Shrink Analytics
Michael Sanders
Shrinkage Control Analyst
J.C. Penney Company, Inc.
$18B
1093 Stores
147K Associates
Basic Correlations and Flaws
Audit
Average
Median
Shrink Range
Score
90's
80's
Failures
Shrink
1.69
1.83
1.93
Shrink
1.73
1.83
1.90
Low
0.09
0.14
0.48
*Shrink results grouped by audit score range
High
9.08
10.03
6.98
Quartiles
Shrink
Shrink
Audit Score
Quartile
1
2
3
4
Average
0.93
1.55
2.11
3.34
Ave
88
87
87
85
* Shrink quartiles with average audit score
Regression Analysis Result
Audit Baseline or
Slope
Score "Intercept"
Slope times Estimated
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%
Regression Analysis on the NFL
INT Take
INT Give
Fumble Take
Fumble Give
Penalty YDS
Pass YD/ATT
Rush YT/ATT
Completion %
Rush YPG, Pass Yards PG
3rd Down Conversions
4th Down Conversions
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.
Defense & Offensive Stats
-1
0
+1
Pearson Correlation (Positive) – Wins/RSH YPG
0.48
RUSH
Team
Wins YPG
Titans
13
137
Colts
12
80
Giants
12
157
Panthers
12
152
Steelers
12
106
Dolphins
11
119
Falcons
11
153
Patriots
11
142
Ravens
11
149
Vikings
10
146
Bears
9
105
Buccaneers 9
115
Cardinals
9
74
Cowboys
9
108
Eagles
9
106
Jets
9
125
Broncos
8
116
Chargers
8
108
Redskins
8
131
Saints
8
100
Texans
8
115
49ers
7
100
Bills
7
115
Packers
6
113
Jaguars
5
111
Raiders
5
124
Bengals
4
95
Browns
4
100
Seahawks
4
111
Chiefs
2
113
Rams
2
103
Lions
0
83
.48 Rush Yards PG
Correlation to Wins
-1
0
+1
Pearson Correlation (Negative) – INT (Give)
0.48 -0.43
RUSH INT
Team
Wins YPG Give
Titans
13
137
9
Colts
12
80
12
Giants
12
157
10
Panthers
12
152
12
Steelers
12
106
15
Dolphins
11
119
7
Falcons
11
153
11
Patriots
11
142
11
Ravens
11
149
12
Vikings
10
146
17
Bears
9
105
14
Buccaneers 9
115
13
Cardinals
9
74
15
Cowboys
9
108
20
Eagles
9
106
16
Jets
9
125
23
Broncos
8
116
18
Chargers
8
108
11
Redskins
8
131
6
Saints
8
100
18
Texans
8
115
20
49ers
7
100
19
Bills
7
115
15
Packers
6
113
13
Jaguars
5
111
13
Raiders
5
124
11
Bengals
4
95
15
Browns
4
100
20
Seahawks
4
111
15
Chiefs
2
113
16
Rams
2
103
19
Lions
0
83
19
-1
.48 Rush Yards PG
-.43 INT Give
Correlation to Wins
0
+1
Pearson Results
0.48 -0.43 0.30
0.35 -0.18 -0.24
0.03
RUSH INT PASS INT
FUM FUM PENALTY
YPG Give YPG (Take) (take) Give
YDS
Wins Team
7 49ers
100
19
211
12
6
16
732
9 Bears
105
14
191
22
10
13
610
4 Bengals
95
15
150
12
12
11
591
7 Bills
115
15
190
10
12
15
538
8 Broncos
116
18
279
6
7
12
739
4 Browns
100
20
149
23
8
6
669
9 Buccaneers 115
13
226
22
8
13
834
9 Cardinals
74
15
292
13
17
15
781
8 Chargers
108
11
241
15
9
9
748
2 Chiefs
113
16
196
13
16
8
645
12 Colts
80
12
256
15
11
5
619
9 Cowboys
108
20
237
8
14
13
952
11 Dolphins
119
7
227
18
12
6
669
9 Eagles
106
16
244
15
14
10
635
11 Falcons
153
11
209
10
8
10
591
12 Giants
157
10
199
17
5
3
821
5 Jaguars
111
13
208
13
4
11
813
9 Jets
125
23
206
14
16
8
569
0 Lions
83
19
185
4
16
10
729
6 Packers
113
13
238
22
6
8
984
12 Panthers
152
12
197
12
13
7
637
11 Patriots
142
11
223
14
8
10
501
5 Raiders
124
11
148
16
8
12
823
2 Rams
103
19
184
12
14
12
718
11 Ravens
149
12
176
26
8
9
785
8 Redskins
131
6
189
13
5
12
644
8 Saints
100
18
311
15
7
8
797
4 Seahawks
111
15
164
9
11
12
601
12 Steelers
106
15
206
20
9
10
812
8 Texans
115
20
267
12
10
12
664
13 Titans
137
9
176
20
11
8
855
10 Vikings
146
17
185
12
13
14
692
0.48 -0.43 0.30
Wins Team
7 49ers
9 Bears
0.35
-0.18 -0.24
0.03
RUSH INT PASS INT
FUM FUM PENALTY
YPG Give YPG (Take) (take) Give
YDS
100
19
211
12
6
16
732
105
14
191
22
10
13
610
-1
0
.55 3rd down conversions
.56 Pass YD/ATT
.48 Rush Yards PG
.49 Completion %
.03 Penalty YDS
.04 4th down conversions
.15 Rush YT/ATT
