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Data driven recruiting

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Brendan Browne
Brendan Browne

The document discusses using data insights to improve recruiting approaches through strategic staffing rather than slot-based recruitment. It presents a framework for strategic staffing that involves workforce demand forecasting, identifying critical roles, and focusing applicant sourcing on those roles. The case study describes how LinkedIn used data on talent supply, demand, skills, and connections to determine that Seattle would be a better location than New York for a new engineering office. Key recommendations include using data to increase influence, clarify problems to be solved, and understand regional differences in supply, demand, skills and connections.

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Driving Improvements in your Recruiting Approach through Data Brendan Browne Director, Global Talent Acquisition, LinkedIn Travis Burge Recruitment Product Consultant APAC, LinkedIn 1
To get us underway. TALENT CONNECT 2012 2
What we will cover today. Slot Recruitment Vs Strategic Staffing Introduce a Framework for Strategic Staffing Case study on how LinkedIn have used data Insights to manage risk for our critical roles Q&A
What is the purpose of a T Intersection sign? To process information and make decisions, take action for next direction TALENT CONNECT 2012
Strategic Sourcing Vs Slot Based Recruitment TALENT CONNECT 2012 5
1 Workforce Demand 2 Forecast Scenario Planning Workforce Gap Workforce Supply Forecast Critical Critical 3 Role 2 Role 4 Critical Role 1 Critical Critical Role 3 Role 5 4 Applicant Applicant Short Silver listing Medalists TALENT CONNECT 2012
1 Workforce Demand 2 Forecast Scenario Planning Workforce Gap Workforce Supply Forecast Critical Critical 3 Role 2 Role 4 Critical Role 1 Critical Critical Role 3 Role 5 4 Applicant Applicant Short Silver listing Medalists TALENT CONNECT 2012
Top Tips? 1. Engage Executive Buy-In 2. Workforce Planning is not about exact numbers 3. Use Multiple Scenarios 4. Focus on Business Critical Roles 5. Market Map External Labour Supply for Critical roles 6. Get the Line Area to own their Workforce Plan TALENT CONNECT 2012
New Markets for Talent: Where, Why, How TALENT CONNECT 2012 9
Where we should open up a new engineering office?
Assumptions Needs to be a healthy supply of talent in region Want to be in a major metro area with eye on NYC or Seattle Need a more junior mix of talent based on relo of internal senior leaders and managers to new metro area Need existing relationships with talent pool Looking for C#, SQL, Java TALENT CONNECT 2012 11
The Where: Over 475,000 Software Engineers on LinkedIn in the US TALENT CONNECT 2012 12
The Where: Big Markets vs. Hidden Gems (based on LinkedIn recruiter contact Feb-Aug 2012) TALENT CONNECT 2012 13
The Where & Some Why: New York provides a 33% larger supply of talent than Seattle, and has less overall demand for talent Supply Demand Region / Metro # SW # SW Eng Jobs % w job change Posted (% Area Engineers Jobs posted (past 12 months) of total (Q2 2012) pool) SF Bay Area 65,000 24,500 37.7% 19% NYC 36,100 5,200 14.4% 13% Boston 28,900 5,600 19.5% 15% Seattle 26,900 20,200* 75.1% 17% Washington DC 20,100 4,200 21.0% 12% Chicago 17,300 2,100 12.4% 13% . TALENT CONNECT 2012 14
The Where & Why: Skill Mix by City New York Seattle (word size represents skill frequency) TALENT CONNECT 2012 15
The Where & Why Seattle has more entry-level engineers NY has more senior and manager-level talent Manager and Above Manager and Above 9.6% 6.9% TALENT CONNECT 2012 16
The Why: ABC Co is more connected in Seattle with 618 employees connected to 2,255 engineers in Seattle New York Seattle Company # % # Company # % # Connected Connected Connected Connected Connected Connected in pool in pool at in pool in pool at company company Company 1 9,721 27% 22,280 Company 1 20,335 76% 43,248 Company 2 8,666 24% 8,620 Company 2 15,573 58% 10,340 Company 3 7,500 21% 7,583 Company 3 11,675 43% 8,965 Company 4 7,435 21% 11,844 Company 4 6,474 24% 1,754 Company 5 7,026 19% 8,974 Company 5 6,311 23% 11,418 Company 6 6,381 18% 5,583 Company 6 5,523 21% 6,745 Company 7 6,299 17% 5,241 Company 7 5,183 19% 6,680 Company 8 6,230 17% 5,103 Company 8 5,097 19% 7,959 ABC Co 1,106 3% 597 ABC Co 2,255 8% 618 TALENT CONNECT 2012 17
Connectedness Matters 21% more likely to be knowledgeable of employers 12% more likely to have a positive impression of employers 10% more likely to consider a job with employers TALENT CONNECT 2012
The Why: Talent Flows: Are we winning and losing talent? TALENT CONNECT 2012
Now What? TALENT CONNECT 2012 20
TALENT CONNECT 2012
Using data to set expectations Keywords: "Data Center" OR "Datacenter" Location Within 50 Miles Industry: Internet Seniority: Manager Interested In: Potential Employees Company Size: 501-1,000 OR 1,001-5,000 OR 5,001-10,000, OR 10,000+ Years of Experience: 6 to 10 years OR More than 10 years Years in Position: 3-5 years OR 6-10 years Company Type: Public Company Language: English Recommendations: 3-4 OR 5-10 OR 11-20 OR >20 TALENT CONNECT 2012
Search Results Initial Search = 7 Remove Recommendation = 19 Remove Years in Position = 71 Remove Company Type = 82 Remove Company Size = 126 TALENT CONNECT 2012
Measuring throughput Forget number of candidatesHOW MANY ARE IDLE? How quickly are hiring managers giving profile/ resume feedback? How quickly can we schedule technical phone screens? Where are the constraints and why are they happening? TALENT CONNECT 2012
Recommendation: Seattle Talent Supply is high Mix of skills is right Experience level of talent is right Connectedness is high Same time zone helps in management and collaboration Have internal leaders with deep professional ties to Seattle TALENT CONNECT 2012 25
Top Tips Data = Increase in Influence Clarify what problem you are trying to solve Understand supply/demand Understand skill mix by region Understand experience levels by region Understand how you compete vs. competition in new markets Connectedness matters TALENT CONNECT 2012 26
TALENT CONNECT 2012
Thank you! Brendan Browne Director, Global Talent Acquisition, LinkedIn Travis Burge Recruitment Product Consultant APAC, LinkedIn

