The facts are clear: Most companies today need to do a better job selecting talent. Recent survey data collected by the Corporate Executive Board indicates that nearly a quarter of all new hires leave their companies within a year, and that hiring managers wish they never extended an offer to one of every five members of their team. And a recent Gallup survey found that 52 percent of American workers were “not engaged” with their work. Can you afford to miss an opportunity to learn how best-in-class organizations are using new technologies to scientifically assess talent before hiring, resulting in lower turnover, higher job performance and greater employee engagement? In this presentation, you will: Learn about new solutions that predict candidate success. Discover how best-in-class organizations are incorporating these new solutions into their hiring process. See the bottom-line results realized by these best-in-class practitioners.

1. Predictive Hiring
Find Candidates Who Will Succeed in your Organization

2. Agenda




Analytic applications and the rise of talent science
Predictive hiring: the use of analytics in recruiting
Not big data but the right data for better hiring decisions
Action plan – what you can do now

3. Analytic applications
Hype Cycle for Analytic Applications, 2013
Published: 31 July 2013
Analytical capability is increasingly packaged as a solution to address
specific business issues, rather than created with generic business
intelligence tools.

4. Talent Science, an Analytics Solution in Its
Infancy
Real Time
Customer Offer
Engine
Talent
Science

5. Talent Science
“Talent Science is not about the technology, but
rather a mindset that sees data as a basis for
decision making.”
Gartner Analytics Hype Cycle, December 2013

6. Why the Interest in Talent Science?
“…this tale about baseball
[Moneyball] may turn out to
be the opening chapter of a
much larger story about
jobs. Predictive statistical
analysis, harnessed to
big data, appears poised
to alter the way millions
of people are hired and
assessed.”
Atlantic Monthly, December 2013

7. Why the Interest in Talent Science?
Leading companies
like Google, Cisco,
KPMG, GE and
Facebook have
already made the
shift from gut feel to
data-driven talent
decision making.

8. Leaders Are Starting With Recruiting

9. Recruiting Has the Biggest Impact on
Business Results
HR Function or Process
Profit
Growth
Profit
Margin
Total
Improvement
Recruiting
3.5x
2.0x
5.5
On-boarding and retention
2.5x
1.9x
4.4
Managing Talent
2.2x
2.1x
4.3
Employer branding
2.4x
1.8x
4.2
Performance management and rewards
2.1x
2.0x
4.1
Developing Leadership
2.1x
1.8x
3.9
Mastering HR Processes
1.8
1.8
3.6
Global people management/global expansion
1.8
1.7
3.5
Enhancing employee engagement
1.8
1.6
3.4
Providing shared services and outsourcing
1.6
1.7
3.3
Source: BCG/WFPMA, 2012

10. Hiring for a Competitive Advantage
McKinsey study shows the value of hiring top performers:
40%
49%
increase in
productivity in
operations roles
increase in
profits in
management
roles
67%
greater revenue
in sales
positions
Source: McKinsey’s “War on Talent”

11. Your Gut Lies
“46% of new hires leave
after 18 months”
Forbes, May 2013
For most managers, intuition is
the primary guide to choosing
from a pool of candidates.
Unfortunately, it’s also a hugely
unreliable predictor of success.
Hiring managers wish they’d
never extended an offer to
one out of every five
members on their team
Corporate Executive Board
Only 30% of American
workers felt a strong
connection to their company
and worked for it with passion.
52% emerged as “not
engaged,” and another 18%
as “actively disengaged.”
Gallup Survey

12. Agenda




Analytic applications and the rise of talent science
Predictive hiring: the use of analytics in recruiting
Not big data but the right data for better hiring decisions
Action plan – what you can do now

13. How Do You Predict Candidate Success Today?
Typical recruitment practices
gather insight from the
candidate’s perspective

14. Resumes and Interviews
Interviews may not provide
a true representation
because candidates:
 can be coached;
 can research interview
questions and
appropriate answers
online;
 are ready to tell you
what you want to hear.

