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Shrink Analytics Michael Sanders Shrinkage Control Analyst J.C. Penney Company, Inc.

Shrink Analytics

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Shrink Analytics. Michael Sanders Shrinkage Control Analyst J.C. Penney Company, Inc. $18B 1093 Stores 147K Associates. Basic Correlations and Flaws. *Shrink results grouped by audit score range. Quartiles. * Shrink quartiles with average audit score. Regression Analysis Result. - PowerPoint PPT Presentation

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Page 1: Shrink Analytics

Shrink AnalyticsMichael Sanders

Shrinkage Control AnalystJ.C. Penney Company, Inc.

Page 2: Shrink Analytics

$18B1093 Stores

147K Associates

Page 3: Shrink Analytics

Basic Correlations and Flaws

*Shrink results grouped by audit score range

Audit Average Median

Score Shrink

Shrink Low High

90's 1.69 1.73 0.09 9.08

80's 1.83 1.83 0.14 10.03

Failures 1.93 1.90 0.48 6.98

Shrink Range

Page 4: Shrink Analytics

Quartiles

* Shrink quartiles with average audit score

Shrink

Quartile

Shrink

Average

Audit Score

Ave

1 0.93 88

2 1.55 87

3 2.11 87

4 3.34 85

Page 5: Shrink Analytics

Regression Analysis Result

Audit Baseline or Slope Slope times Estimated

Score "Intercept" AuditScore Shrink

95 3.15% -0.0130% -1.24% 1.92%

94 3.15% -0.0130% -1.22% 1.93%

93 3.15% -0.0130% -1.21% 1.94%

92 3.15% -0.0130% -1.20% 1.96%

91 3.15% -0.0130% -1.19% 1.97%

90 3.15% -0.0130% -1.17% 1.98%

89 3.15% -0.0130% -1.16% 1.99%

88 3.15% -0.0130% -1.15% 2.01%

Page 6: Shrink Analytics

Regression Analysis on the NFL

Page 7: Shrink Analytics

INT TakeINT Give

Fumble TakeFumble GivePenalty YDSPass YD/ATTRush YT/ATT Completion %

Rush YPG, Pass Yards PG3rd Down Conversions4th Down Conversions

Page 8: Shrink Analytics

Pearson Correlation

• A technique that determines the strength of a relationship between two variables. • +1 indicates they are perfectly related in a

positive linear sense. Example: Caloric intake increases, weight increases.

• -1 indicates they are perfectly related in a negative linear sense. Example: Car price goes down, as age goes up.

• “zero” indicates there is no correlation. Example: The number of Red Sox fans named Steve to the number of wins the Red Sox win have this year.

Page 9: Shrink Analytics

Defense & Offensive Stats

-1 +1 0

Page 10: Shrink Analytics

0.48

Team WinsRUSH YPG

Titans 13 137Colts 12 80Giants 12 157Panthers 12 152Steelers 12 106Dolphins 11 119Falcons 11 153Patriots 11 142Ravens 11 149Vikings 10 146Bears 9 105Buccaneers 9 115Cardinals 9 74Cowboys 9 108Eagles 9 106Jets 9 125Broncos 8 116Chargers 8 108Redskins 8 131Saints 8 100Texans 8 11549ers 7 100Bills 7 115Packers 6 113Jaguars 5 111Raiders 5 124Bengals 4 95Browns 4 100Seahawks 4 111Chiefs 2 113Rams 2 103Lions 0 83

Pearson Correlation (Positive) – Wins/RSH YPG

Page 11: Shrink Analytics

Correlation to Wins

-1 +1 0

.48

Ru

sh Y

ard

s P

G

Page 12: Shrink Analytics

Pearson Correlation (Negative) – INT (Give)

0.48 -0.43

Team WinsRUSH YPG

INT Give

Titans 13 137 9Colts 12 80 12Giants 12 157 10Panthers 12 152 12Steelers 12 106 15Dolphins 11 119 7Falcons 11 153 11Patriots 11 142 11Ravens 11 149 12Vikings 10 146 17Bears 9 105 14Buccaneers 9 115 13Cardinals 9 74 15Cowboys 9 108 20Eagles 9 106 16Jets 9 125 23Broncos 8 116 18Chargers 8 108 11Redskins 8 131 6Saints 8 100 18Texans 8 115 2049ers 7 100 19Bills 7 115 15Packers 6 113 13Jaguars 5 111 13Raiders 5 124 11Bengals 4 95 15Browns 4 100 20Seahawks 4 111 15Chiefs 2 113 16Rams 2 103 19Lions 0 83 19

