Diversity Sourcing

100+ Free Sourcing & Recruiting Tools, Guides, and Resources

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It’s been a LONG time coming, but I finally got around to updating my free sourcing & recruiting tools, guides and resources page where I now keep a current list of the best of my work all in one place for easy bookmarking and reference.

You can find it here on my main page:

 

Here is where you can find all of the best of my Boolean Black belt content all in one place - free sourcing and recruiting how-to guides, tools, presentations, and videos - be sure to bookmark it, and if you're feeling  friendly, tweet it, share it on LinkedIn and/or +1 it on Google Plus.  Many thanks!

 

Additionally, I thought I might as well put all of my best work all in one blog post as well – over 110 of my articles in one place for easy referencing!

My blog is a pursuit of passion and not of profit – if you’ve ever found anything I’ve written helpful to you, all I ask is that you tweet this out, share it on LinkedIn, like it on Facebook, or give this a +1 on Google.

Many thanks for your readership and support – please pay it forward to someone who can benefit.

Big Data, Analytics and Moneyball Recruiting

Big Data, Data Science and Moneyball Recruiting

The Moneyball Recruiting Opportunity: Analytics and Big Data

Human Capital Data is Sexy – and Sourcing is the Sexiest job in HR/Recruiting! 

Is Sourcing Dead? No! Here’s the Future of Sourcing

The End of Sourcing 1.0 and the Evolution of Sourcing 2.0

How to Find Email Addresses

How to Use Gmail and Rapportive to Find Almost Anyone’s Email Address

Social Discovery

2 Very Cool and Free Social Discovery Tools: Falcon and TalentBin

Talent Communities

The Often Overlooked Problem with Talent Communities

Lean / Just-In-Time Recruiting / Talent Pipelines

What is Lean, Just-In-Time Recruiting?

Lean Recruiting & Just-In-Time Talent Acquisition Part 1

Lean Recruiting & Just-In-Time Talent Acquisition Part 2

Lean Recruiting & Just-In-Time Talent Acquisition Part 3

Lean Recruiting & Just-In-Time Talent Acquisition Part 4

The Passive Candidate Pipeline Problem

Semantic Search

What is Semantic Search and How Can it Be Used for Sourcing and Recruiting?

Sourcing and Search: Man vs. Machine/Artificial Intelligence – My SourceCon Keynote

Why Sourcers Won’t Be Replaced By Watson/Machine Learning Algorithms Any Time Soon

Diversity Sourcing

How to Perform Diversity Sourcing on LinkedIn – Including Specific Boolean Search Strings

How to Use Facebook’s Graph Search for Diversity Sourcing

Social Recruiting

How to Find People to Recruit on Twitter using Followerwonk & Google + Bing X-Ray Search

Google Plus Search Guide: How to Search and Find People on Google Plus

Facebook’s Graph Search Makes it Ridiculously Easy to Find Anyone

How to Effectively Source Talent on Social Networks – It Requires Non-Standard Search Terms!

How a Recruiter Made 3 Hires on Twitter in Six Weeks!

Twitter 101 for Sourcers and Recruiters

Anti-Social Recruiting

How Social Recruiting has NOT Changed Recruiting

Social Recruiting – Beyond the Hype

What Social Recruiting is NOT

Sourcing Social Media Requires Outside the Box Thinking

Social Networking Sites vs. Job Boards

LinkedIn Sourcing and Recruiting

Sourcing and Searching LinkedIn: Beyond the Basics – SourceCon Dallas 2012

LinkedIn’s Dark Matter – Profiles You Cannot Find

How to Get a Higher LinkedIn InMail Response Rate

The Most Effective Way to X-Ray Search LinkedIn

LinkedIn Catfish: Fake Profiles, Real People, or Just Fake Photos?

LinkedIn Search: Drive it Like you Stole It – 8 Minute Video of My LinkedIn Presentation in Toronto

How to Search LinkedIn and Control Years of Experience

How to Quickly and Effectively Grow Your LinkedIn Network

How to View the Full Profiles of our 3rd Degree Connections on LinkedIn for Free

How to Find and Identify Active Job Seekers on LinkedIn

LinkedIn Profile Search Engine Optimization

Free LinkedIn Profile Optimization and Job Seeker Advice

Do Recruiters Ruin LinkedIn?

The 50 Largest LinkedIn Groups

How to See Full Names of 3rd Degree LinkedIn Connections for Free

How I Search LinkedIn to Find People

LinkedIn’s Undocumented Search Operator

Does LinkedIn Offer Recruiters any Competitive Advantage?

Have You Analyzed the Value of Your LinkedIn Network?

Where Do YOU Rank In LinkedIn Search Results?

What is the Total Number of LinkedIn Members?