15. Finding the Authentic Candidate
“Over 50% of candidates lie on resumes…”
Steven D Levitt – co-author “Freakonomics”
46% of employer, education, and/or credential reference
checks conducted in 2008 revealed discrepancies between
what the applicant provided and what the source reported…
ADP 2009 Hiring Index Report
“81% of people in a study lied about themselves during job
interviews, with the more extroverted being more apt to tell
untruths …”
Brent Weiss and Robert S. Feldman, University of Massachusetts

16. Personality Assessments
One problem – results can be
faked!
Review of 22 studies that examined
actual faking:
 The estimated number of fakers
on self-assessments ranges from
15% to 49% (average =
30.5%).

17. Gaming the System

18. Skills Assessments
Limited correlation with
job failure
 When new hires fail,
89% of the time it is
for soft skills vs. job
skills.*
Cheating
 Estimates are that up to
1 in 10 candidates
engage in it
*Forbes, 2012

19. Cognitive Assessments
Cognitive Measures can
show Adverse Impact
Adverse impact occurs when
protected classes of applicants (e.g.,
females, non-whites) show
differences on selection tests that
negatively affect hiring for diversity.
Data from a large number of studies
show large average differences by
race/ethnicity on cognitive ability
tests.

20. Agenda




Analytic applications and the rise of talent science
Predictive hiring: the use of analytics in recruiting
Not big data but the right data for better hiring decisions
Action plan – what you can do now

21. A Way to Get More Predictive Data

22. Support for The Importance of
Behavioral Assessments by Others
“Nothing in the science of prediction and selection beats observing
actual performance in an equivalent role.”
Peter Cappelli
Director, Center for Human Resources, The Wharton School
“When the criterion was academic achievement or job performance,
other-ratings yielded predictive validities substantially greater than
and incremental to self-ratings”
Connelly & Ones, 2010, p. 1092
In other words, other people’s ratings of an employee add more
value toward predicting job success.

23. Here’s How You Do it…
• Focus on job-relevant competence at work
– Specific soft skills needed for the job
• Gather feedback via observations from relevant others
– Managers, peers, direct reports, clients
• Strictly adhere to scientific principles and best practices
discussed here today
• Leverage the benefits of technology to expedite the
process and gather large amounts of data

24. What Are Competencies?
Refers to an individual’s
knowledge, skills or abilities
that are necessary to perform
in a job successfully.
– Some examples :
dependability, accepting
feedback, building
relationships, clearly
communicating
– Unique for each job

25. A Job-Specific Assessment Library is Essential
346 surveys based on specific job competencies in SkillSurvey’s
extensive library allows you to get predictive feedback on candidates
for every position. Some examples include:
Engineering



Engineering Managers
Engineering Technician
Mechanical Engineer
Healthcare





Pharmacist
Physician Assistants
Physician
Physical Therapist
RN
Education



Admissions Officer
Faculty
K-12 Educators
Sales



Sales - Field
Sales - Inside
Sales Manager
Customer Service



IT



Customer Service Manager
Customer Service Professional
Customer Service Rep - Online
Database Administrator
Helpdesk Support
Software Developer
Customer Service Hourly
Skilled Trades Worker
Warehouse Order Selector
Finance







Administrative Managers
Customer Service Manager
Operations Manager
Retail
Hourly



Managerial
Accountant
Accounts Payable
Finance Manager
Financial Advisor



Loss/Prevention Specialist
Retail Cashier
Seasonal Worker
Hospitality



Food Service Manager
Hotel Clerk
Housekeeper
Administrative




Administrative Managers
Administrative Professionals
EPIC Administrative Professional
Executive Assistant

26. Engineer Survey

27. Sales Professional Survey

28. Customer Service Representative
Survey

29. RN Survey

30. All Valid Assessment Methodologies Have
Psychometric Integrity
• Consistency
Reliability
Psychometric Integrity
Validity
Compliance
• Measures what
it’s intended to
measure
• Predictive
• No adverse
impact
• No bias