Page 13: Shrink Analytics

Correlation to Wins

-1 +1 0

-.43

IN

T G

ive

.48

Ru

sh Y

ard

s P

G

Page 14: Shrink Analytics

Pearson Results0.48 -0.43 0.30 0.35 -0.18 -0.24 0.03

Wins TeamRUSH YPG

INT Give

PASS YPG

INT (Take)

FUM (take)

FUM Give

PENALTY YDS

7 49ers 100 19 211 12 6 16 7329 Bears 105 14 191 22 10 13 6104 Bengals 95 15 150 12 12 11 5917 Bills 115 15 190 10 12 15 5388 Broncos 116 18 279 6 7 12 7394 Browns 100 20 149 23 8 6 6699 Buccaneers 115 13 226 22 8 13 8349 Cardinals 74 15 292 13 17 15 7818 Chargers 108 11 241 15 9 9 7482 Chiefs 113 16 196 13 16 8 645

12 Colts 80 12 256 15 11 5 6199 Cowboys 108 20 237 8 14 13 952

11 Dolphins 119 7 227 18 12 6 6699 Eagles 106 16 244 15 14 10 635

11 Falcons 153 11 209 10 8 10 59112 Giants 157 10 199 17 5 3 8215 Jaguars 111 13 208 13 4 11 8139 Jets 125 23 206 14 16 8 5690 Lions 83 19 185 4 16 10 7296 Packers 113 13 238 22 6 8 984

12 Panthers 152 12 197 12 13 7 63711 Patriots 142 11 223 14 8 10 5015 Raiders 124 11 148 16 8 12 8232 Rams 103 19 184 12 14 12 718

11 Ravens 149 12 176 26 8 9 7858 Redskins 131 6 189 13 5 12 6448 Saints 100 18 311 15 7 8 7974 Seahawks 111 15 164 9 11 12 601

12 Steelers 106 15 206 20 9 10 8128 Texans 115 20 267 12 10 12 664

13 Titans 137 9 176 20 11 8 85510 Vikings 146 17 185 12 13 14 692

0.48 -0.43 0.30 0.35 -0.18 -0.24 0.03

Wins TeamRUSH YPG

INT Give

PASS YPG

INT (Take)

FUM (take)

FUM Give

PENALTY YDS

7 49ers 100 19 211 12 6 16 7329 Bears 105 14 191 22 10 13 610

Page 15: Shrink Analytics

Correlation to Wins

-1 +1 0

.21

INT

Tak

e

-.43

IN

T G

ive

-.18

Fu

mb

le T

ake

-.24

Fu

mb

le G

ive

.03

Pen

alty

YD

S

.56

Pas

s Y

D/A

TT

.15

Ru

sh Y

T/A

TT

.49

Co

mp

leti

on

%.4

8 R

ush

Yar

ds

PG

.30

Pas

s Y

ard

s P

G

.55

3rd

do

wn

co

nve

rsio

ns

.04

4th

do

wn

co

nve

rsio

ns

Page 16: Shrink Analytics

Multiple Buckets

Page 17: Shrink Analytics

Financial Bucket

• Cash Loss %• Refund %• Chargeback %• Scrap %• Markdowns• On-Hand Adjustments / Scrap

Page 18: Shrink Analytics

Merchandise Trends Bucket

• Months On Hand (COGS / Ending Inventory)• Inventory Turn• Markdowns• On-hand / Not Sold

• Many of these can be evaluated as “whole store” or even more granular “by merchandise category”

Page 19: Shrink Analytics

Human Bucket

• Customer Survey Scores (total and by question)• Turnover• Tenure (Manager / Non-manager)• Training/Certification Compliance Rates• Workers Comp Rates• Payroll to plan (LP and sales separately)• Engagement Scores

Page 20: Shrink Analytics

LP Statistics

LP Staff• Internals• Externals• LP Productivity

Technology• EAS Activations

POS Exceptions• Voids• Dummy SKU usage• No receipt refunds• Line item voids

Compliance / Process• Store Self Inspection

Score• LP Audit Score

Page 21: Shrink Analytics

Completing A Regression Analysis

Page 22: Shrink Analytics

Regression Overview

• Define Regression Analysis

• Everyday life examples

• Making the transition to Loss Prevention

• Running a regression to predict shrink

• Questions

Page 23: Shrink Analytics

Regression

A method used to identify and measure the relationship between two or more variables

In regression there is always one “dependent” variable, and one or more “independent” variables.