Beware When Searching LinkedIn By Company Name

LinkedIn Sourcing Challenge

How to Search for Top Students and GPA’s on LinkedIn

What’s the Best Way to Search LinkedIn for People in Specific Industries?

18 LinkedIn Apps, Tools and Resources

LinkedIn Search: What it Could be and Should be

How to Search Across Multiple Countries on LinkedIn

Private and Out of Network Search Results on LinkedIn

How to “Unlock” and view “Private” LinkedIn Profiles

Searching LinkedIn for Free – The Differences Between Internal and X-Ray Searching

Sourcing and Boolean Search

Basic Boolean Search Operators and Query Modifiers Explained

How to Find Resumes On the Internet with Google

Challenging Google Resume Search Assumptions

Don’t be a Sourcing Snob

The Top 15 Talent Sourcing Mistakes

Why Boolean Search is Such a Big Deal in Recruiting

How to Become a World Class Sourcer

Enough with the Exotic Sourcing Already – What’s Practical and What Works

Sourcing is So Much More than Tips, Tricks, Hacks, and Google

How to Find, Hire, Train, and Build a Sourcing Team – SourceCon 2013

How to Use Excel to Automatically Build Boolean Search Strings

The Current and Future State of Sourcing

Why So Many People Stink at Searching

Is your ATS a Black Hole or a Diamond Mine?

How to Find Bilingual Professionals with Boolean Search Strings

How to Best Use Resume Search Aggregators

How to Convert Quotation Marks in Microsoft Word for Boolean Search

Boolean Search, Referral Recruiting and Source of Hire

The Critical Factors Behind Sourcing ROI

What is a “Boolean Black Belt?”

Beyond Basic Boolean Search: Proximity and Weighting

Why Sourcing is Superior to Posting Jobs for Talent

The Future of Sourcing and Talent Identification

Sourcing is an Investigative and Iterative Process

Beyond Boolean Search: Human Capital Information Retrieval

Do you Speak Boolean?

Is Recruiting Top Talent Really Your Company’s Top Priority?

Sourcing is NOT an Entry Level Function

Boolean Search Beyond Google

The Internet Has Free Resumes. So What?

How to Search Spoke, Zoominfo and Jigsaw for Free

Job Boards vs. Social Networking Sites

What to Do if Google Thinks You’re Not Human: the Captcha

What if you only had One Source to Find Candidates?

Passive Recruiting is a Myth – It Doesn’t Exist

Sourcing: Separate Role or Integrated Function?

The #1 Mistake in Corporate Recruiting

How I Learned What I Know About Sourcing

Resumes Are Like Wine – They Get Better with Age!

Why Do So Many ATS Vendors Offer Such Poor Search Functionality?

Do Candidates Really Want a Relationship with their recruiter?

Recruiting: Art or Science?

What to Consider When Creating or Selecting Effective Sourcing Training – SourceCon NYC

The Sourcer’s Fallacy

Sourcing Challenge – Monster vs. Google – Round 1

Sourcing Challenge – Monster vs. Google – Round 2

Do You Have the Proper Perspective in Recruiting?

Are You a Clueless Recruiter?

Job Boards and Candidate Quality – Challenging Popular Assumptions

When it Comes to Sourcing – All Sources Are Not Created Equal

Boolean Search String Experiments

Boolean Search String Experiment #1

Boolean Search String Experiment #1 Follow Up

Boolean Search String Experiment #2

 

Diversity Sourcing is a Breeze with Facebook Graph Search

Posted by | Diversity Sourcing | 5 Comments

 

In some respects, Facebook’s Graph Search has literally changed the game when it comes to diversity and inclusion with regard to sourcing.

If you don’t already have access to Graph Search, you may not be aware just how easy it is to leverage diversity criteria such as gender, race and ethnicity.

How easy is it?

I’ll show you how.

Gender Sourcing with Facebook Graph Search

Imagine being asked to find and identify as many female _______________ (accountants, project managers, software engineers, etc.) who currently work at a particular company or any company.

Think about it.

Without Graph Search, how would you go about accomplishing this goal?

It’s no easy task. I know several people who’ve worked at some of the top software companies in the world who have had to do some crazy search gymnastics in the past  in order to even somewhat successfully identify a small portion of female software engineers at target companies in order to diversify their software development talent.

With Graph Search, it’s now simply a matter of asking Facebook for all of the female software engineers or any role you’re targeting at any company.

For example:

 

 

As you can see, sourcing for gender diversity is so easy that Facebook’s Graph Search has essentially rendered it a non-issue, at least when it comes to searching for people by title and/or company.

I have to imagine that this has already been done by sourcers and recruiters at Facebook, Microsoft, LinkedIn, etc.

If not – hello!