31. Study Results Prove Behavioral Assessments
From Others Have Psychometric Integrity
Areas of Focus
Results
Reliability
 Internal Consistency
 Test-retest Consistency
 Inter-rater Consistency
Validity
 A statistically significant
predictor of turnover
 A statistically significant
predictor of supervisor
evaluations
Compliance
No statistically significant
difference based on
 Race/Ethnicity
 Gender
 Age

32. SkillSurvey Published Research
11 studies conducted….34,054 new hires tracked….observed over 21 months

33. 69% Decline In First Year Turnover For
Cause
% Termed for Poor Performance Within 365 Days of Hire
8.00
7.32
7.00
6.00
Percent
5.00
4.00
3.00
2.24
2.00
1.00
0.00
Year 1
[144/1968]
Year 2
[116/5178]
Year Using SkillSurvey
[Termed for poor performance/Total number hired]
*new hires across 3 companies and 3 industries (healthcare, retail, engineering)

34. 36% Decline In Voluntary First Year
Turnover
% Termed for Poor Performance Within 365 Days of Hire
20.00
18.24
18.00
16.00
Percent
14.00
11.53
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Year 1
[359/1968]
Year 2
[597/5178]
Year Using SkillSurvey
[Voluntarily termed/Total number hired]
Data from our combined 11 validity studies
*new hires across 3 companies and 3 industries (healthcare, retail, engineering)

35. Statistically Significant Predictor of
Supervisor Evaluations
Meta-Analysis
ρ
SDρ
CI-Low CI-High
% var
1. Bare-bones
.10
.06
.02
.18
38
2. Corrected for unreliability
.13
.08
.03
.24
38
3. Corrected for unreliability &
indirect range restriction
.26
.14
.08
.44
41
Notes. Number of studies (K) = 7. Total sample size (N) = 2789. ρ = sample size mean weighted
correlations. SDρ = standard deviation of ρ. CI-Low = lower 10% of correlation. +CI-High = upper 10% of
correlation. % var = percentage of variance accounted for by all artifacts.

36. HCAHPS (government survey* –
collects patient satisfaction with care)
•
Hospitals using SkillSurvey’s Pre-Hire 360® reference assessment solution in
their recruitment process have statistically higher HCAHPS scores than
other hospitals
•
Hospitals using SkillSurvey’s Pre-Hire 360® for reference checking their job
applicants, outperforming similar hospitals in nine out of ten areas of
patient quality.
•
Followed 5,754 new hires in healthcare companies (9 companies). Results:
Those with low
(vs. high) scores
are 200% more
likely to be fired
for poor
performance
Those with low
(vs. high) scores
are 24% more
likely to terminate
within the first
year
*for a review see - (www.cms.gov or
www.hcahpsonline.org or www.medicare.gov)

37. Predictive Power of Others’ Ratings
Higher ratings by others were accurate
predictors of:
•
Future behavior
•
Voluntary and involuntary turnover
•
Improved satisfaction of line managers
•
Increased customer and patient
satisfaction
•
Enhanced employee engagement

38. Validity Study – Large E&C Corp
Validity Study on New Hires
(2007 – 2012)

39. Measureable Business Outcomes
Overall, results of this study show that
SkillSurvey predicts 3 critical business
outcomes* of New Hires
Lower
Turnover
Overall
Lower
Involuntary
Termination
(fired for
cause)
Higher
Performance
Evaluations
*Note that these results are consistent with our published results based upon six previous client
validity studies: Hedricks, Robie & Oswald (2013). Web-based multisource reference checking:
An investigation of psychometric integrity and applied benefits.
International Journal of Selection & Assessment, 21, 99-110.

