Find more infos: http://blog.strimgroup.com/?p=385 Presentation given during Oracle Solutions Summit Geneva. Theme: Predictive Analytics in HR - creating a culture of analytics-enabled agility.

1. See blog for slides:
http://blog.strimgroup.com/?p=385
Predictive Analytics in HR –
Creating a culture of
analytics-enabled agility
Geneva, 7th March 2013

2. I t d ti
Introduction
The STRIM group of companies
The STRIM management sphere system with its seven
management principles – grouped into three clusters – is the
logical answer to often occuring implementation problems in
practice.
STRIMacademy guides and provides support to employees
during the implementation of strategies and measures.
Obtaining acceptance and addressing hesitation are both
addressed through effective communication.
g
STRIMservices aims to have companies focussing on value
added activities. Analyses lay the groundwork for monitoring
and, required, strategy.
and if required adjusting the strategy
STRIMconsult translates strategies in actions and aligns the
organization as well as leadership- and incentive systems with
the strategy
strategy.
2

3. Introduction
I t d ti
The Conference Board
3

4. CEO most critical challenges i 2013
CEOs t iti l h ll in
Human capital and Operational excellence are the top challenges
p g p g
Top 5 Strategies to meet the Top 3 Challenges
Source: The Conference Board: CEO Challenge Report 2013
Human Capital being involved 4

5. Source: Predictions for 2013. Corporate Talent, Leadership and HR – Nexus of Global Forces Drives New Models for Talent.
Josh Bersin, January 2013 5

6. Vi
View on HR
The core characteristics of the HR function serve to perpetuate many of today s current challenges
Low strategic license
Not having the roles or the bandwidth to be strategic
Few „home-grown“ strategic capabilities, difficulty attracting
high-caliber talent, and ineffective tools to assess them
Ultimate ownership Convincing business
tricky case & burning
Lack of ownership or platform elusive
peer mindset and
p Limited use of forward-
forward
………………….
acceptance/willingness looking metrics that would
to plod along vs. leap reduce reactive measures
frog
Difficulty in communica-
Lack of a budget to ting convincingly – in
g gy
innovate terms expected of a
strategic business partner
Source: McKinsey & The Conference Board Research, 2012
6

7. Vi
View on HR
C-Level and BU leaders view HR as lagging in strategic performance
3,8 / 6
,
4/6
Source: McKinsey & The Conference Board Research, 2012
Legend: Rating on scale of 1-6, 1 = “needs improvement”, 6 = “best practice” 7

8. Vi
View on HR
A minority of CEOs get comprehensive reports on their workforce
Percentage of CEO
P t f CEOs
Percentage of CEOs who
100% believe the relevant
information is important or
very important
y
80%
Information Gap:
CEOs believe
60% information is Don´t receive information
important but
don t
don´t receive
comprehensive
reports
40% Not adequate
20%
Adequate but would like
it now
0%
Costs of Return on Assessments Labour Employees Staff
employee
turnover
investment
on human
of internal
advancement
costs views and
needs
productivity Information received is
capital
comprehensive
Source: PwC Saratoga: Key trends in human capital 2012. A global perspective
8

9. Predictive Analytics in
P di ti A l ti i HR
Stages of development
Stages Focus
F Kind f Information
Ki d of I f ti
HR quantitative & qualitative master data:
Reporting oriented towards headcount, FTE, pay and other cost
recording
the past categories, competencies,
g , p ,
engagement, retention, etc.
connection of deliverables with corporate
relating actual goals such as quality, innovation,
productivity, risk, etc.
explicitly & detailed relationships among data without giving
comparing & (descriptive meaning to the patterns (exploratory);
understanding analytics, trends from the past (risky!); tie HR
benchmarking) metrics to the business
HR Analytics
comparisons between what happened
related to the
yesterday to what will probably happen
future
predicting tomorrow, meaning to the patterns
(prescriptive
Business observed in descriptive analysis;
analytics)
Intelligence focus on (a few) leading indicators
9

10. P di ti A l ti i HR
Predictive Analytics in
Map of causalities (learning and growth perspective)
Managerial
g 0,506 Retention
of Key
Leadership
People
Alignment
0,442 Failure and Availability Risk
Risk
R2=68,2%
0,530 0,326 0,360 Human
Human Relational
Training Capital
Capital
C it l Capital
C it l
Effectiven.
0,751 0,307 R2=28,5%
0,358
-0,337
0,475 Structural
Occupational
Capital
C it l Skill Risk
0,734 Employee 0,491 0,327 Business
Employee Knowledge
Engage- Perfor-
Satisfaction Generation
ment mance
0,543
0,439
R2=44,1%
44 1%
0,456 0,429
Strategy
0,394 Knowledge
Integrity Execution* Integration
-0,372
Risk Employee
Motivation
0,430
0 430 0,262
0 262 Resignation
Risk
Motivation Risk 0,285 -0,233 Human
Value Knowledge
Capital
Alignment Sharing
Depletion
R2=28,5%
28 5%
Remark: Referring to Nick Bontis and Jac Fitz-Enz: Intellectual Capital ROI, 2010
* for further remarks: Mark A. Huselid, Brian E. Becker, Richard W. Beatty: The Workforce Scorecard, 2005 10

11. P di ti A l ti i HR
Predictive Analytics in
HC RoI measurement
Levels of measurement
Revenue
Revenue by business unit
Revenue by country/region
Revenue by product line
yp
Non-wage costs
1 HC RoI = 1,28 2 HC RoI = 1,22 Material costs
Facilities and overhead costs
5% 5% Costs of outsourced activities
…
Revenue - Non Wage Costs
Non-Wage
HC RoI = = 1,13 Average remuneration
Av. Remuneration x Number of FTEs
Salary and wage levels
Performance-related pay
5% …
3 HC RoI = 1,19 Full-time equivalents
Full-time v part-time
p
Temps and casuals
Remark: Anonymised project example of an international transport and logistics company Contract workers
… 11

