Benchmarks und Dashboards sind nicht ausreichend, um einen kontinuierlichen Verbesserungs- und Optimierungsprozess zu institutionalisieren. Mittels statistischer Verfahren, wie Cluster- und Regressionsanalysen, werden Kausalmodelle aufgebaut und prognostizierende Analysen erstellt. Diese Präsentation geht auf Herausforderungen, Handlungsempfehlungen und Stolperfallen beim Aufbau von (HR) Analytics ein. Die Einbindung der sog. externen Evidenz, die Identifikation von Leading Indicators (Frühwarnindikatoren, steuerungsrelevanter Kennzahlen) und die Erstellung der Measurement Map sind nur drei Bestandteile des von uns entwickelten Vorgehens bei der Durchführung einer (HR) Analytics Initiative entlang von Reifegraden (Analytics Maturity).

1. (HR) Analytics Initiative:
How to create
evidence-based change
Zürich, 30. Januar 2014

2. Range of Services
Analyses & Workshops
Strategy & Execution
Webinars & Conferences
Pers. Administration & Reporting
Assessments & Exec. Coachings
Surveys & Publications
2

3. Focus Topics
3

4. Fundamentals
Common Challenges
Getting buy-in from senior leaders and executives about the value of human capital analytics
initiatives;
Showing the impact of human capital analytics initiatives on business and the bottom line to “make the
case” for analytics;
Aggregating data into a single, centralized database with consistent, quality data;
Developing the capabilities (systems, technology, skills and resources) to do the analytics;
Using tangible measures to measure the intangibles; and
Moving from the reactive to the predictive.
Source: Human Capital Analytics, A Primer, The Conference Board
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5. Fundamentals
Guiding Principles
Focus on the critical few
Focus on getting a return on the analytics investment
Develop actionable information
Embrace predictive analytics
Partner with other functions
Aim for high-quality standards
Rely on intuition when necessary
Balance desire for accuracy with need for information
Balance the quantitative with the qualitative
Use meaningful metrics
Communicate data effectively
Develop capability throughout HR/HC
Source: Human Capital Analytics, A Primer, The Conference Board
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6. Fundamentals
Myths about (Predictive HR) Analytics
We (HR) have not matured enough to do predictive analytics.
We don´t capture enough data to do predictive analytics.
We need to make big investments in data technology to do predictive analytics.
We can simply buy a predictive-modeling capability by investing in advanced HR business-intelligence
solutions.
We need to hire a group of statisticians before we can do predictive analytics.
Predictive analytics produces „perfect“ predictions and are always the best technique.
Predictive models are foolproof, i.e. good software tools implies good models.
Predictive models always deliver business results.
Can be built and forgotten.
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7. Fundamentals
Evidence-Based Management: Connect scientific coherences with company-specific procedures
Identification of specific
practices (instruments)
Science
Practice
Metaanalyses
Case
study
Controlled
laboratory/field
experiments
Systematic Systematic
reviews
evaluation
Comprehensive
correlation studies
Expert
survey
Systematic
Follow-up
internal evidence, organizationspecific facts based on
systematically collected data
external evidence, sound scientific
knowledge, generalizable
cause-effect relationships
Identification of general
causal relations (theories)
the interaction creates a collective intelligence
Based on: Brodbeck, F.; Woschée, R.: Grundlagen und Möglichkeiten eines evidenzbasierten Personalmanagements, 2013
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8. Fundamentals
Transformative HR Through Evidence-Based Change (1/2)
Logic driven Analytics
Do you have information overload or persuasive analytics?
Applying proven business tools to talent (talent sourcing, surpluses and shortages
Using logical frameworks (e.g. LAMP model)
Knowing the business models
Segmentation
Where are your pivotal talent segments?
Are you confident you know where your pivotal segments are?
Do you know what investments will attract and engage them?
Do you know what aspects of their performance provide the highest return?
Risk Leverage
Is Human Capital R-I-S-K a four-letter word?
Does your HR department have processes to assess risk?
Does HR have the confidence to distinguish between „good“ risks and „bad“ ones?
It is reckless to ignore this issue when it is so much on the minds of boards and CEOs
Source: Retooling HR, John W. Boudreau. Presentation 2012
8