Not that it wouldn’t, but this approach also works just as well if you were looking for female engineers in Germany, or any position in any country. Read More

Diversity Sourcing: Boolean Search Strings for LinkedIn

Posted by | Diversity Sourcing | 6 Comments

 

 

When it comes to diversity sourcing and recruiting, the first thing that comes to mind for many people is posting jobs on diversity sites and in diverse groups. However, as I have written about many times, posting jobs is an intrinsically limited talent acquisition strategy and it fails to expose you to the “deep end” of the talent pool.

At best, posting jobs can only give you access to approximately 30% of the total talent pool – those active and casual job seekers who will actually take the time to run a search for jobs and apply to an opening.

How can you access the other 70%?

Proactive sourcing, of course!

I’ve spoken at a few conferences this year (HCI, LinkedIn Talent Connect, SourceCon) in which I’ve detailed some Boolean search strings for diversity sourcing on LinkedIn, and I’ve had several requests for the specific searches I’ve demonstrated.

While the search strings I’ve used in my presentations are already posted on the conference websites, I thought it would be a good idea to create and release some new and improved diversity sourcing search strings here for quick and easy access to some “starter” queries.

However, it’s important to know that what I’m publishing is the tip of the iceberg. I have no idea what your particular diversity sourcing need might be, or even what country you’re sourcing in – it’s up to you to adapt what you see here to your specific needs.

While I know some folks will be happy to simply snag the strings, what I really want my readers to get from this post is an understanding of and appreciation for the critical underlying thought process necessary for any successful sourcing endeavor, let alone diversity sourcing.

When it comes to information retrieval, if you can conceive it, you can almost always achieve it – including diversity sourcing – and there are often many different ways to achieving your search goals.

The “magic” of search strings does not lie in the Boolean logic or site specific search syntax, nor does it exist in the keywords and phrases you search for – the true power of search lies within your own mind.

What is Your Diversity Need?

So let’s get back to basics for just a second.

When you’re creating and executing Boolean search strings for talent discovery, you’re really performing information retrieval.

Information retrieval is the activity of obtaining information resources relevant to an information need.

An information retrieval process begins when you enter a query into an information system (e.g., databases, the Internet, social networks, etc.), and queries are simply formal statements of information needs.

So when it comes to diversity sourcing, what’s your information need?

This seems like such a simple question, but I honestly don’t think many people begin their sourcing efforts with this in mind.

LinkedIn Diversity Sourcing for Gender: How to Find LinkedIn Profiles of Women

Let’s say you need to find women with specific skills and experience.

Your first step is to think about what might show up predominantly, and ideally only on profiles of women.

What comes to mind for many people includes searching for women’s groups, sororities, women-only sports, and women’s colleges, including searching explicitly for the words “Women,” “Women’s,” and “female” for an exploratory search into all of the various women’s groups.

However, many of these approaches are extremely narrow in scope and low in quantity of results (e.g. “Society of Women ____,” “Association of Female _____,” etc.). or diluted with false positives. For example, searching for (“women’s” OR womens) on LinkedIn yields almost 1M results in the U.S., but scrolling through the pages, you can see that there are a fair amount of profiles of men that are returned, and that’s to be expected given that the search terms aren’t exclusive to women’s profiles – they can show up on men’s profiles too.

As an example of something that is less obvious, outside of the box, and more exclusive to women’s profiles would be something I’ve hypothesized, tested, and confirmed on LinkedIn – searching for (her OR she).

For those who haven’t seen me present on that search before – do you know why it returns LinkedIn profiles of women?

 

 

It works because “her” and “she” can be mentioned in the summary and recommendation sections of a women’s profiles.

 

 

You would not likely find many LinkedIn profiles of men mentioning “her ” or “she,” although they do exist. (sourcing challenge – do you know how to find them exclusively?)

Yes, (her OR she) is a bit clever, and yes, I’m a bit proud of the discovery, but it clearly demonstrates the fact that all anyone needs to do is *think* about what terms that could be searched for that would be relatively unique to the people you are trying to find and test any ideas you come up with to verify.

While (her OR she) “works” in that it returns predominantly women-only results,  it returns less than 400,000 profiles in the U.S. – so certainly not a big slice of all of the women on LinkedIn.

How could we do better?

Let’s try another more traditional search approach – women’s universities and colleges.

