SCORECARD SESSION 4

© 2005 Corporate Executive Board

TELLING STORIES WITH HR METRICSTurning Data into Information

CLC METRICS

© 2005 Corporate Executive Board 2

Roadmap for the Presentation

Introduction to Data-Driven StoriesIntroduction to

Data-Driven Stories

Elements of a High Value StoryElements of a High Value Story

The Challenge at HandThe Challenge at Hand

Case Example:Early Tenure Turnover



Worthy of Any CFO’s Consideration Organizations That Effectively Leverage Their Workforces Reap Financial Return

Sample Research

Source: Becker, Huselid Pickus and Spratt, Human Resources Management Journal, Vol. 31(1), Spring 1997; Bilmes (2002), The People Factor; PwC Global Human Capital Survey, 2002.

Findings Study

Companies with a documented HR strategy have 35 percent higher revenues per employee, 12 percent lower absenteeism and more efficient performance management and reward systems. Three-quarters of those firms with a documented HR strategy also feel that their performance management systems are “very effective.” Companies with lower absenteeism have higher profits per employee.

PwC Global Human Capital Survey

Companies that scored highest against the “people scorecard” - earned higher total shareholder returns than lower scoring companies; top scoring companies had an average return of 27 percent whereas those at the bottom earned just 8 percent.

The People Factor

A 35% improvement in Human Capital Architecture “sophistication” is linked to a 10% to 20% gain in market capitalization per employee.

Human Resources Management Journal


The Shareholder PerspectiveIn Light of the Increasing Proportion of Market Value Tied to Intangible Assets, the

Need for Human Capital Metrics Is Not Expected to Wane

Percentage of Market Value Derived from Each Asset Class

Source: 1.The Brooking Institution Analysis of S&P500 Companies.

2. Kaplan and Norton

38%

62%

85%

62%

38%

15%

0%

50%

100%

1982 1992 2002

Intangible Assets Tangible Assets

Top 10 Nonfinancial Metrics as Valued by Investors

Source: Ernst & Young Center for Business Innovation, Measures that Matter (1997)

Four of ten are human capital measures.Four of ten are human capital measures.

1. Strategy execution2. Management credibility3. Quality of strategy4. Innovativeness5. Ability to attract talented people6. Market share7. Management experience8. Quality of executive compensation9. Quality of major processes10. Research leadership

1 1 2


Updated Mandate: Provide Information, Not Data

Timeline of Major HR Information Management Challenges1970–2005

The Current Challenge in HR Information Management Is to Transform Data Captured in Systems into Information That Supports Decision Making

Provide Workforce Information to the Business

Collect Workforce Data in SystemsCollect Workforce Data in Systems

Build an Enterprise-Wide System

Build an Enterprise-Wide System

Protect DataFrom Y2k Concerns

Protect DataFrom Y2k Concerns

Extract Data from SystemsExtract Data from Systems


Human Capital Management Systems MarketTotal Revenue 2001-2007 (E)

Investments Largely Fall Short of ExpectationsDespite Significant Spend on Human Capital Management Systems— ~$5 Billion per Year…

Source: Barron, Monica and Fenella Scott, “The Human Capital Management Applications Report, 2002-2007.” AMR Research Report, 2003.

$4.1 $4.1 $4.3 $4.7 $5.0 $5.3 $5.6

0

1

2

3

4

5

6

2001 2002 2003 2004 2005 2006 2007

$ B

illio

ns


37%

29%

34%

Agree or Strongly AgreeNeither Agree nor DisagreeDisagree or Strongly Disagree

Distribution of HR Executive Reponses

…HR Executives Lack Confidence That the Systems Produce the Metrics Required to Support the Business

Investments Largely Fall Short of Expectations

39%

41%

20%

Ineffective or Very IneffectiveNeither Ineffective nor EffectiveEffective or Very Effective

Source: Corporate Leadership Council 2002 Metrics Survey.

The HR function’s measurement system is

clearly linked to corporate strategy.

Overall, how would you rate the effectiveness of your current

HR measurement/metrics system?

n = 271 organizations. n = 271 organizations.




Data-Driven Stories






Oversupplying Human Capital Data Decreases its Value, as Decision-Makers Lack Time to Conceptualize and Organize the Information

A Key Measurement Risk: The Data Quagmire

Value

SupplyDemand

Quantity

Supply and Demand CurveIllustrative

“If the supply of information is exploding, the value of the information is plummeting… What is the scarce resource? The scarce resource is the ability to conceptualize and organize the information in some creative way to create large amounts of value. That's our challenge-to recombine information in novel ways.”

Dr. J. Doyne Foyner McKinsey Professor Santa Fe Institute

The Information Glut


The Quagmire Illustrated: IBM While Armed with Data, IBM Experienced Poor Performance in the Early ’90s. As Part

of the Turnaround Effort, CEO Lou Gerstner Required Concise Presentations

Source: Austin, Robert D. and Richard L. Nolan, “IBM Corporation Turnaround,” Harvard Business School, 2000.

IBM Senior Executive MeetingsCirca 1992

Revenue = $64.5 BillionNet Income = ($5 Billion)Revenue = $64.5 BillionNet Income = ($5 Billion)

IBM Senior Executive MeetingsCirca 1994

Revenue = $64 BillionNet Income = $3 BillionRevenue = $64 BillionNet Income = $3 Billion


Avoiding the Quagmire: Employing StorytellingDocumentary Filmmakers Practice an Art That Requires Filtering Available

Information to That Which Is Relevant for Building a Compelling, Interesting Story

Crafting a Documentary Film

History of Ray Charles

Born:Ray Charles Robinson

Date:September 23, 1930

Location:Albany, Georgia

Storytelling


Filtering Human Capital Data Through StoriesEmploying a Storytelling Filter when Presenting Human Capital Data Aids

in Building a Compelling Case for Organization Decision-Making and Action

Crafting a Data-Driven Story

Human Capital Metrics

Q4 2004 Storytelling


Building Upon Current Paradigms Incorporating Data-Driven Stories into Current Decision-Making Processes Improves the

Effectiveness of the Process and Credibility of Recommendations

Experience-Driven Story

IllustrativeData-Driven Story

Illustrative

Alice is a high flyer.Alice is a high flyer.