9. Fundamentals
Transformative HR Through Evidence-Based Change (2/2)
Integration and Synergy
Is your HR portfolio less than the sum of its parts?
If your individual HR programs are good, but the function as a whole feels underpowered then it probably reveals
a lack of integration and synergy.
Synergy means finding ways to make 1+1=3. Too often programs, practices and organizational units are in silos
(1+1=2) or actually in conflict (1+1=0).
Optimization
Spreading „peanut butter“ of making investments?
Does HR have the courage and analytical rigor to optimize investments in the workforce?
Do you invest more where ROIP is higher. Rather than investing in traditional areas where the ROIP may be
lower?
Source: Retooling HR, John W. Boudreau. Presentation 2012
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10. Fundamentals
Continuum of Human Capital Analytics
Optimization
Predictive
Analysis
Causation
Correlations
Benchmarks
Anecdotes
Scorecards
& Dashboards
Source: Human Capital Analytics. How to Harness the Potential of Your Organization´s Greatest Asset.
Gene Pease, Boyce Byerly, Jac Fitz-enz. P. 17
10

11. Fundamentals
The LAMP Framework
A
L
„The Right Logic“
Rational Talent Strategy
(Competitive Advantage, Talent
Pivot Points)
„The Right Analytics“
Valid Questions and Results
(Information, Design, Statistics)
HR Metrics and
Analytics That Are
A Force For
Strategic Change
P
M
„The Right Measures“
Sufficient Data
(Timely, Reliable, Available)
„The Right Process“
Effective Knowledge Management
(Values, Culture, Influence)
Source: Investing in People. Financial Impact of Human Resource Initiatives. Wayne Cascio and John Boudreau. P. 10.
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12. Fundamentals
HR Analytics Procedure Model
analyze
maturity
EBM: Capital „E“
and small „e“
determine
stakeholder
requirements
define HR research agenda
create measurement map
identify data & information
sources
identify leading indicators &
KPIs
assess situation
(strat. analyses)
gather data & information
transform data & information
assess
internal and external
environment
find cause
(domains)
connections and
trends
define analytical approach
invest &
evaluate
communicate &
use intelligence
results
develop a prediction
scenario(s)
predict RoI
launch & monitor
progress
report results
Talent?
Work process?
…
probability of future
events
make a list of metrics
to determine the rate
of success (cost, time
cycle, quality,
quantity, reaction, …)
Is it related to
Integrate results
execute &
optimize
look
for connections to business outcomes
at leading indicators for solution clues
(remark: this is a future-focused exercise)
consider
methodologies
consistency
project
management
…
strive for
high quality
transparency
credibility
stakeholder
input and buy-in
…
recycle the process
Source 1+2
: HR Analytics Handbook; Laurie Bassi. Human Capital Analytics; Gene Pease, Boyce Byerly, Jac Fitz-enz
Source 3
: TCB Research Report Human Capital Analytics: A Primer
Source 4
: STRIM Unique Selling Proposition (proprietary development in co-operation with
)
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13. Fundamentals
HR Analytics Procedure Model: Situational Assessment
Source: Human Capital Analytics, A Primer, p. 27.
13

14. Fundamentals
HR Analytics Procedure Model: Maturity Levels
Source: Human Capital Analytics, A Primer, p. 15.
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15. Fundamentals
HR Analytics Procedure Model: Measurement Map
Investment
Leading Indicators
Business Results
Strategic
Goals
# of Customer
Contacts
Selling Success
Performance
Objectives
- Prospect for
customers
New Customer
Sales Volume
Appointments
(# and %)
Closing Ratio
- Identify customer
wants and needs
Product
Presentations
(# and %)
- Present and
demonstrate the
product
Proposals
Presented
(# and %)
Gross Profit per
Sale
- Manage
customer
expectations
Increase Revenue
Repeat and Referral
Sales Volume
Repeat Customers
- Negotiate and
close the deal
Referral Business
Total Gross Profits
Gross Profit per
Sale
Customer
Satisfaction Index
Source: Human Capital Analytics. How to Harness the Potential of Your Organization´s Greatest Asset.
Gene Pease, Boyce Byerly, Jac Fitz-enz. P. 64: Measurement Map for a Sales Training Initiative
15

