Building a Solid Foundation with the Boolean Black Belt | Talent Connect Vegas 2013

LinkedIn Sourcing
Building a Solid Foundation
Glen Cathey
SVP, Strategic Talent Acquisition and Innovation

Kforce
Author, booleanblackbelt.com

Who is this guy?
 SVP, Talent Acquisition Strategy and Innovation
– Architect Kforce's sourcing and recruiting strategy for 1300+ recruiters

 16+ years recruiting
– Professional staffing
 I.T., Engineering F&A, Healthcare I.T., Federal, Clinical Research

– Global RPO delivery

 Sourcing/Recruiting Blogger
– www.booleanblackbelt.com
– 15K+ unique visitors per week from 110+ countries

 Speaker
–
–
–
–

6X LinkedIn Talent Connect speaker (US, CA, UK)
6X SourceCon speaker
2X Australasian Talent Conference (Sydney & Melbourne)
2X TruLondon

 LinkedIn Certified, Top 19 most connected on LinkedIn globally

Adaptive
search, maximum
inclusion & strategic
exclusion
Query logic/syntax and other
search methods (e.g., faceted
search)

The nature and limitations of the text you're
interrogating, the behavior of the people
generating the text and of your competitors, and
core information retrieval

What is the value of…

Beinecke Library, Yale University

“The flood of data means more noise (i.e., irrelevant/useless
information) but not necessarily more signal (i.e., relevant results)”
Often we expect too much of computers and not enough of ourselves.
People blame systems “when they should be asking better questions.”

Nate Silver
•
•
•

Principal, FiveThirtyEight
Author, The Signal and the Noise:
Why So Many Predictions Fail-but
Some Don't
Correctly predicted 50 out of 50 states
in the 2012 presidential election

Title only

Source: John Zappe, SourceCon

Title only

Source: Suzanne Chadwick, The Social Recruiter blog

Competitive Advantage?
 LinkedIn members performed nearly 5.7 billion
professionally-oriented searches on the
platform in 2012.

 LinkedIn's corporate talent solutions are used

by 90 of the Fortune 100 companies.

 (How) Are your searches any different than
anyone else's?

Understanding Search and Retrieval

You can only get what you search for

Every search you run includes
and excludes qualified people

Who are you finding?

 Do the best people
necessarily mention your
search terms?

 Common search terms may
be "overvalued" and yield no
competitive advantage

How many results…
do you review per search?
can you review per search?
should you review?

9,000+ mobile developers in
the Bay area

Nearly 16,000 accountants

Understand your target talent pool…
Why does the average person join
LinkedIn?

What does the average person use
LinkedIn for?

Why would someone who isn't looking
for a job fill out their LinkedIn profile
as they would a resume?
The answers to these questions are
critical in developing your search
strategies

Understand your target talent pool…

Understand the competition…
Your competitors and other companies
are using LinkedIn Recruiter to look for
the same people you are

Are your searches any different than your
competitors?

Are you processing search results in the
same way as your competitors?

Are you finding and fighting over the
same talent pool?

Before & Beyond Boolean…
 Information Retrieval is the science of searching for
documents, information within documents, and searching relational
databases and the Internet.

 An information retrieval process begins when a user enters a query into
a system.

 Queries are formal statements of information needs.

 As Nate Silver suggests, you should learn to ask better questions.

LinkedIn's working on understanding your
searches…

However…
 No system or database:
– will ever "know" what you want or need
– can determine "relevance"

 Relevance
– is the ability (as of an information retrieval
system) to retrieve material that satisfies
the needs of the user*

When it comes to Human–Computer Information
Retrieval (HCIR)*, it is necessary for people to take responsibility
for search results by expending cognitive energy

Source: Gary Marchionini

AND
 Mandatory inclusion
 Typically reduces results
 Unnecessary to type in LinkedIn
 Use for required skills

"Objective C" Cocoa xcode iOS "software engineer"

OR

 Inclusive – "at least one of"
 Use parentheses to group terms – (A OR B OR C) (D OR E OR F)
 Typically increases results

