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EFFECTIVE PERFORMANCE MANAGEMENT: HOW DO YOU MEASURE UP? Thursday, June 13, 2013
SPEAKERS:
Lori Holsinger (Atlanta)
Julia Howes (Boston)
Colleen O’Neill (Atlanta)
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Today’s Presenters
QUESTIONS?
Please type your questions in the Q&A section of the toolbar and we will do our best to answer it.
While in full-screen mode, simply use the Q&A button on the bottom right-hand side of your screen.
While in half-screen mode, use the Q&A panel on the bottom right-hand side of your screen.
Colleen O’Neill
Atlanta
+1 404 442 3516
Julia Howes
Boston
+1 617 747 9508
Lori Holsinger
Atlanta
+1 404 442 3527
MERCER WEBCAST 2 August 19, 2013
Agenda What we’ll cover today
Context and Current Trends
Mercer 2013 Global Performance Management Survey: Key Findings
Measurement and Analysis: Emerging Practices
Case Study
Summary/ Questions
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Vision for Performance Management
Consistently applied
Clearly focused
Simple and easy to use
3
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Design Requirements
4
Global
Differentiate Connect
Local Brand
Results Innovate
Tailored Abolish
Honesty Growth
Re-imagine
Align Engage Reward Develop Simplify Measure
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DO YOU HAVE ALL THE NECESSARY INFORMATION TO MAKE STRATEGIC DECISIONS
Reality of Performance Management
5
Section 2
MERCER 2013 GLOBAL PERFORMANCE MANAGEMENT SURVEY KEY FINDINGS
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Global Practice Report What companies do to try create performance management value
1,056 organizations
53 countries
17%
11% 29%
2%
5% 4%
21%
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Global Practice Report What companies do to try create performance management value
1,056 organizations
53 countries
Industries with highest participation
Durable Manufacturing
14%
Technology
10%
Consumer Goods
8%
Financial services
7% Non-durable
Manufacturing
5%
Insurance
5%
Transportation
5%
Energy
7%
Pharmaceutical
5%
Retail
5%
Education
4%
Tele communications
4%
Professional Services
5%
Healthcare provider
7%
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Performance Expectations
Year-end Process
9
Design Characteristics Globally more similarities than differences
Set ind. goals Use competencies
Self-assessment
Overall perf. rating
Use 5-point rating scale
Link perf & pay
80%
40%
60%
100%
20%
95%
85% 82% 89%
57%
89%
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Consistency CEO Evaluation
10
Design Characteristics Tailoring to business units or leadership levels uncommon
Common design
Tailored to executives
Bottom line & Ops
Leader qualities
80%
40%
60%
100%
20%
75%
25%
66%
50%
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Driver Analysis What elements have the most impact on overall success?
Management Skills
Executive Commitment
Calibration Technology
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Driver Analysis What elements have the most impact on overall success?
Candid dialogue
29%
48%
“SMART” goal setting
Linking performance & development
Management Skills Executive
Commitment Calibration Technology
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Driver Analysis Goal setting is undervalued
Year-end activities
Process & calendar
Goal setting
Conversations
Management Skills Executive
Commitment Calibration Technology
20%
40%
30%
10%
People Management Training ?
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Management Skills Executive
Commitment Calibration Technology
14
Driver Analysis Goal cascade is under-utilized
Company
Business Unit
Employee
56%
51%
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Driver Analysis What elements have the most impact on overall success?
Management Skills Executive
Commitment Calibration Technology
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Driver Analysis What elements have the most impact on overall success?
Discuss PM as
core to business
Provide regular
coaching
75%
90%
Highly committed executives
Management Skills Executive
Commitment Calibration Technology
100
25
50
75
0
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Driver Analysis What elements have the most impact on overall success?
Management Skills Executive
Commitment Calibration Technology
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Driver Analysis Calibration increases accuracy of evaluations and increase awareness
Management Skills Executive
Commitment Calibration Technology
Leaders
(or subset)
All employees
27%
56%
Who is reviewed?
100
25
50
75
0
56%
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Driver Analysis What elements have the most impact on overall success?
Management Skills Executive
Commitment Calibration Technology
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Driver Analysis Technology is less prevalent than expected
52% 19% 52% 19%
HRMS Only
HRMS + 1
Fully Integrated
No Integration
Management Skills Executive
Commitment Calibration Technology
53%
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Opportunities Align philosophy with what is measured
Link perf & pay
Measure people mgmt
Reward people mgmt
89%
36%
10%
80%
40%
60%
100%
0%
20%
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89%
22
Opportunities Align philosophy with what is measured
Link perf & pay
80%
40%
60%
100%
0%
20%
Year-end review
94%
Link perf & succession
51%
Drive performance
68%
75% measure
completion of evaluation
42% measure pay for performance link
23% measure
selection of top perf. for
succession
19% measure
retention bet. top & poor perf.