The benefit of using regression, is that you can make reasonable estimates about expected results.

Page 24: Shrink Analytics

Regression in Everyday Life

Page 25: Shrink Analytics

Regression in Everyday Life

PriceAge

Ne

we

r -

Old

er

Low

er -

Hig

her

PriceMileage

Less

- M

ore

Low

er -

Hig

her

Independent Dependent

Independent Dependent

Pearson = -.844

Pearson = -.734

Page 26: Shrink Analytics

Regression in Everyday Life

Lowest Mileage

Highest Mileage

USED CAR ADS

Year Make ModelList Price Miles

2007 Honda Accord $20,599 18,998

2007 Honda Accord $18,499 18,205

2007 Honda Accord $17,499 15,155

2007 Honda Accord $17,499 34,802

Notice that both vehicles are listed at $17,499.

Page 27: Shrink Analytics

Regression in Everyday Life

• It appears that at least one of the prices is too high.

• How can we determine what the correct price should be?

• We can pull sample data and run a regression analysis in Excel!

Page 28: Shrink Analytics

Regression in Everyday Life

Pulling Sample Data

The order of the data is important.

In Excel Regression always put the dependent (price) variable to the left of the independent variables.

The independent variables (age and miles) should be placed in the columns next to the dependent variable.

Page 29: Shrink Analytics

Regression in Everyday Life

Start by selecting Tools on the top menu

Then select Data Analysis…

Page 30: Shrink Analytics

Regression in Everyday Life

The Data Analysis dialogue box will open.

Scroll down in the dialogue box and select Regression.

Page 31: Shrink Analytics

Regression in Everyday Life

The Regression dialogue box will open up.

In the box Input Y, we will define the range of our dependent variable including the title. Price is in column B.

Next, in the Input X box we will select the range for the independent variables. Age and mileage are in columns C and D.

Finally. Check the Labels Box

Page 32: Shrink Analytics

Regression in Everyday Life

Here we can see the “Multiple R” is .859. Like the Pearson Correlation coefficient, the closer to 1 this number is, the more accurate the estimations made below.

The area that we want to focus on is right here.

Page 33: Shrink Analytics

Regression in Everyday Life

CoefficientsIntercept 20100.06127Age -964.6729734Miles (K's) -28.76870811

The Honda Accord Coefficients

Start with baseline price.

Each year old subtract.

Each 1K miles subtract.

So how much should we expect to pay for a 2007 Honda Accord with no more than 20,000 miles on it?

Page 34: Shrink Analytics

Regression in Everyday Life

A 2007 Honda Accord is 2 years old and has 20,000 miles on it.

•Per the Regression we should start with $20,100 as a base price.

•For each year old the vehicle is we should subtract -$964.67. In this case our vehicle is two years old which equates to = - $1,929 (2 * -$964.67).

•Finally for each 1,000 miles we should subtract -$28.77. For 20K miles we estimate on our vehicle, this would equate to -$575 (20 * -$28.77).

•Therefore a 2007 Honda Accord with 20,000 miles should cost us about $20,100 - $1,929 - $575 or $17,596.

Page 35: Shrink Analytics

Regression in Everyday Life

What is it really worth?

USED CAR ADS

Year Make Model List Price Miles

2007 Honda Accord $20,599 18,998

2007 Honda Accord $18,499 18,205

2007 Honda Accord $17,499 15,155

2007 Honda Accord $17,499 34,802

$17,624

$17,647

$17,735

$17,170

FMV

Page 36: Shrink Analytics

How many games should a team win?