16. Fundamentals
HR Analytics Procedure Model: Indicators (1/2)
Source: Human Capital Analytics, A Primer, p. 19.
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17. Fundamentals
HR Analytics Procedure Model: Indicators (2/2)
1
2
3
Human Capital Measures (HCMs):
Employee engagement
69,2%
77,9%
!
Leadership
38,5%
47,1%
!
Employee commitment
36,5%
40,4%
Readiness level
33,7%
44.2%
!
Turnover (voluntary)
28,8%
94,2%
!
Employee satisfaction
28,8%
64,4%
Competence level
27,9%
36,5%
Workforce diversity
24,0%
78,8%
Training
21,2%
57,7%
Promotion rate
17,3%
44,2%
Executive stability (or chum)
17,3%
31,7%
Workforce age
16,3%
65,4%
Health and safety
14,4%
48,1%
Span of control
8,7%
39,4%
Depletion cost
5,8%
14,4%
Other
4,8%
8,7%
!
HR risk
perspective
Source: Jac Fitz-Enz: The New HR Analytics, 2010
% of HR professionals naming these HCMs as being leading ind.
% of HR professionals naming these HCMs as being in use
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18. Fundamentals
Typical skill proficiency levels required for each of the four analyst types
Quantitative
Business
knowledge
and design
Relationship
and
consulting
Coaching
and staff
development
Champion
Professional
Semi-professional
Amateur
Basic
Foundational
Intermediate
Advanced
Expert
Source: HR Analytics Handbook. Laurie Bassi. P. 25
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19. Fundamentals
Benefits of Predictive Analytics in HR
HR can redirect the money they spend today on the wrong employee initiatives to more beneficial
employee initiatives.
The investments that they decide to make that focus on employees will result in tangible outcomes
that benefit shareholders, customers and employees themselves.
The returns on such investments, via their impact on the top and/or bottom lines, can be quantified.
HR departments can be held accountable for impacting the bottom-line the same way business or
product leaders are held accountable.
HR executives will be included in the conversation, because they can now quantify their numerous
impacts on business outcomes.
Source: Scott Mondore, Shane Douthitt and Maris Carson, Strategic Management Decisions: Maximizing the Impact and Effectiveness of
HR Analytics to Drive Business Outcomes
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20. Predictive HR Analytics
Map of causalities (learning and growth perspective)
Retention
of Key
People
Managerial
Leadership
Alignment
Risk
Failure and Availability Risk
Human
Capital
Integrity
Risk
Value
Alignment
Occupational
Skill Risk
Employee
Engagement
Employee
Satisfaction
Human
Capital
Effectiven.
Relational
Capital
Structural
Capital
Training
Business
Performance
Knowledge
Generation
Strategy
Execution*
Knowledge
Integration
Employee
Motivation
Motivation Risk
Resignation
Risk
Knowledge
Sharing
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
Human
Capital
Depletion
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21. Would you like to know more? We invite you ...
http://www.strimgroup.com/de/fachtagungen
Talent Relationship Management: May 22
Talent Relationship Management : June 6
Talent Relationship Management : June 26-27
Human Capital Analytics: September 19
Human Capital Analytics : October 16
Human Capital Analytics : October 30
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22. Your Personal Point of Contact
Chairman and CEO at
STRIMgroup AG, Zurich / CH
Senior Fellow at The
Conference Board in New York
Lecturer in the Master's
program in Human Capital
Management at Lake
Constance Business School /
Germany
Gütschstrasse 22
845 Third Avenue
CH-8122 Binz (Zürich)
New York, NY 10022-6600
Telefon: +41 (0)43 366 05 58
Telefon: +49 (0)172 7590 688
volker.mayer@strimgroup.com
volker.mayer@conference-board.org
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