOT out is intitle:directory in most cases.

Conclusion

Being able to control how close words are mentioned to each other via the NEAR operator enables us to achieve semantic search – tapping into sentence structure and the power of meaning in language. Instead of throwing a bunch of words together and having to sift through large volumes of irrelevant and false positive results, we can attempt to harness semantics to find people based on what they have experience DOING, not just based on what words they happen to include somewhere in their resume.

Kudos to Monster for being the only major online job board to support the NEAR operator, and props to Exalead for not only supporting NEAR, but going a step further and supporting configurable proximity via NEAR/x.

Can’t get enough of semantic search for sourcing and recruiting? My next post will cover how to achieve semantic search for sourcing and recruiting without using any proximity operators. 

Stay tuned!

Boolean Search Strings for a Sales/Use Tax Manager 

NOTE!
For those who have absolutely no interest in seeing search examples for a tax manager position, the search principles and tips contained in the post can be applied to ANY SEARCH, and you may also be interested in a demonstration of the NEAR operator, as well as a couple of ”hidden lessons” in the Google search section of this post. 

Boolean Search Strings for a Sales/Use Tax Manager 

Recently I recieved a request for some search help with crafting Booleans for a Tax Manager with sales/use tax experience. I experimented with some searches using a few different resources and thought I would share the exercise with you - the searches you see below can serve as good starting points and get your imagination going. 

We’ll take a look at search ideas for Monster (leveraging the NEAR operator), Careerbuilder/Hotjobs, and Google, and LinkedIn via Google. 

Monster

This search is for Monster only – taking advantage of the fact that it’s the only major job board that recognizes the NEAR operator:

(CPA or “C.P.A.” or “certified public”) and (accounting or audit* or tax*) NEAR (Manager or Supervisor) and tax* NEAR (state or local) and (sales or use) NEAR tax* and tax*

Hint: the multiple mentions of tax* is NOT redundant.

Notice I did not search for any specific title. One of the beautiful aspects of the NEAR operator is that you don’t have to worry about thinking of all of the various titles your target candidates could possibly have.  The above search will return results of anyone who mentions any combination of tax and manager within 10 words of each other. Also, you’ll see I specifically targeted any word starting with the root of “tax” to be mentioned within 10 words of (state or local) and (sales or use), increasing the likelihood that they would be mentioned in the same sentence and therefore more relevant to the search and what we’re looking for.

Here are some examples:

Looking good, yes? Notice how in the second sample resume the title is “Tax and Accounting Manager.”  Anyone who was searching only for “Tax Manager” and “Manager of Tax” types of titles would not be able to find this person. However, the NEAR operator frees us from having to target specific titles and nets us people like the candidate above.  Thanks NEAR operator!

Configurable Proximity

If you had access to a resume database that supports configurable proximity (which I knew the person who requested help did), you could get even more precise, closing the word gap down from 10 (the distance of the NEAR operator on Monster) to 5 and 8:

(CPA or “C.P.A.” or “certified public”) and audit* and (manager or supervisor) w/5 (accounting or audit* or tax*) and tax* w/8 (state or local) and (sales or use) w/8 tax* and tax*

Configurable proximity searching is a beautiful thing.

Careerbuilder/Hotjobs

Without the ability to use proximity, we’ll have to go back to a more standard search and target titles for a first pass.  While I am not a big fan of title searches because if you forget a title (or two, or more!), you leave great candidates behind in a hidden talent pool as we just saw above with the “Tax and Accounting Manager”, it’s a good start to “sniper” in on the people who are holding a neon sign over their head saying, “Hey! I’m a Tax Manager!” 

(CPA or “C.P.A.” or “certified public”) and (”tax manager” or ”manager of tax” or “accounting manager” or “tax supervisor” or “manager tax”) and tax* and (state or local) and (sales or use)

You may be wondering why I used “manager tax” – I did so because there are some companies and people who list the title of: Manager, Tax.

To make a second pass at this search and avoid all of the folks we found and reviewed from the first search, we can use the NOT operator in a way you can’t on Google:

(CPA or “C.P.A.” or “certified public”) and NOT (”tax manager” or ”manager of tax” or “accounting manager” or “tax supervisor” or “manager tax”) and tax* and (manager or director or supervisor) and (state or local) and (sales or use)

That search results in a mutually exclusive result set from the first title search.

Google

For this example, I’ll target a specific area – Minneapolis, MN. My first example will target area codes I pulled from nanpa.

(intitle:resume | inurl:resume) (”tax manager” | ”manager of tax” | “accounting manager” | “manager tax”) ~tax (”sales” | “use”) (CPA | “C.P.A.” | “certified public”) (763 | 651 | 952 | 612)

That search pulls 12 results, of which only 1 is viable (the first one).