(“Agnes Scott College” OR “Alverno College” OR “Barnard College” OR “Bay Path College” OR “Bennett College” OR “Brenau University” OR “Brescia University College” OR “Bryn Mawr College” OR “Carlow College” OR “Cedar Crest College” OR “Chatham University” OR “College of New Rochelle, The” OR “College of Saint Benedict” OR “College of Saint Elizabeth” OR “College of Saint Mary” OR “Columbia College” OR “Converse College” OR “Cottey College” OR “Douglass Residential College of Rutgers University” OR “Hollins University” OR “Judson College” OR “Mary Baldwin College” OR “Meredith College” OR “Midway College” OR “Mills College” OR “Moore College of Art & Design” OR “Mount Holyoke College” OR “Mount Mary College” OR “Mount St. Mary’s College” OR “Notre Dame of Maryland University” OR “Pine Manor College” OR “Russell Sage College” OR “St. Catherine University” OR “Saint Joseph College” OR “Saint Mary-of-the-Woods College” OR “Saint Mary’s College” OR “Salem College” OR “Scripps College” OR “Simmons College” OR “Smith College” OR “Spelman College” OR “Stephens College” OR “Sweet Briar College” OR “Trinity Washington University” OR “Wellesley College” OR “Wesleyan College” OR “Wilson College” OR “Women’s College”)

 

 

That’s less than my (her OR she) search, although of course you could use the -/NOT operator to make each search mutually exclusive and to eliminate overlap.

Let’s try a sorority search:

(“Alpha Chi Omega” OR “Alpha Delta Chi” OR “Alpha Delta Pi” OR “Alpha Epsilon Omega” OR “Alpha Epsilon Phi” OR “Alpha Gamma Delta” OR “Alpha Kappa Alpha” OR “alpha Kappa Delta Phi” OR “Alpha Phi Gamma” OR “Alpha Phi” OR “Alpha Pi Omega” OR “Alpha Pi Sigma” OR “Alpha Rho Lambda” OR “Alpha Sigma Alpha” OR “Alpha Sigma Kappa” OR “Alpha Sigma Omega” OR “Alpha Sigma Rho” OR “Alpha Sigma Tau” OR “Alpha Xi Delta” OR “Ceres” OR “Chi Omega” OR “Chi Upsilon Sigma” OR “Delta Chi Lambda” OR “Delta Delta Delta” OR “Delta Gamma” OR “Delta Gamma Pi” OR “Delta Kappa Delta” OR “Delta Phi Epsilon” OR “Delta Phi Lambda” OR “Delta Phi Mu” OR “Delta Phi Omega” OR “Delta Psi Delta” OR “Delta Sigma Chi” OR “Delta Sigma Theta” OR “Delta Tau Lambda” OR “Delta Xi Nu” OR “Delta Xi Phi” OR “Delta Zeta” OR “Gamma Alpha Omega” OR “Gamma Eta” OR “Gamma Phi Beta” OR “Gamma Phi Omega” OR “Gamma Rho Lambda” OR “Gamma Sigma Sigma” OR “Kappa Alpha Theta” OR “Kappa Beta Gamma” OR “Kappa Delta Chi” OR “Kappa Delta Phi” OR “Kappa Delta” OR “Kappa Kappa Gamma” OR “Kappa Phi Gamma” OR “Kappa Phi Lambda” OR “Kappa Phi Zeta” OR “Lambda Pi Chi” OR “Lambda Pi Upsilon” OR “Lambda Psi Delta” OR “Lambda Tau Omega” OR “Lambda Theta Alpha” OR “Lambda Theta Nu” OR “Mu Sigma Upsilon” OR “Omega Phi Beta” OR “Omega Phi Chi” OR “Phi Beta Chi” OR “Phi Mu” OR “Phi Sigma Rho” OR “Phi Sigma Sigma” OR “Pi Beta Phi” OR “Pi Lambda Chi” OR “Sigma Alpha Epsilon Pi” OR “Sigma Alpha Iota” OR “Sigma Delta Tau” OR “Sigma Gamma Rho” OR “Sigma Iota Alpha” OR “Sigma Kappa” OR “Sigma Lambda Alpha” OR “Sigma Lambda Gamma” OR “Sigma Lambda Upsilon” OR “Sigma Omega Nu” OR “Sigma Omega Phi” OR “Sigma Omicron Pi” OR “Sigma Phi Kappa” OR “Sigma Phi Omega” OR “Sigma Pi Alpha” OR “Sigma Psi Zeta” OR “Sigma Sigma Rho” OR “Sigma Sigma Sigma” OR “Tau Theta Pi” OR “Theta Nu Xi” OR “Theta Phi Alpha” OR “Zeta Phi Beta” OR “Zeta Tau Alpha”)

 

 

Not bad – now we’re up to a little over 650,000 profiles, which is actually much higher than simply searching for the term “sorority.”

However, instead of trying these more traditional search ideas, let’s try to think of the single most inclusive way of finding women on LinkedIn.

Well, what’s more inclusive than first names?

Of course you can search by groups, sororities, and sports, but you can find a larger portion of people by searching by first name.

You might be asking, “How can I possibly create and run a search by all of the female names – there must be thousands?!?!”

