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2008 isee User Conference Burlington Vermont October 9-10, 2008 PowerPoint PPT Presentation


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2008 isee User Conference Burlington Vermont October 9-10, 2008 Quantifying, Linking and Modeling Soft Variables Prof Kevin Austin Janelle Edgar [email protected] [email protected]

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2008 isee User Conference Burlington Vermont October 9-10, 2008

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2008 isee user conference burlington vermont october 9 10 2008

2008 isee User Conference

Burlington Vermont

October 9-10, 2008

Quantifying, Linking and Modeling Soft Variables

Prof Kevin Austin

Janelle Edgar

[email protected]

[email protected]

If any information or ideas are used please acknowledge the source.


2008 isee user conference burlington vermont october 9 10 2008

Flows

&

Dashboard

Profit

Value Model

&

Dashboard

Value Model

&

Dashboard

Rev

Exp

Channels

Dynamic Modelling

Financial

Process

Technology

Staff

Policy &

Strategy

Stakeholders

Model

Model

Model

In

Data

Out

Resources

CPU

Retention Model

&

Dashboard

Retention Model

&

Dashboard

Impact

&

Dashboard

Process

Dashboard

Financial

Dashboard

Technology

Dashboard

Business Simulator


2008 isee user conference burlington vermont october 9 10 2008

Staff Loss & Retention Drivers

Loss Drivers / Negative

Loss Driver 1X% X(0-10)

Loss Driver 2X%X (0-10)

Loss Driver 3X%X (0-10)

Loss Driver 4X%X (0-10)

Loss Driver 5X%X (0-10)

Retention Drivers / Positive

Retention Driver 1X%X (0-10)

Retention Driver 2X%X (0-10)

Retention Driver 3X%X (0-10)

Retention Driver 4X%X (0-10)

Retention Driver 5X%X (0-10)

Impact Weight

Mgt Perf

Score

Impact Weight

Mgt Perf

Score

100%

100%

- 100

+ 100

  • Discover the Drivers

  • Identify the Loss & Retention Drivers

  • Apply the weights

  • Impact weight assigned to each Driver.

  • Addition of impact weights = 100%

  • Assign Management Performance Scores

  • Score between 0-10, where 0 = Very Poor and 10 = Outstanding


2008 isee user conference burlington vermont october 9 10 2008

Identify the Employee Retention Index

Loss Drivers / Negative

Loss Driver 128 x 3.2 / 10 = 28 x 0.32 = 9.0

Loss Driver 225 X3.4 / 10 = 25 x 0.34 = 8.5

Loss Driver 321 x4.2 / 10 = 21 x 0.42 = 8.8

Loss Driver 416 x4.5 / 10= 16 x 0.45 = 7.2

Loss Driver 510 x3.9 / 10= 10 x 0.39 = 3.9

Impact Weight %

Mgt Perf

Score

Loss

Index

The Loss Driver Index is calculated using a combination of Relative Impact and Management’s Current Performance for all Loss Drivers.

The Staff Loss Driver Index was - 63 out of a possible maximum - 100.

The + 37 calculation on the left is subtracted from 100 and the sign changed as it is really out of – 100 not +100. (100 – 37 = 63 …change the sign = - 63) This is done because extreme goodness is actually zero and extreme badness is – 100).

Total = 37

Retention Drivers / Positive

Loss Driver 127 x 3.2 / 10 = 27 x 0.32 = 8.6

Loss Driver 222 X5.1 / 10 = 22 x 0.51 = 11.2

Loss Driver 319 x4.3 / 10 = 19 x 0.43= 8.2

Loss Driver 417 x4.6 / 10= 17 x 0.46 = 7.8

Loss Driver 515 x4.3 / 10= 15 x 0.43 = 6.5

Impact Weight %

Mgt Perf

Score

Retention

Index

The Retention Driver Index is calculated using a combination of Relative Impact and Management’s Current Performance for all Retention Drivers.

The Staff Retention Driver Index was

+ 42 out of a possible maximum +100.

Total = 42


2008 isee user conference burlington vermont october 9 10 2008

Identify the Employee Retention Index

Loss Drivers / Negative

28 x 3.2 / 10 = 28 x 0.32 = 9.0

25 X3.4 / 10 = 25 x 0.34 = 8.5

21 x4.2 / 10 = 21 x 0.42 = 8.8

16 x4.5 / 10= 16 x 0.45 = 7.2

10 x3.9 / 10= 10 x 0.39 = 3.9

Impact Weight %

Mgt Perf

Score

Loss

Index

Culture of stress, bullying, pressure

The Loss Driver Index is calculated using a combination of Relative Impact and Management’s Current Performance for all Loss Drivers.

The Staff Loss Driver Index was - 63 out of a possible maximum - 100.

The + 37 calculation on the left is subtracted from 100 and the sign changed as it is really out of – 100 not +100. (100 – 37 = 63 …change the sign = - 63) This is done because extreme goodness is actually zero and extreme badness is – 100).

Too much work

Lack of appreciation, recog’n, reward or valuing

Poor management & leadership competency

Lack of career pathways &

opps for development

Total = 37

Retention Drivers / Positive

27 x 3.2 / 10 = 27 x 0.32 = 8.6

22 X5.1 / 10 = 22 x 0.51 = 11.2

19 x4.3 / 10 = 19 x 0.43= 8.2

17 x4.6 / 10= 17 x 0.46 = 7.8

15 x4.3 / 10= 15 x 0.43 = 6.5

Impact Weight %

Mgt Perf

Score

Retention

Index

Sufficient resources &

balanced workload

The Retention Driver Index is calculated using a combination of Relative Impact and Management’s Current Performance for all Retention Drivers.

The Staff Retention Driver Index was +42 out of a possible maximum +100.

Proactive, supportive,

Decisive leaders & mgrs

Career development & opps

Clear vision, values, direction

Value me / my contributions

apprec’n, recog’n & reward

Total = 42


2008 isee user conference burlington vermont october 9 10 2008

Net Employee Retention Index


2008 isee user conference burlington vermont october 9 10 2008

Financial Impact


2008 isee user conference burlington vermont october 9 10 2008

Loss & Retention Drivers

Link between Retention & Financial Model


2008 isee user conference burlington vermont october 9 10 2008

Retention Modelling Console


2008 isee user conference burlington vermont october 9 10 2008

Financial Impact Console


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