Rushing YPGINT Give

Pass YD/ATT3rd Down Conversions

Defensive Sacks

Page 37: Shrink Analytics

Predicting Wins using Excel RegressionRUSH YPG INT Give PASSING YDS/ATT 3RD DOWN CONVERSION DEF SACKS

PEARSON 0.48 -0.43 0.56 0.55 0.56

Team Actual Wins RUSH YPG INT Give Passing Yards/Att 3rd down Conversion Defensive Sacks49ers 7 100 19 6.0 37.9 30Bears 9 105 14 5.5 35.6 28Bengals 4 95 15 4.3 34.7 17Bills 7 115 15 5.9 39.9 24Broncos 8 116 18 7.1 47.5 26Browns 4 100 20 4.7 34.0 17Buccaneers 9 115 13 6.1 38.4 29Cardinals 9 74 15 7.1 41.9 31Chargers 8 108 11 7.7 45.9 28Chiefs 2 113 16 5.4 38.3 10Colts 12 80 12 6.8 50.2 30Cowboys 9 108 20 6.6 42.9 59Dolphins 11 119 7 7.0 37.0 40Eagles 9 106 16 6.2 41.3 48Falcons 11 153 11 7.4 43.4 34Giants 12 157 10 6.1 43.1 42Jaguars 5 111 13 5.8 40.8 29Jets 9 125 23 5.9 41.1 41Lions 0 83 19 5.3 28.8 30Packers 6 113 13 6.6 44.2 27Panthers 12 152 12 7.3 39.3 37Patriots 11 142 11 6.1 43.2 31Raiders 5 124 11 5.2 28.5 32Rams 2 103 19 5.2 31.9 30Ravens 11 149 12 6.0 40.9 34Redskins 8 131 6 5.5 35.2 24Saints 8 100 18 7.7 48.5 28Seahawks 4 111 15 5.1 31.3 35Steelers 12 106 15 6.0 41.1 51Texans 8 115 20 7.3 42.1 25Titans 13 137 9 6.1 36.1 44Vikings 10 146 17 6.0 39.4 45

Page 38: Shrink Analytics

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.870646627R Square 0.75802555Adjusted R Square 0.711492002Standard Error 1.786591886Observations 32

ANOVAdf SS MS F Significance F

Regression 5 259.9790753 51.99581506 16.28987216 2.69281E-07Residual 26 82.98967472 3.191910566Total 31 342.96875

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept -8.624953688 3.640415735 -2.369222175 0.025534715 -16.10793533 -1.141972048 -16.10793533 -1.141972048RUSH YPG 0.031743312 0.017309459 1.833870807 0.078144635 -0.003836791 0.067323415 -0.003836791 0.067323415INT Give -0.269060522 0.088673509 -3.034283032 0.005414563 -0.451331528 -0.086789517 -0.451331528 -0.086789517Passing Yards/Att 0.152764983 0.59142283 0.258300788 0.798208539 -1.062922042 1.368452008 -1.062922042 1.3684520083rd down Conversion 0.285355841 0.093728168 3.044504617 0.005281274 0.092694834 0.478016848 0.092694834 0.478016848Defensive Sacks 0.142335589 0.034024835 4.183285167 0.000289462 0.072396539 0.212274639 0.072396539 0.212274639

Looking at the Multiple R we can see that the value is .870 which is very close to 1 and indicates that these five metrics combined have a strong correlation to victories.

Predicting Wins Output

Again we only want to focus here.

Page 39: Shrink Analytics

Predicting Wins Regression

CoefficientsIntercept -8.624953688RUSH YPG 0.031743312INT Give -0.269060522Passing Yards/Att 0.1527649833rd down Conversion 0.285355841Defensive Sacks 0.142335589

Baseline

Page 40: Shrink Analytics

Predicting Wins Regression

• Pittsburgh won the Super Bowl.• According to the regression how many wins

should Pittsburgh have gotten based on the following information?

• How many should the Lions have won?

TeamRUSH YPG INT Give

Passing Yards/Att

3rd down Conversion

Defensive Sacks

Steelers 105.6 15 5.95 41.1 51Lions 83.3 19 5.28 28.8 30

Page 41: Shrink Analytics

Predicting Wins Regression

TeamRUSH YPG INT Give

Passing Yards/Att

3rd down Conversion

Defensive Sacks

Steelers 105.6 15 5.95 41.1 51Lions 83.3 19 5.28 28.8 30

Start (Intercept) Rush YPG INT Give

Passing Yards/Att

3rd Down Conversion

Defensive Sacks

Predicted Wins

Basis -8.625 0.032 -0.269 0.153 0.285 0.142

Steelers -8.625 3.352 -4.036 0.909 11.728 7.259 11Lions -8.625 2.644 -5.112 0.807 8.218 4.270 2