Yes, there are thousands, and no, we can’t practically search for all of them – and certainly not in a single search.

However, you can go to a number of websites and find the most popular female names and search for those, which will statistically yield a significant portion of the women represented on LinkedIn. In the U.S., we can use the Social Security Administration website, which conveniently lets you search for the top 200 most common first names for girls and boys by decade.

For this post, I copied the top 200 female first names from the 1960′s, 1970′s, and 1980′s into Excel, sorted them alphabetically, then removed the duplicates, yielding the top 317 female names from those 3 decades. Then I used those names to create a Boolean OR statement, which looks like this:

(Abigail OR Adrienne OR Aimee OR Alexandra OR Alexis OR Alice OR Alicia OR Alisha OR Alison OR Allison OR Alyssa OR Amanda OR Amber OR Amy OR Ana OR Andrea OR Angel OR Angela OR Angelica OR Angie OR Anita OR Ann OR Anna OR Anne OR Annette OR April OR Ashlee OR Ashley OR Audrey OR Autumn OR Barbara OR Becky OR Belinda OR Beth OR Bethany OR Betty OR Beverly OR Bonnie OR Brandi OR Brandy OR Brenda OR Brianna OR Bridget OR Brittany OR Brittney OR Brooke OR Caitlin OR Candace OR Candice OR Carla OR Carmen OR Carol OR Caroline OR Carolyn OR Carrie OR Casey OR Cassandra OR Cassie OR Catherine OR Cathy OR Charlene OR Charlotte OR Chelsea OR Cheryl OR Christie OR Christina OR Christine OR Christy OR Cindy OR Claudia OR Colleen OR Connie OR Courtney OR Cristina OR Crystal OR Cynthia OR Dana OR Danielle OR Darlene OR Dawn OR Deanna OR Debbie OR Deborah OR Debra OR Denise OR Desiree OR Diana OR Diane OR Dominique OR Donna OR Doris OR Dorothy OR Ebony OR Eileen OR Elaine OR Elizabeth OR Ellen OR Emily OR Erica OR Erika OR Erin OR Evelyn OR Felicia OR Frances OR Gail OR Gina OR Glenda OR Gloria OR Gwendolyn OR Hannah OR Heather OR Heidi OR Helen OR Holly OR Jackie OR Jaclyn OR Jacqueline OR Jaime OR Jamie OR Jane OR Janet OR Janice OR Jasmine OR Jean OR Jeanette OR Jeanne OR Jenna OR Jennifer OR Jenny OR Jessica OR Jill OR Jillian OR Jo OR Joan OR Joann OR Joanna OR Joanne OR Jodi OR Jody OR Jordan OR Joy OR Joyce OR Judith OR Judy OR Julia OR Julie OR Kara OR Karen OR Kari OR Karla OR Katelyn OR Katherine OR Kathleen OR Kathryn OR Kathy OR Katie OR Katrina OR Kayla OR Kelli OR Kellie OR Kelly OR Kelsey OR Kendra OR Kerri OR Kerry OR Kim OR Kimberly OR Krista OR Kristen OR Kristi OR Kristie OR Kristin OR Kristina OR Kristine OR Kristy OR Krystal OR Lacey OR Latasha OR Latoya OR Laura OR Lauren OR Laurie OR Leah OR Leslie OR Linda OR Lindsay OR Lindsey OR Lisa OR Loretta OR Lori OR Lorraine OR Lynn OR Mallory OR Mandy OR Marcia OR Margaret OR Maria OR Marie OR Marilyn OR Marissa OR Martha OR Mary OR Maureen OR Meagan OR Megan OR Meghan OR Melanie OR Melinda OR Melissa OR Melody OR Meredith OR Michele OR Michelle OR Mindy OR Miranda OR Misty OR Molly OR Monica OR Monique OR Morgan OR Nancy OR Natalie OR Natasha OR Nichole OR Nicole OR Nina OR Norma OR Olivia OR Pam OR Pamela OR Patricia OR Patty OR Paula OR Peggy OR Penny OR Phyllis OR Priscilla OR Rachael OR Rachel OR Rebecca OR Rebekah OR Regina OR Renee OR Rhonda OR Rita OR Roberta OR Robin OR Robyn OR Rose OR Ruth OR Sabrina OR Sally OR Samantha OR Sandra OR Sandy OR Sara OR Sarah OR Shannon OR Shari OR Sharon OR Shawna OR Sheena OR Sheila OR Shelia OR Shelley OR Shelly OR Sheri OR Sherri OR Sherry OR Sheryl OR Shirley OR Sonia OR Sonya OR Stacey OR Stacie OR Stacy OR Stefanie OR Stephanie OR Sue OR Susan OR Suzanne OR Sylvia OR Tabitha OR Tamara OR Tami OR Tammie OR Tammy OR Tanya OR Tara OR Tasha OR Taylor OR Teresa OR Terri OR Terry OR Theresa OR Tiffany OR Tina OR Toni OR Tonya OR Tracey OR Traci OR Tracie OR Tracy OR Tricia OR Valerie OR Vanessa OR Veronica OR Vicki OR Vickie OR Victoria OR Virginia OR Wanda OR Wendy OR Whitney OR Yolanda OR Yvette OR Yvonne)

Then I put that OR statement into the first name field on LinkedIn.