12. P di ti A l ti i HR – T St t i
Predictive Analytics in Top Strategies
Effective Leaders
Measurement
M t Measurement
M t 2006 2011
Levels Category Status Status Eight core practices
0 Inputs/Indicators 100% 100%
1 Reaction 92% 89%
Ask, „What needs to be
2 Learning 48% 59%
3 Application 11% 34%
done?
done?“
4 Impact 8% 21% Ask, „What is right for the
5 RoI 2% 11%
enterprise?“
Develop action plans
Take responsibility for
decisions
Take responsibility for
communicating
Are focused on
opportunities rather than
on problems
Run productive meetings
Think and say „we“ rather
than „I.“
Source: Jack Phillips, Patricia Pulliam Phillips, Rebecca L. Ray: Measuring Leadership Development. Quanify Your
Program´s Impact and RoI on Organizational Performance, 2012. 12

13. P di ti A l ti i HR – T St t i
Predictive Analytics in Top Strategies
Raising Engagement (1/2)
Six successful traits
Drivers of Engagement
Trust and integrity
Nature of the job
Line of sight between
individual performance and
company performance
Career growth opportunity
Pride about the company
Coworkers/team members
E l
Employee d l
development
Personal relationships with
one s manager
Pay fairness
Personal influence
Well-being
Source: PwC Saratoga. Managing people in a changing world. Key trends in human capital, a global perspective, 2010.
The Conference Board. Linkage of Engagement Drivers to Common Challenges, 2013. 13

14. P di ti A l ti i HR – T St t i
Predictive Analytics in Top Strategies
Raising Engagement (2/2)
Effect of motivation and hygiene factors on engagement
and contribution Culture of Engagement
Set the tone at the top; align
words and actions
Communicate the importance
of teamwork and collaboration
Increase company events to
mix groups together
Launch
La nch peer coaching/
mentoring programs
Financial rewards/peer
recognition
L
Launch cross f
h i l k
functional task
forces to break down silos
Use technology to enable
sharing/access of information
Adapt performance
management systems to hold
managers and employees
accountable for success
Source: Scarlett Surveys International, 2008.
The Conference Board. Employee Engagement in a VUCA* World, 2011. (* Volatile, Uncertain, Complex, Ambiguous) 14

15. E id
Evidence-Based M
B d Management
t
Connect scientific coherences with company-specific procedures
Capital „E and small „e
E“ e“
Capital „E“
external evidence
Identification of general Identification of specific
causal relations (theories) practices (instruments) sound scientific evidence
generalizable cause effect
cause-effect
relationships
Science Practice
Small „e“
internal evidence
organization-specific
Meta- Case evidence
analyses study
data that are systematically
collected in a particular
Controlled
Systematic Systematic Expert organization and situation
laboratory/field
reviews evaluation survey to enable local evidence-
experiments
based decisions
Comprehensive Systematic
correlation studies Follow-up the interaction creates a
collective intelligence
15

16. Predictive Analytics in
P di ti A l ti i HR
HC RoI analysis
16

17. Cl i R k
Closing Remarks
Our approach in Predictive Analytics in HR Plan
Focus on leading indicators, corresponding benchmarks
Scan
Commitment on target corridors and timelines
Transparency on the data collection Capability planning and gap-analysis – associated with
process, what formulas are being used,why workforce categories in terms of valued capabilities
the data matter to the operation Succession planning, scenario planning, forecasts, etc.
Human-capital
Human capital facts: quantitative
and qualitative information
Produce
Internal analyses: segmen-
tation of HR data, core Execution of measures
competencies, etc.
p , identified,
identified new methods and
External analyses: target guidelines as well as IT
groups, PESTEL, etc. systems and warehouses
HC RoI Analysis: Process optimization applied
Capital „E“ and small „e“; to hiring, compensation,
impact on bus. performance development or retention
HR service delivery: service
integration, outsourcing and
Predict offshoring
HR meas rement foc sed on
measurement focused
Rethink leading indicators; employee engagement, knowledge value-adding results
management, turnover, executive reward, etc.
Talent value model: „Why do
Reassess the suitability of leadership development programmes employees choose to stay?“
and overall effectiveness of learning and development
Recommendations for policy adjustments
Remark: Referring to Fitz-Enz, J.: The New HR Analytics: Predicting the Economic Value of Your Company´s Human Capital Investment,
2010 ( HCM:21) and to Davenport, T.; Harris, J.; Shapiro, J.: Competing on Talent Analytics, HBR October 2010. 17

18. C t tP
Contact Person ==> F slides, visit: htt //bl
> For lid i it http://blog.strimgroup.com/?p=385
ti /? 385
President and CEO at STRIMgroup AG
in Zurich
Senior Fellow Human Capital at
The Conference Board in New York
Member in several corporate and
educational networks
Lecturer in the Master degree course
Human Capital Management at the
Constance University of Appl. Sciences
Author and publicist to strategy and
human resources issues
Selected professional positions:
Head of Global HR Analytics, Gütschstrasse 22 845 Third Avenue
Deutsche Bank AG and
AG, CH-8122 Binz (Zürich) New York, NY 10022-6600
York
Senior Manager hrs at Telefon: +41 (0)43 366 05 58 Telefon: +1 410 433 660 558
PricewaterhouseCoopers AG. volker.mayer@strimgroup.com volker.mayer@conferenceboard.org
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