.21 INT Take
.30 Pass Yards PG
-.18 Fumble Take
-.24 Fumble Give
-.43 INT Give
Correlation to Wins
+1
Multiple Buckets
Financial Bucket
•
•
•
•
•
•
Cash Loss %
Refund %
Chargeback %
Scrap %
Markdowns
On-Hand Adjustments / Scrap
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”
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
LP Statistics
LP Staff
• Internals
• Externals
• LP Productivity
POS Exceptions
• Voids
• Dummy SKU usage
• No receipt refunds
• Line item voids
Technology
• EAS Activations
Compliance / Process
• Store Self Inspection
Score
• LP Audit Score
Completing A Regression Analysis
Regression Overview
• Define Regression Analysis
• Everyday life examples
• Making the transition to Loss Prevention
• Running a regression to predict shrink
• Questions
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.
Regression in Everyday Life
Pearson = -.844
Dependent
Independent
Mileage
Independent
Price
Pearson = -.734
Price
Dependent
Lower - Higher
Age
Lower - Higher
Less - More
Newer - Older
Regression in Everyday Life
Regression in Everyday Life
USED CAR ADS
List
Price
Miles
Notice that both
vehicles are listed
at $17,499.
Year
Make
Model
2007
Honda
Accord
$20,599 18,998
2007
Honda
Accord
$18,499 18,205
2007
Honda
Accord
$17,499 15,155
Lowest Mileage
2007
Honda
Accord
$17,499 34,802
Highest Mileage
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!
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.
Regression in Everyday Life
Start by selecting Tools
on the top menu
Then select Data Analysis…
Regression in Everyday Life
The Data Analysis dialogue
box will open.
Scroll down in the dialogue
box and select Regression.
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
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.
Regression in Everyday Life
The Honda Accord Coefficients
Intercept
Age
Miles (K's)
Coefficients
20100.06127
-964.6729734
-28.76870811
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?
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.
Regression in Everyday Life
What is it really worth?
USED CAR ADS
List Price
Miles
FMV
Year
Make
Model
2007
Honda
Accord
$20,599
18,998
$17,624
2007
Honda
Accord
$18,499
18,205
$17,647
2007
Honda
Accord
$17,499
15,155
$17,735
2007
Honda
Accord
$17,499
34,802
$17,170
How many
games should a
team win?
Rushing YPG
INT Give
Pass YD/ATT
3rd Down Conversions
Defensive Sacks
Predicting Wins using Excel Regression
PEARSON
Team
49ers
Bears
Bengals
Bills
Broncos
Browns
Buccaneers
Cardinals
Chargers
Chiefs
Colts
Cowboys
Dolphins
Eagles
Falcons
Giants
Jaguars
Jets
Lions
Packers
Panthers
Patriots
Raiders
Rams
Ravens
Redskins
Saints
Actual Wins
7
9
4
7
8
4
9
9
8
2
12
9
11
9
11
12
5
9
0
6
12
11
5
2
11
8
8
RUSH YPG
0.48
INT Give
-0.43
RUSH YPG
100
105
95
115
116
100
115
74
108
113
80
108
119
106
153
157
111
125
83
113
152
142
124
103
149
131
100
INT Give
19
14
15
15
18
20
13
15
11
16
12
20
7
16
11
10
13
23
19
13
12
11
11
19
12
6
18
PASSING YDS/ATT 3RD DOWN CONVERSION
0.56
0.55
Passing Yards/Att
6.0
5.5
4.3
5.9
7.1
4.7
6.1
7.1
7.7
5.4
6.8
6.6
7.0
6.2
7.4
6.1
5.8
5.9
5.3
6.6
7.3
6.1
5.2
5.2
6.0
5.5
7.7
3rd down Conversion
37.9
35.6
34.7
39.9
47.5
34.0
38.4
41.9
45.9
38.3
50.2
42.9
37.0
41.3
43.4
43.1
40.8
41.1
28.8
44.2
39.3
43.2
28.5
31.9
40.9
35.2
48.5
DEF SACKS
0.56
Defensive Sacks
30
28
17
24
26
17
29
31
28
10
30
59
40
48
34
42
29
41
30
27
37
31
32
30
34
24
28
Predicting Wins Output
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.870646627
R Square
0.75802555
Adjusted R Square
0.711492002
Standard Error
1.786591886
Observations
32
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.