Does it work?

Yes.

 

 

It also works in LinkedIn Recruiter – sometime this year LinkedIn apparently removed the character limits in the search fields.

So now we have almost 22,000,000 profiles of U.S. women on LinkedIn, give or take some false positives (and there are some).

How big of a slice does this represent in relation to all of the U.S. women on LinkedIn?

Let’s do a little math.

LinkedIn’s website now claims 187M members worldwide, and that “Sixty-three percent of LinkedIn members are located outside of the United States, as of September 30, 2012.”

That means that 37% of LinkedIn members are located in the United States, and that there are approximately 69M U.S. LinkedIn members.

Assuming that this Forbes article is accurate in reporting that LinkedIn has an even ratio of men and women (51%/49%), then there should be approximately 33.8M female profiles on LinkedIn.

So the ~21.8M results from my search of the 317 first names I ran above should be capturing approximately 65% of all of the U.S. women on LinkedIn (21.8/33.8).

Not bad for a single search, and massively more inclusive of any other way of searching for women on LinkedIn (groups, colleges, sororities, etc.)!

Coincidentally, the search I’ve been using in my conference presentations included 313 of the most common female names from a baby names website, and it returns 21,669,184 results vs. the 21,791,355 from my 3-decade SSA-based search.

Of course, if your diversity need is to find male candidates, you can do the exact same thing as above, using the most common male names.

LinkedIn Diversity Sourcing: Racial and Ethnic Diversity

Is your information need to find racially or ethnically diverse candidates?

Well then, all you have to do is think about what might show up predominantly, and ideally only on profiles of people representing specific racial and ethnic groups.

What comes to mind for many people includes searching for groups, fraternities and sororities, and historically black colleges and universities.

Speaking of which, here is a search for 105 HCBU’s:

(“Alabama A&M University” OR “Alabama State University” OR “Albany State University” OR “Alcorn State University” OR “Allen University” OR “University of Arkansas at Pine Bluff” OR “Arkansas Baptist College” OR “Barber-Scotia College” OR “Benedict College” OR “Bennett College” OR “Bethune-Cookman University” OR “Bishop State Community College” OR “Bluefield State College” OR “Bowie State University” OR “Central State University” OR “Cheyney University of Pennsylvania” OR “Claflin University” OR “Clark Atlanta University” OR “Clinton Junior College” OR “Coahoma Community College” OR “Concordia College, Selma” OR “Coppin State University” OR “Delaware State University” OR “Denmark Technical College” OR “Dillard University” OR “University of the District of Columbia” OR “Edward Waters College” OR “Elizabeth City State University” OR “Fayetteville State University” OR “Fisk University” OR “Florida A&M University” OR “Florida Memorial University” OR “Fort Valley State University” OR “Gadsden State Community College” OR “Grambling State University” OR “Hampton University” OR “Harris-Stowe State University” OR “Hinds Community College at Utica” OR “Howard University” OR “Huston-Tillotson University” OR “Interdenominational Theological Center” OR “J. F. Drake State Technical College” OR “Jackson State University” OR “Jarvis Christian College” OR “Johnson C. Smith University” OR “Kentucky State University” OR “Knoxville College” OR “Lane College” OR “Langston University” OR “Lawson State Community College” OR “LeMoyne-Owen College” OR “Lewis College of Business” OR “Lincoln University” OR “Lincoln University of Missouri” OR “Livingstone College” OR “University of Maryland Eastern Shore” OR “Meharry Medical College” OR “Miles College” OR “Mississippi Valley State University” OR “Morehouse College” OR “Morehouse School of Medicine” OR “Morgan State University” OR “Morris Brown College” OR “Morris College” OR “Norfolk State University” OR “North Carolina A&T State University” OR “North Carolina Central University” OR “Oakwood University” OR “Paine College” OR “Paul Quinn College” OR “Philander Smith College” OR “Prairie View A&M University” OR “Rust College” OR “Saint Paul’s College” OR “Savannah State University” OR “Selma University” OR “Shaw University” OR “Shorter College” OR “Shelton State Community College” OR “South Carolina State University” OR “Southern University at New Orleans” OR “Southern University at Shreveport” OR “Southern University and A&M College” OR “Southwestern Christian College” OR “Spelman College” OR “St. Augustine’s College” OR “St. Philip’s College” OR “Stillman College” OR “Talladega College” OR “Tennessee State University” OR “Texas College” OR “Texas Southern University” OR “Tougaloo College” OR “Trenholm State Technical College” OR “Tuskegee University” OR “University of the Virgin Islands” OR “Virginia State University” OR “Virginia Union University” OR “Virginia University of Lynchburg” OR “Voorhees College” OR “West Virginia State University” OR “Wilberforce University” OR “Wiley College” OR “Winston-Salem State University” OR “Xavier University of Louisiana”)