Page 42: Shrink Analytics

Predicting Wins Regression

JCP LOSS PREVENTIONNFL WINS PREDICTOR MODEL

Team Prediction Actual Var Team Prediction Actual Var49ers 5 7 2 Jaguars 8 5 -3Bears 6 9 3 Jets 8 9 1Bengals 3 4 1 Lions 2 0 -2Bills 7 7 0 Packers 9 6 -3Broncos 9 8 -1 Panthers 11 12 1Browns 2 4 2 Patriots 11 11 0Buccaneers 8 9 1 Raiders 6 5 -1Cardinals 7 9 2 Rams 4 2 -2Chargers 10 8 -2 Ravens 10 11 1Chiefs 4 2 -2 Redskins 8 8 0Colts 10 12 2 Saints 9 8 -1Cowboys 11 9 -2 Seahawks 6 4 -2Dolphins 11 11 0 Steelers 11 12 1Eagles 10 9 -1 Texans 6 8 2Falcons 12 11 -1 Titans 11 13 2Giants 13 12 -1 Vikings 10 10 0

Page 43: Shrink Analytics

Transitioning to Loss Prevention

Dependant (Predicted) Variables • Price, Wins, Shrink %

Independent (Data) Variables•Age, Miles, Rush YPG, Def Sacks, Refunds, Over-Short Cash

Page 44: Shrink Analytics

Predicting Shrink

• Myths

• Where do I start?

1. You can predict shrink with 100% accuracy.

2. There are too many variables to provide an

accurate prediction.

Page 45: Shrink Analytics

External Apps per 100 Hours

Over/Short Cash

Customer Survey…Variety of

Merchandise

Store Associate Turnover

Shrink Predictor Tool

Shrink %

Independent Variables Dependent Variable

Page 46: Shrink Analytics

Shrink Predictor Tool

Shrink Predictor Tool - RILA2008 Regression Analysis

PEARSON -0.162 0.411 -0.164 0.145

Region Dist Store

2008 Shrink Rate

Ext Apps Per 100

LPO HRSO/S Cash

%

Customer Survey..Variety

of Merch

Store Associate Voluntary Turnover

Page 47: Shrink Analytics

Shrink Predictor Tool - RILA2008 Regression Analysis

PEARSON -0.162 0.411 -0.164 0.145

Region Dist Store

2008 Shrink Rate

Ext Apps Per 100

LPO HRSO/S Cash

%

Customer Survey..Variety

of Merch

Store Associate Voluntary Turnover

3 4111 2063 2.24% 2.05 0.0125% 0% 44%1 4053 2039 2.70% 1.67 0.0465% 38% 67%7 4210 524 1.99% 3.08 0.0299% 41% 78%7 4215 170 1.08% 1.76 0.0204% 0% 85%3 4106 1020 1.68% 2.53 0.0181% 0% 65%3 4112 1552 0.82% 2.52 0.0158% 46% 87%1 4053 714 1.39% 1.43 0.0187% 36% 77%

Shrink Predictor Tool

Page 48: Shrink Analytics

Build your own Shrink Predictor Report

Update your information in these 5 columns

Page 49: Shrink Analytics

Shrink Predictor Tool

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.441283549R Square 0.194731171Adjusted R Square 0.177506169Standard Error 0.00604101Observations 192

ANOVAdf SS MS F Significance F

Regression 4 0.001650271 0.000412568 11.30514669 3.08277E-08Residual 187 0.006824341 3.64938E-05Total 191 0.008474612

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 0.012501899 0.002179971 5.734891415 3.8634E-08 0.008201402 0.016802396 0.008201402 0.016802396Internal Apps per $1M Sales -0.000411294 0.000291278 -1.412032782 0.159602203 -0.000985907 0.000163319 -0.000985907 0.000163319O/S Cash % 17.06975635 3.18630046 5.357233745 2.46469E-07 10.78404269 23.35547001 10.78404269 23.35547001Customer Survey..Variety of Merch -0.004943738 0.002577291 -1.918191974 0.056610062 -0.010028039 0.000140563 -0.010028039 0.000140563Store Associate Voluntary Turnover 0.001559057 0.002519633 0.618763313 0.536825302 -0.003411502 0.006529615 -0.003411502 0.006529615

From the Summary Output, Copy the highlighted cells and paste them into the Coefficient Updater Tab in the Shrink Predictor Workbook.

Page 50: Shrink Analytics

Here we will take the information from the Summary Output and paste it into the yellow boxes on the Coefficient Updater tab.