You may be asking why I didn’t use -~job or something like that. Good question – thank you for asking.

You will find that if you add something like that that to the original search, you will actually eliminate the only viable result, which happens to be a resume on a site called resumebucket. If you clicked the link, you’ll see the word “job” in the upper right hand portion of the site where users can perform a job search.

Also, you will notice that the 1 viable resume from the original Google search has the words “job duties” in each of the candidate’s experiences. Interesting, yes?

If you’re REALLY perspicacious, you will also notice on the left hand side of the resumebucket resume result, the words “sample resumes.”

So if you decided to try and use -”resumes” or -sample, you can say “bye bye” to the only real result available in the search.

The moral of the story here is be careful before you wave the “magic wand of false positive removal” when creating your Booleans and trying to get rid of results that mention the word job, jobs, resumes, or sample. You just might be eliminating results you can actually use!

Just for kicks, let’s revisit the search, and instead of using area codes, let’s try and target zip codes in a 30 mile radius of 55401. I used zipmath to find all of the zip codes, and will leverage Google’s numrange search functionality.

(intitle:resume | inurl:resume) (”tax manager” | ”manager of tax” | “accounting manager” | “manager tax”) ~tax (”sales” | “use”) (CPA | “C.P.A.” | “certified public”) 54016..55599

Well, that gets us 11 results, but only 1 additional unique resume (the second result).

Now, reviewing this resume – it doesn’t look like he’s qualified, but we do get to learn a valuable lesson. Notice how he does NOT mention a phone number, but DOES mention a zip code. So this teaches us that it’s not a good idea to search for people in a specific location by area code or zip code – you should at least do both. Of course, you could also shoot for mention of the state or state abbreviation, but then you lose the ability to control WHERE they live in the state, which is kinda useful in most cases unless you can relocate people.

There are some sourcing professionals who say that the (intitle:resume | inurl:resume) is too limiting – I don’t really agree.  While it’s true that not everyone in the world titles their resume with “resume,” I find that if I do not shoot for (intitle:resume | inurl:resume), I get lots of non-resume junk even when I search for the word “resume.” Let’s put that idea to the test here.

-intitle:resume -inurl:resume “resume” (”tax manager” | “manager of tax” | “accounting manager” | “manager tax”) ~tax (”sales” | “use”) (CPA | “C.P.A.” | “certified public”) 54016..55599 -~job -~jobs

Notice how I used the minus sign to get rid of the results we’ve already seen that happen to have the word “resume” in the title or url. Also, without (intitle:resume | inurl:resume), I did have to resort to using the wand of false positive removal (-~job -~jobs). Yes, we could have also used -sample, -eoe, etc. I tried the -eoe and got the same number of results, and while there is a result with the word “sample” in it – be careful when using this word as a false positive removal tool.  Just as we learned earlier, there are candidates who can mention the word “sample” or “samples” in their resume.

Click here to review the results. Nothing viable, just junk and some jobs that slipped through.

Also – I could not finish this Google search section of the post without saying that this is yet another exercise in how few resumes are actually on the Internet, especially for certain types of people. In my experience, there just aren’t a lot of finance and accounting people with resumes on the Internet. If you’re feeling my pain on this one, be sure to read this post on The Internet has free resumes – SO WHAT?

LinkedIn via Google

Let’s target the Minneapolis area going for specific titles first and leverage the trusty site: command

site:linkedin.com “greater minneapolis-st. paul area” (”tax manager” | ”manager of tax” | “accounting manager” | “manager tax”) ~tax (”sales” | “use”) (CPA | “C.P.A.” | “certified public”) -inurl:jobid -inurl:find -intitle:directory -inurl:answers -~inurl:updates

Click here to view the results. Looking good!

You could go a little looser on titles to catch alternates (but you’ll pick up some garbage along the way):

site:linkedin.com “greater minneapolis-St. Paul area” “manager” ~tax (”sales” | “use”) (CPA | “C.P.A.” | “certified public”) -inurl:jobid -inurl:find -intitle:directory -inurl:answers -~inurl:updates

Click here to view the results.

Final thoughts

There are many ways to tweak those searches to find more candidates, such as using the NOT/- operator to experiment with eliminating some of the search terms and targeting others, or targeting director level tax professionals as well as tax accountants in addition to supervisors and managers to network and generate referrals, but this was a good exercise and it covered a number of best practices. Additonally, hopefully we all learned something – to be careful before wielding the NOT/- operator to get rid of false positives, because sometimes it can actually eliminate the only real results!

For anyone who made it this far and is still reading…thanks!  Seriously though – next week I will put out 2 more posts on semantic search.  I’ve been watching my stats and I am getting crazy interest in the concept so I’ll be feeding the fire next week.  Stay tuned!