More Related Content

Data driven recruiting

  • 1. Driving Improvements in your Recruiting Approach through Data Brendan Browne Director, Global Talent Acquisition, LinkedIn Travis Burge Recruitment Product Consultant APAC, LinkedIn 1
  • 2. To get us underway. TALENT CONNECT 2012 2
  • 3. What we will cover today. Slot Recruitment Vs Strategic Staffing Introduce a Framework for Strategic Staffing Case study on how LinkedIn have used data Insights to manage risk for our critical roles Q&A
  • 4. What is the purpose of a T Intersection sign? To process information and make decisions, take action for next direction TALENT CONNECT 2012
  • 5. Strategic Sourcing Vs Slot Based Recruitment TALENT CONNECT 2012 5
  • 6. 1 Workforce Demand 2 Forecast Scenario Planning Workforce Gap Workforce Supply Forecast Critical Critical 3 Role 2 Role 4 Critical Role 1 Critical Critical Role 3 Role 5 4 Applicant Applicant Short Silver listing Medalists TALENT CONNECT 2012
  • 7. 1 Workforce Demand 2 Forecast Scenario Planning Workforce Gap Workforce Supply Forecast Critical Critical 3 Role 2 Role 4 Critical Role 1 Critical Critical Role 3 Role 5 4 Applicant Applicant Short Silver listing Medalists TALENT CONNECT 2012
  • 8. Top Tips? 1. Engage Executive Buy-In 2. Workforce Planning is not about exact numbers 3. Use Multiple Scenarios 4. Focus on Business Critical Roles 5. Market Map External Labour Supply for Critical roles 6. Get the Line Area to own their Workforce Plan TALENT CONNECT 2012
  • 9. New Markets for Talent: Where, Why, How TALENT CONNECT 2012 9
  • 10. Where we should open up a new engineering office?
  • 11. Assumptions Needs to be a healthy supply of talent in region Want to be in a major metro area with eye on NYC or Seattle Need a more junior mix of talent based on relo of internal senior leaders and managers to new metro area Need existing relationships with talent pool Looking for C#, SQL, Java TALENT CONNECT 2012 11
  • 12. The Where: Over 475,000 Software Engineers on LinkedIn in the US TALENT CONNECT 2012 12
  • 13. The Where: Big Markets vs. Hidden Gems (based on LinkedIn recruiter contact Feb-Aug 2012) TALENT CONNECT 2012 13
  • 14. The Where & Some Why: New York provides a 33% larger supply of talent than Seattle, and has less overall demand for talent Supply Demand Region / Metro # SW # SW Eng Jobs % w job change Posted (% Area Engineers Jobs posted (past 12 months) of total (Q2 2012) pool) SF Bay Area 65,000 24,500 37.7% 19% NYC 36,100 5,200 14.4% 13% Boston 28,900 5,600 19.5% 15% Seattle 26,900 20,200* 75.1% 17% Washington DC 20,100 4,200 21.0% 12% Chicago 17,300 2,100 12.4% 13% . TALENT CONNECT 2012 14
  • 15. The Where & Why: Skill Mix by City New York Seattle (word size represents skill frequency) TALENT CONNECT 2012 15
  • 16. The Where & Why Seattle has more entry-level engineers NY has more senior and manager-level talent Manager and Above Manager and Above 9.6% 6.9% TALENT CONNECT 2012 16
  • 17. The Why: ABC Co is more connected in Seattle with 618 employees connected to 2,255 engineers in Seattle New York Seattle Company # % # Company # % # Connected Connected Connected Connected Connected Connected in pool in pool at in pool in pool at company company Company 1 9,721 27% 22,280 Company 1 20,335 76% 43,248 Company 2 8,666 24% 8,620 Company 2 15,573 58% 10,340 Company 3 7,500 21% 7,583 Company 3 11,675 43% 8,965 Company 4 7,435 21% 11,844 Company 4 6,474 24% 1,754 Company 5 7,026 19% 8,974 Company 5 6,311 23% 11,418 Company 6 6,381 18% 5,583 Company 6 5,523 21% 6,745 Company 7 6,299 17% 5,241 Company 7 5,183 19% 6,680 Company 8 6,230 17% 5,103 Company 8 5,097 19% 7,959 ABC Co 1,106 3% 597 ABC Co 2,255 8% 618 TALENT CONNECT 2012 17
  • 18. Connectedness Matters 21% more likely to be knowledgeable of employers 12% more likely to have a positive impression of employers 10% more likely to consider a job with employers TALENT CONNECT 2012
  • 19. The Why: Talent Flows: Are we winning and losing talent? TALENT CONNECT 2012
  • 20. Now What? TALENT CONNECT 2012 20
  • 22. Using data to set expectations Keywords: "Data Center" OR "Datacenter" Location Within 50 Miles Industry: Internet Seniority: Manager Interested In: Potential Employees Company Size: 501-1,000 OR 1,001-5,000 OR 5,001-10,000, OR 10,000+ Years of Experience: 6 to 10 years OR More than 10 years Years in Position: 3-5 years OR 6-10 years Company Type: Public Company Language: English Recommendations: 3-4 OR 5-10 OR 11-20 OR >20 TALENT CONNECT 2012
  • 23. Search Results Initial Search = 7 Remove Recommendation = 19 Remove Years in Position = 71 Remove Company Type = 82 Remove Company Size = 126 TALENT CONNECT 2012
  • 24. Measuring throughput Forget number of candidatesHOW MANY ARE IDLE? How quickly are hiring managers giving profile/ resume feedback? How quickly can we schedule technical phone screens? Where are the constraints and why are they happening? TALENT CONNECT 2012
  • 25. Recommendation: Seattle Talent Supply is high Mix of skills is right Experience level of talent is right Connectedness is high Same time zone helps in management and collaboration Have internal leaders with deep professional ties to Seattle TALENT CONNECT 2012 25
  • 26. Top Tips Data = Increase in Influence Clarify what problem you are trying to solve Understand supply/demand Understand skill mix by region Understand experience levels by region Understand how you compete vs. competition in new markets Connectedness matters TALENT CONNECT 2012 26
  • 28. Thank you! Brendan Browne Director, Global Talent Acquisition, LinkedIn Travis Burge Recruitment Product Consultant APAC, LinkedIn