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.441283549

CoefficientsIntercept 0.012501899Internal Apps per $1M Sales -0.000411294O/S Cash % 17.06975635Customer Survey..Variety of Merch -0.004943738Store Associate Voluntary Turnover 0.001559057

Once you paste the information in the shaded boxes click on the Shrink Predictor tool tab.

Page 51: Shrink Analytics

When you click on the Shrink Predictor Tool tab you will see that the Predicted Shrink column is filled out.

This means that the equation to predict shrink is now functional.

Page 52: Shrink Analytics

Equation based on Historical Data

With Current Data - After

Page 53: Shrink Analytics

Shrink Predictor Tool

• The true value of a Shrink Predictor is to identify stores which are predicted to be materially higher than their past results or to identify outliers in the current model. Not to identify the highest shrink stores.

• In the sample data we provided there are 192 stores. Of the 192 stores, 42 had variances that were greater than the standard deviation of .60 or 21% of the sample. 79% of the stores fell within one deviation.

• In most cases, the more metrics you use, the lower the standard deviation and subsequently the more accurate your prediction will be.

Page 54: Shrink Analytics

Recap of Steps

1. Collect Historical Data (Monthly trickles, not annual) • Test with old data (2007 or 2008) • Run Pearson Correlation to select strongest

metrics.2. Run Regression Analysis on 4 strongest 2007 or

2008 metrics using the tool we provided. • Establish Intercept, MultipleR and Coefficients

3. Apply Regression Results (Intercept, MultipleR and Coefficients) to current 2009 monthly metrics to predict future shrink using the tool provided.

Page 55: Shrink Analytics

Roll-out

• Selling point, “Predict versus React”.• One version of the truth, one focus.

Page 56: Shrink Analytics

Focus on Exceptions

Region Dist Store

2008 Shrink Rate

Internal Apps per

$1M SalesO/S Cash

%

Customer Survey..Variety

of Merch

Store Associate Voluntary Turnover

Predicted Shrink

Variance To 2008

1 Total -7.47%3 Total 0.58%5 Total 7.43%7 Total -3.75%9 Total 3.21%Grand Total 0.00%

Page 57: Shrink Analytics

Focus On Exceptions

Dist Store

2008 Shrink Rate

Internal Apps per

$1M Sales

O/S Cash

%

Customer Survey..

Variety of Merch

Store Associate Voluntary Turnover

Predicted Shrink

Variance To 2008

4207 747 1.30% 1.83 0.02% 36% 81% 1.44% 0.14%4207 778 1.13% 2.21 0.01% 39% 66% 1.26% 0.13%4207 875 1.17% 5.77 0.03% 36% 47% 1.42% 0.25%4207 893 1.20% 1.12 0.04% 35% 85% 1.88% 0.68%4207 1014 0.37% 2.12 0.02% 38% 65% 1.35% 0.98%4207 1406 0.61% 1.43 0.02% 39% 57% 1.42% 0.81%4207 1457 1.32% 1.91 0.04% 0% 102% 2.03% 0.71%4207 1469 2.28% 1.71 0.06% 0% 129% 2.34% 0.06%4207 1996 1.73% 0.96 0.03% 37% 75% 1.71% -0.02%4207 2117 1.40% 2.67 0.03% 37% 98% 1.56% 0.16%4207 2119 1.43% 0.12 0.01% 40% 71% 1.39% -0.05%4207 2129 1.13% 1.77 0.01% 41% 57% 1.26% 0.13%

Page 58: Shrink Analytics

Additional Considerations

• Segment stores if diversity is pronounced, consider:

• Box size• Box format• Volume• Risk Rating• Brand• Technology (CCTV / EAS)• Major event (remodel, crisis event)

• Use the same methodology to create Risk Ratings on static data. (crime index, census info, 3YR shrink, etc.)

Page 59: Shrink Analytics

Questions

Michael SandersShrinkage Control AnalystJ.C. Penney Company, Inc. [email protected]

• To download the Shrink Predictor Tool, visit: http://www.rila.org/protection/resources/Documents/SHRINKPREDICTORTOOL.xls

• To view the help guide for the Shrink Predictor Tool, visit: http://www.rila.org/protection/resources/Documents/SHRINKPREDICTORTOOLHELPGUIDE.pdf

• Google “Regression Analysis” or “Pearson Correlation”• Search Excel Help for “Regression Analysis” or “Pearson

Correlation”• www.visualstatistics.net