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​ Jennifer Shappley
​ Director, Talent Acquisition, LinkedIn
How LinkedIn Used Data to Drive Recruiting Efficiency
​ Chris Pham
​ Data Scientist, LinkedIn
How LinkedIn Used Data to Drive Recruiting Efficiency
•  The analytics performed to estimate hires
•  How we achieved alignment between
Recruiting, HR, and Finance
•  How we held Recruiting accountable
•  Impact and lessons learned
•  Q&A
Agenda
LinkedIn operates the largest professional network on the internet
450M+
members around
the globe
+2
new members
per second
10K
employees
worldwide
30
cities around
the globe
45%
headcount growth
rate per year
Headcount Plan is finalized
Visualization shown is for illustrative purposes only
With timing of budget planning cycles,
Recruiting is never in line with business demand
How does Recruiting become more strategic when
it comes to meeting hiring demand?
Forecasting hires and staffing Recruiting teams accordingly
Workforce Plan
-forecasting hiring demand-
Incremental Hiring Backfill Hiring “Ripple” Effect
FP&A Incremental
Headcount Plan
Org Shape
Incremental Hiring
by Level!
Attrition Rates
Transfer
Probabilities
Promotion
Rates
Probability of
Internal Transfer
(r)
1
Adjusted Backfill!
Hiring by Level!
Backfill Hiring!
By Level!
( 1 - r )
________
Forecasting hires and staffing Recruiting teams accordingly
Capacity Plan
-determining people needed to meet targets-
Seasonality
Incremental Headcount
by Month
Historical seasonality
of terminations
18 sub-BUs and
5 regions
Total hiring by BU,
Region, Level and !
Month!
Recruiting Resources
Recruiter Productivity
(hires per month)
Support Ratios
Manager Span
of Control
Talent Acquisition!
Headcount!
Forecasting hires and staffing Recruiting teams accordingly
Incremental Hiring Backfill Hiring “Ripple” Effect
FP&A Incremental
Headcount Plan
Org Shape
Incremental Hiring !
by Level!
Attrition Rates
Transfer
Probabilities
Promotion
Rates
Probability of
Internal Transfer
(r)
1
Adjusted Backfill!
Hiring by Level!
Backfill Hiring!
By Level!
( 1 - r )
_________
Backfill Hiring “Ripple” Effect Seasonality Recruiting Resources
Incremental Headcount
by Month
Historical seasonality
of terminations
18 sub-BUs and
5 regions
Total hiring by BU,
Region, Level and
Month!
Recruiter Productivity
(hires per month)
Support Ratios
Manager Span
of Control
Talent Acquisition!
Headcount!
Workforce Plan
Forecasting hiring demand
Capacity Plan
Determining people needed to meet targets
NAPKIN
How do we enforce operational
excellence and accountability?
Meet
Regularly
Drive
Alignment
Adjust as
Needed
A new operational framework between Analytics & TA Leadership
Check your
Assumptions
•  TA lead & Analytics partner
meet monthly to review
resourcing and attainment
to plan

•  Review demand 
•  Is attrition what we
expected?

•  Did we accurately plan for
demand? 
•  This will be a dynamic
process – review regularly
and make necessary
adjustments

•  Partner with Analytics to
recalibrate model based on
actual outcomes
•  Ensure you’re aligned with
your business leaders

•  Regularly sync with Finance
and Human Resources
Involving the right Stakeholders
Talent Acquisition HR Business Partner
Finance Business Leader
Capacity of TA team, insight into
employment trends
Insight into workforce trends,
attrition and organizational
changes
Timing of hires to align with
budget
Where will the team be growing?
What skillsets will be needed in
the future?
Talent
Analytics
95%
of actual hires were accurately predicted by our model
13%
of our annual budget was given back to the business
•  The modeling done would’ve been useless if we
hadn’t turned it into an actionable plan
•  Recalibrating the model when necessary
•  Transparency of data across all recruiting teams to
break down silos in how teams managed productivity
and performance
•  Buy-in is hard
•  Don’t wait for the stars to align
•  You don’t need perfect data quality, or complex tools,
or even an analytics team
Lessons Learned
Turbocharging the recruiting engine: How LinkedIn used data to drive recruiting efficiency | Talent Connect 2016