While people have been leveraging HCBU’s for years in their diversity sourcing efforts, unlike most (all?) ATS’s, resume databases, and Internet search engines, LinkedIn is the only place that I am aware of that can handle 3,000+ character Boolean search strings to allow you to search for all of them at once (thank you LinkedIn!).

 

 

While anyone who performs diversity sourcing in the U.S. is familiar with HCBU’s, not everyone knows that you can also search for colleges and universities that have a high percentage of other racial or ethnic groups, such as historically Native American colleges and universitieshere’s the search on LinkedIn – it all comes down to your specific need.

If you’d like to take the fraternity/sorority approach, here is a LinkedIn search for African American fraternities and sororities.

(“Sigma Pi Phi” OR “Alpha Phi Alpha” OR “Kappa Alpha Psi” OR “Omega Psi Phi” OR “Phi Beta Sigma” OR “Sigma Rhomeo” OR “Wine Psi Phi” OR “Iota Phi Theta” OR “Phi Delta Psi” OR “Delta Psi Chi” OR “Beta Phi Pi” OR “MALIK Fraternity” OR “Sigma Phi Rho” OR “Phi Rho Eta” OR “Gamma Psi Beta” OR “Alpha Kappa Alpha” OR “Delta Sigma Theta” OR “Zeta Phi Beta” OR “Sigma Gamma Rho” OR “Phi Delta Kappa” OR “Iota Phi Lambda” OR “Eta Phi Beta” OR “Gamma Phi Delta”)

Depending on need, you can also construct queries for Asian American, Latino, LGBT, and other cultural interest fraternities and sororities.

As with all information retrieval efforts, it comes down to your specific information need and discovering ways of achieving those needs.

LinkedIn Diversity Sourcing: Surname Search

Speaking of specific diversity sourcing needs, you may be able to experiment with searching for last names to achieve your diversity sourcing goals.

Just as a quick and random example, here is search for the top 100 Chinese surnames:

(Lǐ OR Wáng OR Zhāng OR Liú OR Chén OR Yáng OR Zhào OR Huáng OR Zhōu OR Wú OR Xú OR Sūn OR Hú OR Zhū OR Gāo OR Lín OR Hé OR Guō OR Mǎ OR Luó OR Liáng OR Sòng OR Zhèng OR Xiè OR Hán OR Táng OR Féng OR Yú OR Dǒng OR Xiāo OR Chéng OR Cáo OR Yuán OR Dèng OR Xǔ OR Fù OR Shěn OR Zēng OR Péng OR Lǚ OR Sū OR Lú OR Jiǎng OR Cài OR Jiǎ OR Dīng OR Wèi OR Xuē OR Yè OR Yán OR Yú OR Pān OR Dù OR Dài OR Xià OR Zhōng OR Wāng OR Tián OR Rén OR Jiāng OR Fàn OR Fāng OR Shí OR Yáo OR Tán OR Shèng OR Zōu OR Xióng OR Jīn OR Lù OR Hǎo OR Kǒng OR Bái OR Cuī OR Kāng OR Máo OR Qiū OR Qín OR Jiāng OR Shǐ OR Gù OR Hóu OR Shào OR Mèng OR Lóng OR Wàn OR Duàn OR Zhāng OR Qián OR Tāng OR Yǐn OR Lí OR Yì OR Cháng OR Wǔ OR Qiáo OR Hè OR Lài OR Gōng OR Wén)

 

 

You could of course combine this approach with one or more of the gender diversity Boolean search strings if that would help you achieve your diversity sourcing goals.

Final Thoughts on LinkedIn Diversity Sourcing

To be sure, what I’ve demonstrated here has some obvious limitations and is far from perfect, but it does effectively illustrate that you do have some creative proactive sourcing options for underrepresented gender and racial/ethnic groups in your organization, allowing you to move beyond relying solely on posting jobs and hoping to get qualified (and diverse!) applicants from active candidates.

Proactive diversity sourcing has the distinct benefit of giving you access to the deeper end of the talent pool – those people (typically more than 2/3rds of any given population) who aren’t actively seeking employment and thus cannot be reached through any form of job advertisement, no matter where you post or share it.

