Archive for October, 2008

Resumes on the Internet: Monster vs. Google

If you are a sourcer or recruiter I am sure that at some point in your career you’ve read somewhere or heard someone say how the Internet has 10X the number of candidates that can be found on the online job boards. I’ve always taken that for face value because, to be honest, it’s really [...]

Google, Internet Sourcing, Job Boards, Monster vs. Google

Artificial Intelligence Resume Matching vs. Human Cognition

Over the years, I have had the opportunity to evaluate several of the “big name” resume and job matching applications that claim to use artificial intelligence and I can say that the claim that they can find the same resumes that an “experienced recruiter” would choose is both accurate and inaccurate.

Photo: Scott Ingram Photography
From my [...]

Artificial Intelligence Matching

Fishing from the Hidden Talent Pool

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 [...]

Boolean, Hidden Talent Pools

Targeting PAST experience on LinkedIn – can it be done?

I recently had a recruiter ask me if there were any way to be able to search LinkedIn for people who have worked at a specific company in the past, but who are NOT currently working for that company.
I can see why some Sourcers and Recruiters would want to specifically target people who are not [...]

Boolean, Google, Internet Sourcing, LinkedIn, Passive Sourcing and Recruiting

Twittering for Sourcing

I recently saw a discussion on ERE started by Erika Hansen Brown on the topic of using Twitter for sourcing. I weighed in on the discussion, which can be found here: http://tinyurl.com/4q73dw
Personally, I think that Twitter is most effective when leveraged for passive talent identification and acquisition via recruiter and/or employer branding and job opening [...]

Twitter

The value of a resume database

How do you value a database? I say that the value of a database lies not in the information contained within, but in the ability of a user to extract out precisely and completely what the user needs.
When talking about the value of a company’s internal candidate database or the online job board resume databases, we [...]

Resume Sourcing

Resumes are not dead!

With the buzz I continue to see and hear surrounding Twitter, social networks, Internet sourcing (blogs, articles, etc.) and such, it’s easy to look at resumes as dull, outdated, or at least “uncool” when it comes to sourcing and recruiting. I fear there are many people who get blinded by the “shiny object” factor of each and every [...]

LinkedIn, Resume Sourcing, Twitter

The Hidden Talent Pools in every source of candidates

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 [...]

Boolean, Hidden Talent Pools

Job Boards = Bad Candidates? Don’t believe the hype.

As stated in a previous post, I continue to see well respected thought leaders in the staffing industry make claims that the value of the job boards is waning and that the quality of candidates on the job boards is low. Not long ago I weighed in on an ERE discussion in response to the [...]

Job Boards, Myths and Misconceptions

Talent Mining – what is it anyway?

By my definition, Talent Mining is a simple adaptation of Data Mining, which according to Wikipedia is the process of sorting through large amounts of data and picking out relevant information, or “the nontrivial extraction of implicit, previously unknown, and potentially useful information from data” and “the science of extracting useful information from large data [...]

Talent Mining