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Turbocharging the recruiting engine: How LinkedIn used data to drive recruiting efficiency | Talent Connect 2016

  • 1.
  • 2. ​ Jennifer Shappley ​ Director, Talent Acquisition, LinkedIn How LinkedIn Used Data to Drive Recruiting Efficiency
  • 3. ​ Chris Pham ​ Data Scientist, LinkedIn How LinkedIn Used Data to Drive Recruiting Efficiency
  • 4. •  The analytics performed to estimate hires •  How we achieved alignment between Recruiting, HR, and Finance •  How we held Recruiting accountable •  Impact and lessons learned •  Q&A Agenda
  • 5. LinkedIn operates the largest professional network on the internet 450M+ members around the globe +2 new members per second 10K employees worldwide 30 cities around the globe 45% headcount growth rate per year
  • 6. Headcount Plan is finalized Visualization shown is for illustrative purposes only With timing of budget planning cycles, Recruiting is never in line with business demand
  • 7. How does Recruiting become more strategic when it comes to meeting hiring demand?
  • 8. Forecasting hires and staffing Recruiting teams accordingly Workforce Plan -forecasting hiring demand- Incremental Hiring Backfill Hiring “Ripple” Effect FP&A Incremental Headcount Plan Org Shape Incremental Hiring by Level! Attrition Rates Transfer Probabilities Promotion Rates Probability of Internal Transfer (r) 1 Adjusted Backfill! Hiring by Level! Backfill Hiring! By Level! ( 1 - r ) ________
  • 9. Forecasting hires and staffing Recruiting teams accordingly Capacity Plan -determining people needed to meet targets- Seasonality Incremental Headcount by Month Historical seasonality of terminations 18 sub-BUs and 5 regions Total hiring by BU, Region, Level and ! Month! Recruiting Resources Recruiter Productivity (hires per month) Support Ratios Manager Span of Control Talent Acquisition! Headcount!
  • 10. Forecasting hires and staffing Recruiting teams accordingly Incremental Hiring Backfill Hiring “Ripple” Effect FP&A Incremental Headcount Plan Org Shape Incremental Hiring ! by Level! Attrition Rates Transfer Probabilities Promotion Rates Probability of Internal Transfer (r) 1 Adjusted Backfill! Hiring by Level! Backfill Hiring! By Level! ( 1 - r ) _________ Backfill Hiring “Ripple” Effect Seasonality Recruiting Resources Incremental Headcount by Month Historical seasonality of terminations 18 sub-BUs and 5 regions Total hiring by BU, Region, Level and Month! Recruiter Productivity (hires per month) Support Ratios Manager Span of Control Talent Acquisition! Headcount! Workforce Plan Forecasting hiring demand Capacity Plan Determining people needed to meet targets
  • 12. How do we enforce operational excellence and accountability?
  • 13. Meet Regularly Drive Alignment Adjust as Needed A new operational framework between Analytics & TA Leadership Check your Assumptions •  TA lead & Analytics partner meet monthly to review resourcing and attainment to plan •  Review demand •  Is attrition what we expected? •  Did we accurately plan for demand? •  This will be a dynamic process – review regularly and make necessary adjustments •  Partner with Analytics to recalibrate model based on actual outcomes •  Ensure you’re aligned with your business leaders •  Regularly sync with Finance and Human Resources
  • 14. Involving the right Stakeholders Talent Acquisition HR Business Partner Finance Business Leader Capacity of TA team, insight into employment trends Insight into workforce trends, attrition and organizational changes Timing of hires to align with budget Where will the team be growing? What skillsets will be needed in the future? Talent Analytics
  • 15. 95% of actual hires were accurately predicted by our model
  • 16. 13% of our annual budget was given back to the business
  • 17. •  The modeling done would’ve been useless if we hadn’t turned it into an actionable plan •  Recalibrating the model when necessary •  Transparency of data across all recruiting teams to break down silos in how teams managed productivity and performance •  Buy-in is hard •  Don’t wait for the stars to align •  You don’t need perfect data quality, or complex tools, or even an analytics team Lessons Learned