Ultimately, what I really wanted to accomplish by writing this article was to get you thinking a little bit differently when it comes to diversity sourcing and sourcing in general. Effective sourcing, diversity or otherwise, isn’t about Boolean search strings – it’s about critical thinking and always seeking to step outside of the box to find ways to meet your information needs.

As an added bonus for reading this entire post, here is a LinkedIn search of the top 200 most popular female first names in the United States from the 1950′s, 60′s, 70′s, and 80′s de-duped to the most popular 354 names from those decades, which captures the 23-62 year old demographic, which nearly covers the entire span of LinkedIn’s strongest representation.

 

 

 

 

One Job Board to Rule Them All? Hint: It’s not Facebook.

Posted by | Diversity Sourcing, Facebook, Job Boards, Job Search | 18 Comments

Like many people in HR/recruiting, I recently read about Facebook making the jump into offering searchable jobs.

What took them so long anyway?

Apparently, Facebook is planning to launch its own job board later this summer, and the board will aggregate the job postings of third-party providers, making them available for search by Facebook users.

This comes after Facebook announced late last year that they would be entering into a partnership with the U.S. Labor Department to provide job-hunting resources to explore and develop systems where jobs can be posted and delivered “virally” through Facebook at no charge.

Some people think that Facebook offering job board functionality will positively affect the U.S. economy and job marketplace.

No offense to Facebook, but I’m happy to say we don’t need them to launch a job board to help put America to work.

I believe there is something that the United States government (or any country’s government, for that matter) can do to facilitate putting more people to work, without the help of any other site or company, let alone Facebook. Read More

LinkedIn User Demographics and Visitor Statistics 2011

Posted by | Analytics, Diversity Sourcing, LinkedIn | 19 Comments

Would you like to know more about LinkedIn’s user demographics, as well as LinkedIn’s visitor statistics broken down by country, city, and state?

If so, you’ve come to the right place!

After patiently waiting for a whole year since my last post on LinkedIn statistics, I’m excited to bring you LinkedIn’s latest user demographics and visitor statistics for 2011.

In this post, I will compare the data I presented in September 2010 to the data I just pulled from Quantcast.

Quantcast is used by 9 of the top 10 media agencies because they quite accurately quantify Internet audiences.

While some sites are not directly measured and only have estimated data at this time (such as Facebook and Twitter), LinkedIn is fully “quantified.”

LinkedIn_Quantcast_Directly_Measured_Data

In other words, Quantcast directly measures LinkedIn’s visitors – which gives us great information and some very interesting insights!

Read on to see the following LinkedIn data:

  • Global monthly visitors
  • Global monthly visits
  • Visits per person
  • Pageviews per person
  • Visit frequency
  • Business activity
  • User demographics (gender, age, ethnicity, income, education level)
  • Monthly visitors by country
  • Monthly visitors by city (global)
  • Monthly visitors by state (U.S.) Read More

LinkedIn Demographics and Visitor Statistics

Posted by | Analytics, Diversity Sourcing, LinkedIn | 23 Comments

LinkedIn_Quantcast_Daily_Visits
If you have ever been curious about LinkedIn’s demographics, as well as LinkedIn’s visitor statistics broken down by country, city, and state, you’ve come to the right place! A while ago I stumbled across a very interesting and powerful website called Quantcast, which is used by 9 of the top 10 media agencies because they quite accurately quantify Internet audiences.

While some sites are not directly measured and only have estimated data at this time (such as Facebook and Twitter), LinkedIn is fully “quantified.” In other words, Quantcast directly measures LinkedIn’s visitors – which gives us some very interesting insights.

LinkedIn_Quantcast_Directly_Measured_Data Read More

How to Search LinkedIn for Diversity Sourcing

Posted by | Diversity Sourcing, LinkedIn | 7 Comments

People Puzzle SmallIf you ever have a need to perform diversity sourcing, I’m going to show you a trick on LinkedIn that goes beyond the obvious and “everyone’s doing it” methods of searching for fraternities, sororities, specific universities, and of course groups, societies and associations.

Let’s say you were in need of identifying people with specific skills and experience that are also women (software engineers, CFO’s, etc.), and you’ve already tried the standard methods of identifying them. One tactic some people and organizations utlize is searching for common first names for women. However, with most search engines, you’re limited in the size of the search string you can run (sometimes as few as 100 characters!), so you can’t search for many names with a single search. Plus, limiting yourself to only the most common first names is, well…limiting.

While I’ve written about the fact that LinkedIn’s search fields appear bottomless (I have yet to find a limit to the number of characters/terms that can be entered and searched for), I don’t know of many people who try and take advantage of LinkedIn’s limitless search fields.

See where I might be going here? Read More