Editor's Notes

  1. Introduction
  2. Agenda
  3. Planning for future workforce requirements
  4. Slot requirement Vs Strategic Staffing
  5. This slide shows us who were winning talent from, and who were losing talent to. As you might expect, red means were losing talent to the organizations listed on the left, and green means were winning talent from those organizations. [NOTE: this isnt our actual data, but were sharing it here as an example of what we review regularly]The left-hand side of this slide is fairly straightforward: we can see how many hires weve made from those companies in the past four quarters, and how many hires theyve made from us.On the right side, weve calculated that data as a ratio.We can watch this data over time to understand whats trending. It helps us identify competitive hiring risks or areas where we need more focus.
  6. Lets talk about how we start executing and operating using the future of data NOWTODAY.In the States we call the impossible to find candidate the purple squirrel because they dont exist.Raise your hand if you have every worked with a hiring manager who is asking you to recruit a type of candidate who simply doesnt exist. They are asking for too much.The Engineer with a PhD from Oxford who also speaks Italian, can lead teams, can also sell to customers and has worked at a multinational, a start-up and is now working at either company X, Y or Z.Can any of you relate with that?Well we can now start using structured data to educate our clients, our executives and hiring managers about the market and the addressable candidate pool. What does this do for our function?It builds credibility.Lets take a look at an actual example from LinkedIn.
  7. This was from an actual search we conducted for a Data Center Manager. David Henke, our SVP of Engineering, was under the gun, putting huge pressure on recruiting to get the search filled.We met with David to understand his requirements seen here. Told him we would be back in an hour to talk further. We knew that this was going to be very hard to fill, but we didnt want to get in to a debate with our Sr. Vide President. So we went away, pulled some data and returned to his office to meet him.
  8. Weve also taken data from our internal processes and used it to identify where are our latencies in the system and our biggest opportunities to drive efficiency for Talent Acquisition. For instance
  9. Can anyone tell me what this is?Right its a bunch of market data at the New York Stock Exchange.What if we had enough data on companies to literally have an index of company value?