Evidence based Change through Analytics

Zürich, 30. Januar 2014

(HR) Analytics Initiative:

How to create

evidence-based change

http://blog.strimgroup.com/?p=1038


2

Range of Services

Analyses & Workshops

Pers. Administration & Reporting

Strategy & Execution

Assessments & Exec. Coachings

Webinars & Conferences

Surveys & Publications

3

Focus Topics

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

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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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.

7

Fundamentals Evidence-Based Management: Connect scientific coherences with company-specific procedures

Identification of general

causal relations (theories)

Identification of specific

practices (instruments)

Science Practice

Based on: Brodbeck, F.; Woschée, R.: Grundlagen und Möglichkeiten eines evidenzbasierten Personalmanagements, 2013

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the interaction creates a collective intelligence

Meta-

analyses

Controlled

laboratory/field

experiments

Comprehensive

correlation studies

Systematic

reviews

Systematic

evaluation

Systematic

Follow-up

Expert

survey

Case

study

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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

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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

10

Fundamentals Continuum of Human Capital Analytics

Anecdotes Scorecards

& Dashboards

Benchmarks

Correlations

Causation

Predictive

Analysis

Optimization

Source: Human Capital Analytics. How to Harness the Potential of Your Organization´s Greatest Asset.

Gene Pease, Boyce Byerly, Jac Fitz-enz. P. 17

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Fundamentals The LAMP Framework

HR Metrics and

Analytics That Are

A Force For

Strategic Change

„The Right Logic“

Rational Talent Strategy

(Competitive Advantage, Talent

Pivot Points)

L „The Right Measures“

Sufficient Data

(Timely, Reliable, Available)

M

„The Right Analytics“

Valid Questions and Results

(Information, Design, Statistics)

A

„The Right Process“

Effective Knowledge Management

(Values, Culture, Influence)

P

Source: Investing in People. Financial Impact of Human Resource Initiatives. Wayne Cascio and John Boudreau. P. 10.

12 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 )

Fundamentals HR Analytics Procedure Model

assess situation

(strat. analyses)

find cause

(domains)

analyze

maturity

define analy-

tical approach

execute &

optimize

Integrate results

invest &

evaluate

determine

stakeholder

requirements

assess

internal and external

environment

connections and

trends

probability of future

events

EBM: Capital „E“

and small „e“

create measure-

ment map

define HR research agenda

identify data & information

sources

identify leading indicators &

KPIs

gather data & information

transform data & information

Is it related to

Talent?

Work process?

…

consider

methodologies

consistency

project

management

…

strive for

high quality

transparency

credibility

stakeholder

input and buy-in

…

communicate &

use intelligence

results

look

for connections to business outcomes

at leading indicators for solution clues

(remark: this is a future-focused exercise)

develop a prediction

scenario(s)

make a list of metrics

to determine the rate

of success (cost, time

cycle, quality,

quantity, reaction, …)

predict RoI

launch & monitor

progress

report results

recycle the process

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Fundamentals HR Analytics Procedure Model: Situational Assessment

Source: Human Capital Analytics, A Primer, p. 27.

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Fundamentals HR Analytics Procedure Model: Maturity Levels


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Fundamentals HR Analytics Procedure Model: Measurement Map

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

Investment Leading Indicators Business Results Strategic

Goals

Selling Success

Performance

Objectives

- Prospect for

customers

- Identify customer

wants and needs

- Present and

demonstrate the

product

- Manage

customer

expectations

- Negotiate and

close the deal

# of Customer

Contacts

Closing Ratio

Total Gross Profits Increase Revenue

Customer

Satisfaction Index

Referral Business

Repeat Customers Gross Profit per

Sale

New Customer

Sales Volume

Appointments

(# and %)

Proposals

Presented

(# and %)

Product

Presentations

(# and %) Gross Profit per

Sale

Repeat and Referral

Sales Volume

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Fundamentals HR Analytics Procedure Model: Indicators (1/2)


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Fundamentals HR Analytics Procedure Model: Indicators (2/2)

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

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%

1 2 3

HR risk

perspective

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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

19 Source: Scott Mondore, Shane Douthitt and Maris Carson, Strategic Management Decisions: Maximizing the Impact and Effectiveness of

HR Analytics to Drive Business Outcomes

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.

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Predictive HR Analytics Map of causalities (learning and growth perspective)

Managerial

Leadership

Training Human

Capital

Relational

Capital

Structural

Capital

Human

Capital

Effectiven.

Retention

of Key

People

Business

Perfor-

mance

Knowledge

Generation

Employee

Engage-

ment

Employee

Satisfaction

Employee

Motivation

Value

Alignment

Strategy

Execution*

Knowledge

Integration

Knowledge

Sharing

Human

Capital

Depletion

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

Motivation Risk

Failure and Availability Risk

Occupational

Skill Risk

Integrity

Risk

Alignment

Risk

Resignation

Risk

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Would you like to know more? We invite you ... http://www.strimgroup.com/de/fachtagungen

Talent Relationship Management: May 22

Human Capital Analytics: September 19

Talent Relationship Management : June 6

Human Capital Analytics : October 16

Talent Relationship Management : June 26-27

Human Capital Analytics : October 30

http://www.strimgroup.com/de/fachtagungen

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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

845 Third Avenue

New York, NY 10022-6600

Telefon: +49 (0)172 7590 688

[email protected]

Gütschstrasse 22

CH-8122 Binz (Zürich)

Telefon: +41 (0)43 366 05 58

[email protected]