40. Overall, Those Who Termed Were Significantly Less
Likely to Have a SkillSurvey Pre-Hire 360®
NO
YES
SkillSurvey
Pre-Hire 360®
SkillSurvey
Pre-Hire 360®
Terminated,
overall (n=5,146)
53.8%
35.9%
33.3% less likely
to terminate
Involuntary term-fired; (n=556)
74.8%
25.2%
66.3% less likely
to terminate
Voluntarily
terminated
(n=2,513)
75.2%
24.8%
67.9% less likely
to terminate
* 2,900/10,256 (27.6%) had a Pre-Hire 360® Report
% Less Likely
to Terminate,
if had SkillSurvey
Pre-Hire 360®
40

41. Engineers Who Termed Were Significantly Less
Likely to Have a SkillSurvey Pre-Hire 360®
NO
SkillSurvey PreHire 360®
YES
SkillSurvey PreHire 360®
% Less Likely
to Terminate,
if had SkillSurvey
Pre-Hire 360®
Terminated, overall
(n=546)
69.2%
30.8%
55.8% less likely
to terminate
Involuntary term-fired; (n=52)
84.6%
15.4%
81.8% less likely
to terminate
Voluntarily
terminated (n=384)
69.5%
30.5%
56.1% less likely
to terminate
Feb 20, 2014 Confidential, for internal Gartner use only without prior approval from SkillSurvey
41

42. Engineers With Low Scores Were Much
More Likely to Be Fired
Engineers With Low Scores Were 507% More Likely
to be Involuntarily Terminated (Fired)
9.1
Low (3/30)
Medium
(0/91)
0.0
1.5
High (5/340)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Percent Who Were Involuntarily Terminated
Feb 20, 2014 Confidential, for internal Gartner use only without prior approval from SkillSurvey
7.0
8.0
9.0
10.0

43. 344 Work-Weeks Saved -- 95% Reduction in
Time Spent Reference Checking 9,727
Candidates
400
364.8
350
300
Number of WorkWeeks to Ref
Check 9,727
Candidates
250
95%
reduction
200
150
100
20.4
50
0
Telephone
SkillSurvey Pre-Hire 360®
Feb 20, 2014 Confidential, for internal Gartner use only without prior approval from SkillSurvey
43

44. Lowest-Scoring Behaviors of Engineers Who
Were Involuntarily Terminated (Fired)
• Interpersonal Skills
– Act as a business partner with peers and all levels of management to
achieve business objectives
– Communicate information, ideas and data clearly, concisely and so others
will understand
• Delivers Results:
–
–
–
–
Handle multiple projects effectively in a fast-paced environment
Plan and prioritize work effectively
Handle multiple tasks effectively within demanding deadlines
Consistently meet or exceed goals
• Displays Professionalism:
– Show the ability to be self-managed (e.g. can manage own time and
prioritize work to meet deadlines)
*the average score of these behaviors was “medium low”
44

45. Agenda




Analytic applications and the rise of talent science
Predictive hiring: the use of analytics in recruiting
Not big data but the right data for better hiring decisions
Action plan – what you can do now

46. Determine How Behavioral Assessments
From Others Will Fit Into Your Process
1. Recruiters
Source
Candidates
2. Screen Top
3-5
Candidates
3. SkillSurvey
Pre-Hire
360®
4. Select top two, and
use feedback to
structure behavioral
interviews

47. Getting Money From IT For a Test
Position this as a Predictive Talent Analytics pilot
Goal:
Test a new cloud-based tool to predict which candidates
have the highest probability of succeeding after they are
hired.
Expected Business Outcomes:
• Lower first year turnover, both voluntary and for cause
• Better supervisor ratings at 90, 180 and 360 days

48. The Risk of Failure is Low, SkillSurvey Has A
Significant Track Record of Success
1.3 MM
850+
Candidates
CLIENTS
7 of the Top 14
U.S. Hospitals
7 of the Top 20
Children’s Hospitals
GROWTH SINCE
2009
6.5 MM
Reference Providers
Education
Healthcare
Hospitals
300%+
Energy &
Utilities
Financial
Services
Services & IT
Technology
Engineering &
Construction
Consumer
Products
Human
Resources
Manufacturing
Pharma &
Medical Devices
Long-Term Care
45+
Magnet Hospitals
50+
Teaching Hospitals