What is job fit really about?
January 12, 2012
ERE.net
Paul Basile, CEO Matchpoint Careers, Inc [email protected]
Introductions
2
POLL: who are we?
• In-house talent acquisition specialist • Talent management specialist • HR generalist • Professional recruiter • None of the above
Our agenda 1. The hiring process now 2. The results we get 3. The value of valid criteria 4. The way to get fit right
1. What to measure 2. How to measure 3. When to measure 4. Practicalities
3
Fit matters
• Subjective fit
• Perceived fit
• Objective fit
4
Fit matters – why people leave
5
What is hiring like now?
6
What is hiring like now?
7
What is hiring like now?
8
How’s it working?
9
How’s it working?
How’s it working?
How’s it working?
Current hiring – the results
• 85% of applicants are unfit for the job
• 55% of employees are dissatisfied with their job
• 46% of new hires leave within 18 months
• 30% of business failures are due to poor hiring decisions
13
Savvy hiring beats current hiring
Sele
ctio
n ap
proa
ch
Job performance
False positives
True negatives
True positives
False negatives
14
Sele
ctio
n ap
proa
ch
Job performance
False positives
True negatives
True positives
False negatives
Hiring on skill or experience (as now)
15
Job performance
False negatives
True positives
False positives
True negatives Se
lect
ion
appr
oach
Hiring on valid criteria
16
Selection criteria
Knowledge Skills Experience
Traditional criteria
17
Selection criteria
Knowledge Skills Experience
Cognitive abilities Patterns of behavior Interests & motivation (i.e., fit)
Traditional criteria
Performance-predicting criteria
18
Research into selection criteria
0
-0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7 Correlation coefficient
21
Weak predictors
0
-0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Unstructured interviews (0.18)
Years of education (0.10) Years of job experience (0.18)
Graphology (0.02)
Age (-0.1)
Weakly predictive
Correlation coefficient
21
Medium predictors
0
-0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Knowledge of the job (0.48)
References (0.36)
Unstructured interviews (0.18)
Years of education (0.10) Years of job experience (0.18)
Graphology (0.02)
Age (-0.1)
Weakly predictive
Somewhat predictive
Personality tests (0.40)
Correlation coefficient
23
Fit (0.26)
Strong predictors
0
-0.1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Cognitive ability tests (0.51)
Cognitive ability tests with behavioral assessment (0.67)
Knowledge of the job (0.48)
References (0.36)
Unstructured interviews (0.18)
Years of education (0.10) Years of job experience (0.18)
Graphology (0.02)
Age (-0.1)
Weakly predictive
Somewhat predictive
Powerfully predictive
Personality tests (0.40)
Correlation coefficient
Structured interviews (0.51)
24
Fit (0.26)
Baselines and differentiators
23
Employee performance
Baselines • Skills • Knowledge
Differentiators • Cognitive ability • Behavior • Preferences
Differentiators • Cognitive ability • Behaviors
– Apply to all roles, in different combinations – Relatively stable over time for an individual – Strongest reliable predictors of human performance
• Preferences – this is FIT – Different for each individual, and can change over time – Account for around 26% of engagement, 12% of
performance and 26% of managerial potential
Are higher competency and preference levels linked to superior work performance? Yes, but…
24
Competencies
Fit for purpose • Similar roles make similar demands… • …but every organization is different
– Different competencies and preferences – Different levels of individual competencies and preferences
• Need for tailoring by role and by organizational context
Fit, not absolute score, predicts performance
25
Getting the fit right
26
Measuring fit
27
Measuring fit
28
Measuring fit
29
Measuring fit
30
Measuring fit
31
Getting fit between job and candidate
Candidates register & complete
assessments
Hiring companies profile the job
Rank candidates according to their fit
with the job
Shortlist delivered to hiring company
Final selection & on-boarding
32
Timing to get fit right
Application forms / résumés
Interviews / other assessments
Psychometric assessments
Traditional recruitment pipeline
33
Timing to get fit right
Application forms / résumés
Interviews / other assessments
Psychometric assessments
Self-selection, employer-specific assessments
Interviews / other assessments
Psychometric assessments
Performance-predicting recruitment pipeline
Traditional recruitment pipeline
34
Technologies that fit • Online assessments, more efficient than ever • Analytics, fast and graphic • Validation studies, rich and quick • ATS linkages to assessments and person-job fit analyses • Computer adaptive testing, gaming…
35
But is it practical?
Candidate time
36
But is it practical?
< 1h 30 mins
Candidate time
37
But is it practical?
< 1h 30 mins
Candidate time Employer time
38
But is it practical?
< 1h 30 mins < 30 minutes
Candidate time Employer time
39
But is it practical?
< 1h 30 mins < 30 minutes
Candidate time Employer time Data processing time
40
But is it practical?
< 1h 30 mins < 30 minutes < 1 minute
Candidate time Employer time Data processing time
41
Results
42
Companies that use scientific performance prediction to get fit right, compared to those who don’t, have:
• 75% greater year-on-year increase in hiring manager satisfaction
• 75% greater yr-on-yr reduction in hiring costs • 2.5 x greater year-on-year increase in profit per
employee