Section 3
MEASUREMENT AND ANALYSIS EMERGING PRACTICES
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What Happens in an Internet Minute?
24
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Meet the New Boss: Big Data Companies Trade In Hunch-Based Hiring
for Computer Modeling –The Wall Street Journal
Big Data Upends the Way
Workers Are Paid –The Wall Street Journal
Big Data for HR – Why Does It Matter? Leading HR functions are leveraging extensive data in their systems, as executives are demanding insights
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Data in Motion
Velocity
Volume
Data at Rest Data in Different Forms
Variety
Data in Doubt
Veracity
Common Terms Used to Characterize Challenging Aspects of “Big Data”
Source: IBM Whitepapers; 451 whitepapers, and Innosight Analysis
Data in Motion
Velocity
Volume
Data at Rest
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Do Big Data Challenges Present Themselves in Performance Management Analytics?
Traditional Performance Management Issue
• Current state: Performance ratings happen at a point in time
• Future state: measure performance ratings by events that occur at
multiple, different points in time (events such as terminations, promotions,
pay increase)
• Current state: Segmentation of measures into current performance ratings
(high, medium, low) provide limited analytical value, as often 80-90% of our
workforce are medium performers.
• Future state: track performance over previous periods to truly understand
long term organizational performance.
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Big Data
Big Data
Big Data
Big Data
Big Data Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data Big
Data
Big Data
Big Data
Big Data
Big Data Big
Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
Big Data
How do we define Big Data for HR?
Using data sets that have historical sweeps
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Long Data
As beautiful as a snapshot is,
how much richer is a moving
picture, one that allows us
to see how processes and
interactions unfold over time?
– Samuel Arbesman
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Long Data for HR…
Create Employee Timeline Develop Detailed Story for Each Employee
Employee Hired
Feb 2009
John Smith Employee EE001101
Mercer Analytics
Nov 2010
Performance Review 1, Promotion,
Compensation Adjustment
Nov 2011
Performance Review 2,
Compensation Adjustment
Nov 2012
Performance Review 3,
Lateral Movement,
Compensation Adjustment
2009
2000s
Hired 2010 2011 2012 January
2013
February March Now
Sustained High Performer
Mar 2013
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• Over time, do we really reward our employees who consistently
receive high performance ratings through: greater pay increases;
bonuses; promotions; and career opportunities?
• Where are our performance: pay outliers, e.g. employees who have
received a low performance rating but an above median pay
increase?
• Which employees received high performance ratings both this year
and last year?
• Which employees have received high performance rating over the
past three to five years?
• Which employees received low performance ratings more than three
times in their past five reviews?
• What are the characteristics of employees that upgraded from a
medium to high performer?
• What are the characterizes of employees that downgraded from a
high to a medium performer? From a medium to a low performer?
Performance Management Analysis Critical questions answered
31
Reward for Performance
$
New Employee Segments
Downgrades and Upgrades
Section 4
CASE STUDY
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Mercer Analytics Year-over-year rating variability
We love these employees – how are
we going to keep them?
These downgrades are of concern. What organizational factors
contributed to this, and what
interventions are needed to retain and
engage these employees?
Was there an active intervention that
occurred to move these employees
from a Low to Medium performer?
This big jump (from High to Low, or Low to High) is unusual in
one review period. Should we check the integrity of our PM
process?
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A simple click over the arrows brings up a list of employees in each of these categories.
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Mercer Analytics Year-over-year rating variability - promotions
Monitor these segments by key events (terminations, promotions, lateral
movements, pay increase) and characteristics (tenure, age, gender, career
level).
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Mercer Analytics Year-over-year rating variability - terminations
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Mercer Analytics Pay for performance
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Mercer Analytics Sustained performance ratings
• Identify employees that
have "Sustained High
Performance" over a
longer period e.g. 3, 4, 5
years.
– We want to ensure that
these employees have
a substantially different
employment experience
(through pay,
performance etc)
compared to the rest of
our employee
population.
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Summary
39 August 19, 2013
Test whether program giving desired return
Leverage long data
Focus on value drivers
How do you measure up?
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Additional Resources
Resource URL
Mercer Analytics platform www.mercer.com/merceranalytics
Talent Management consulting expertise http://www.mercer.com/referencecontent.ht
m?idContent=1405750
Workforce metrics and analytics webcast
series
www.mercer.com/webcastseries
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Questions?
QUESTIONS?
Please type your questions in the Q&A section of the toolbar and we will do our best to answer it.
While in full-screen mode, simply use the Q&A button on the bottom right-hand side of your screen.
While in half-screen mode, use the Q&A panel on the bottom right-hand side of your screen.
Colleen O’Neill
Atlanta
+1 404 442 3516
Julia Howes
Boston
+1 617 747 9508
Lori Holsinger
Atlanta
+1 404 442 3527
MERCER WEBCAST