Frank was a regrettable loss.

Frank was a regrettable loss.

Sabrina is a rising star.Sabrina is a rising star.

Looking at the data…Looking at the data…

Promotion Rate Cost per Hire Offer Acceptance




Data-Driven Stories






Critical Elements of a Data-Driven Story Whether Driven by Data, Fiction or Experience, All Narratives Share

a Set of Critical Elements That When Taken Together Provide Information

Scene-Setting Plot

Development Dénouement

Construct Paradigms

Build Context

Source: Chzarniawska, Barbara, A Narrative Approach to Organization Studies, Sage Publications (1998).

Highlight Logical Reasoning

Build Understanding

Answer “So What” Questions

Drive Decisions & Actions

Elements of a Story


Setting the Scene: Establish Common Ground Establishing Common Ground Enables Uniform

Interpretation of Results Throughout the Audience

Success Strategies: Maintain a consistent format

Divide the workforce into relevant workforce segments

Pitfalls to Avoid: Assuming all audience members share the same perspective

Including extraneous data points

Example: Workforce Profile

Headcount Regional Distribution

Gender Composition Tenure Composition

Employment Level Composition Salary Distribution


Setting the Scene: Size the Opportunity Sizing the Opportunity Highlights the Value of the Analysis for the Audience

Success Strategies: Clearly document assumptions

Preview assumptions with key members of the audience

Pitfalls to Avoid: Overstating the opportunity

Using folklore rather than research to build assumptions

Example: Cost Savings

$5 M

$151 M

$1,000 per Hire $30,000 per Hire


Plot Development: Make Logical ConnectionsUtilizing Logic Provides Connection Between Individual Data Elements and Helps the Audience Understand the Argument Within the Analysis

Success Strategies: Outline the argument before developing final deliverables

Clearly document assumptions made in absence of data

Pitfalls to Avoid: Treating correlations as causations

Omitting seemingly obvious elements of the logic chain

Example: Document the Logic Chain

Conclusion = T

• If P, Then Q

• P

• If Q, Then R

• S

• If R and S,

Then T

Conclusion = T

• If P, Then Q

• P

• If Q, Then R

• S

• If R and S,

Then T


Success Strategies: Construct the logical argument around that one overall message

Remove slides from the story to determine if they are necessary

Pitfalls to Avoid: Adding interesting information that does not further the argument

Attempting to answer too many questions in the same deliverable

Plot Development: Focus on the PlotFocusing on the Plot During the Analysis Holds Audience Attention on the Main Argument

Example: Storyboard


Example: Use Text to Communicate “So What”

Success Strategies: Succinctly communicate key findings in prose

Lead presentations with the primary conclusion

Pitfalls to Avoid: Assuming audience members will agree with your conclusion

Overstating conclusions based on the data available

Dénouement: Draw ConclusionsExplicitly Drawing Conclusions Ensures That

All Audience Members Understand the Analysis’ Key Findings

As a manager tenure in a store increases…Manager Tenure

…termination rate decreases…Termination Rate

…leading to a more tenured hourly workforceWorkforce Tenure

A F

A F

A F


Example: Suggest Next Steps Success Strategies: Explicitly set up the decision-making conversation

Maintain a point of view about the most beneficial next steps

Pitfalls to Avoid: Assuming that only one path of next steps exists

Suggesting no next steps

Dénouement: Suggest Next StepsSuggesting Next Steps Sets Up a Decision-Making Conversation and Thus,

Increases the Likelihood That Action Will Occur




Data-Driven Stories






0 2 4 6 8 10 12 14 16 18 20 22 24

2001 Hires 2002 Hires 2003 Hires

Significant Low Tenure Turnover Present Within Tamarack*

New Hire Retention2001–2003

Projected New Hire RetentionNext 10 Years

100%

x%

2x%

3x%

4x%

Hires

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2003 VTR 2002 VTR 2001 VTR

100%

x%

2x%

3x%

4x%

* Pseudonym.


Reducing First Year Turnover Can Result in Significant Cost Savings

Annual Hires Required to Maintain a Steady Headcount Five-Year Cumulative Cost Savings

2004 2005 2006 2007

Current Scenario 1 Scenario 2

6x

5x

4x

At $1k Cost Turnover At $20k Cost Turnover

Scenario 1 Scenario 2

$40x

$20x

$0x$x $2x

$19x

$36x

Scenario 1: ↓ First Year Turnover by 5%Scenario 2: ↓ First Year Turnover by 10%

Scenario 1: ↓ First Year Turnover by 5%Scenario 2: ↓ First Year Turnover by 10%


“No Shows” Are a Significant Driver of First Year Turnover

Percentage of TerminationsBy Separation Reason (2003)

CareerOpportunity

No Show Personal Return toSchool

WorkEnvironment

Other

< 1 Year Tenure 1+ Years Tenure

2x%

x%

0%


Most “No Shows” Occur in the First Three Months of Employment

Percentage of First Year Terminations Occurring in the First Three Months

By Separation Reason (2003)

CareerOpportunity

No Show Personal Return to School WorkEnvironment

Other

< 3 Months Tenure 3 to 12 Months Tenure

100%

0%

SCORECARD SESSION 4

Business

Transcript of SCORECARD SESSION 4