OR
Use #1: searching for (un)related desired skills

(MapReduce OR ETL OR SAS OR SPSS OR Sqoop
OR Pig OR NoSQL OR Hive OR Hbase OR Flume)

OR
Use #2: searching for term variations and synonymous, related and
relevant terms
(develop OR developer OR developed OR developing OR
development OR develops)

(tax OR taxes OR taxation)
(VP OR "V.P." OR SVP OR "S.V.P." OR "Vice President")
("software engineer" OR developer OR programmer)
("Objective C" OR ObjectiveC OR "Objective-C")
(Deloitte OR PwC OR PricewaterhouseCoopers OR "Price
Waterhouse" OR "Pricewaterhouse" OR "E&Y" OR Ernst OR
KPMG)

NOT/ Exclusionary
 Almost always decreases results
 Can also use the minus sign

Use #1 Reducing/eliminating false positives

"Objective C" Cocoa xcode iOS "software engineer"
-(recruiter OR executive search OR recruiting)

#2 Mutually exclusive search progressions
Search #1 iOS "Objective C" Cocoa iPhone xcode
Search #2 iOS "Objective C" Cocoa iPhone -xcode
Search #3 iOS "Objective C" Cocoa -iPhone xcode
iOS

Cocoa

"Objective C"

"iPhone

xcode

Query Modifiers

Search Field Size

 Quotation Marks " "

~3,000+ characters per field

 Exact phrase searching

 Parentheses ( )
 Grouping OR statements

Not Supported
 Asterisk *
 Root word/stemming

 Near
 Proximity search

(XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR
XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR
XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX OR XXXXXXX)

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 Annie OR April OR Arlene 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 Carole 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 Constance 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 Delores OR Denise OR Desiree OR Diana OR Diane OR
Dianne OR Dolores OR Dominique OR Donna OR Doreen OR Doris OR Dorothy OR
Ebony OR Eileen OR Elaine OR Elizabeth OR Ellen OR Emily OR Erica OR Erika OR
Erin OR Eva OR Evelyn OR Felicia OR Frances OR Gail OR Gayle OR Geraldine OR
Gina OR Glenda OR Gloria OR Grace OR Gwendolyn OR Hannah OR Heather OR
Heidi OR Helen OR Holly OR Irene OR Jackie OR Jaclyn OR Jacqueline OR Jaime
OR Jamie OR Jan OR Jane OR Janet OR Janice OR Janis 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
Josephine OR Joy OR Joyce OR Juanita OR Judith OR Judy OR Julia OR Julie OR
June OR Kara OR Karen OR Kari OR Karla OR Katelyn OR Katherine OR Kathleen
OR Kathryn OR Kathy OR Katie OR Katrina OR Kay 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 Lillian OR Linda OR Lindsay OR Lindsey OR Lisa OR Lois OR Loretta OR
Lori OR Lorraine OR Louise OR Lynda OR Lynn OR Lynne OR Mallory OR Mandy
OR Marcia OR Margaret OR Maria OR Marianne OR Marie OR Marilyn OR Marissa
OR Marjorie OR Marlene OR Marsha 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 Mildred 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 Patsy OR Patti 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 Rosa OR Rose OR Rosemary
OR Roxanne OR Ruby 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 Vicky OR Victoria OR Virginia OR Vivian OR Wanda
OR Wendy OR Whitney OR Yolanda OR Yvette OR Yvonne

This search finds 68% of all U.S. women on LinkedIn

Would people with the skills and experience I'm looking for use my
title(s) and explicitly mention my search terms?

How can I retrieve profiles I've previously excluded?

How can I search LinkedIn specifically to find people that no one else
has?

How many ways could someone possibly express the skills and
experience I am looking for?

Am I overlooking people because I don't see the right keywords?

"Only he who can see the invisible
can do the impossible."
– Frank Gaines

Building a Solid Foundation with the Boolean Black Belt | Talent Connect Vegas 2013

Building a Solid Foundation with the Boolean Black Belt | Talent Connect Vegas 2013

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