ANOVA
df
Regression
Residual
Total
Intercept
RUSH YPG
INT Give
Passing Yards/Att
3rd down Conversion
Defensive Sacks
5
26
31
SS
259.9790753
82.98967472
342.96875
MS
F
Significance F
51.99581506 16.28987216
2.69281E-07
3.191910566
Coefficients Standard Error
t Stat
P-value
-8.624953688
3.640415735 -2.369222175 0.025534715
0.031743312
0.017309459 1.833870807 0.078144635
-0.269060522
0.088673509 -3.034283032 0.005414563
0.152764983
0.59142283 0.258300788 0.798208539
0.285355841
0.093728168 3.044504617 0.005281274
0.142335589
0.034024835 4.183285167 0.000289462
Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
-16.10793533 -1.141972048 -16.10793533 -1.141972048
-0.003836791 0.067323415 -0.003836791 0.067323415
-0.451331528 -0.086789517 -0.451331528 -0.086789517
-1.062922042 1.368452008 -1.062922042 1.368452008
0.092694834 0.478016848 0.092694834 0.478016848
0.072396539 0.212274639 0.072396539 0.212274639
Again we only want to focus here.
Predicting Wins Regression
Intercept
RUSH YPG
INT Give
Passing Yards/Att
3rd down Conversion
Defensive Sacks
Coefficients
-8.624953688
0.031743312
-0.269060522
0.152764983
0.285355841
0.142335589
Baseline
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?
Team
Steelers
Lions
RUSH
YPG
105.6
83.3
Passing 3rd down
INT Give Yards/Att Conversion
15
5.95
41.1
19
5.28
28.8
Defensive
Sacks
51
30
Predicting Wins Regression
RUSH
YPG
INT Give
105.6
15
83.3
19
Team
Steelers
Lions
Basis
Steelers
Lions
Start
(Intercept) Rush YPG INT Give
-8.625
0.032
-0.269
-8.625
3.352
-4.036
-8.625
2.644
-5.112
Passing
3rd down
Yards/Att Conversion
5.95
41.1
5.28
28.8
Defensive
Sacks
51
30
Passing
Yards/Att
0.153
0.909
3rd Down
Conversion
0.285
11.728
Defensive
Sacks
0.142
7.259
0.807
8.218
4.270
Predicted
Wins
11
2
Predicting Wins Regression
JCP LOSS PREVENTION
NFL WINS PREDICTOR MODEL
Team
49ers
Bears
Bengals
Bills
Broncos
Browns
Buccaneers
Cardinals
Chargers
Chiefs
Colts
Cowboys
Dolphins
Eagles
Falcons
Giants
Prediction
5
6
3
7
9
2
8
7
10
4
10
11
11
10
12
13
Actual
7
9
4
7
8
4
9
9
8
2
12
9
11
9
11
12
Var
2
3
1
0
-1
2
1
2
-2
-2
2
-2
0
-1
-1
-1
Team
Jaguars
Jets
Lions
Packers
Panthers
Patriots
Raiders
Rams
Ravens
Redskins
Saints
Seahawks
Steelers
Texans
Titans
Vikings
Prediction
8
8
2
9
11
11
6
4
10
8
9
6
11
6
11
10
Actual
5
9
0
6
12
11
5
2
11
8
8
4
12
8
13
10
Var
-3
1
-2
-3
1
0
-1
-2
1
0
-1
-2
1
2
2
0
Transitioning to Loss Prevention
Dependant (Predicted) Variables
• Price, Wins, Shrink %
Independent (Data) Variables
• Age, Miles, Rush YPG, Def Sacks,
Refunds, Over-Short Cash
Predicting Shrink
• Myths
1. You can predict shrink with 100% accuracy.
2. There are too many variables to provide an accurate
prediction.
• Where do I start?
Shrink Predictor Tool
Independent Variables
Dependent Variable
External Apps per 100 Hours
Over/Short Cash
Customer Survey…Variety of
Merchandise
Store Associate Turnover
Shrink %
Shrink Predictor Tool
Shrink Predictor Tool - RILA
2008 Regression Analysis
PEARSON
Region Dist
-0.162
Store
2008
Shrink
Rate
Ext Apps
Per 100
LPO HRS
0.411
O/S Cash
%
-0.164
0.145
Customer
Survey..Variety
of Merch
Store
Associate
Voluntary
Turnover
Shrink Predictor Tool
Shrink Predictor Tool - RILA
2008 Regression Analysis
PEARSON
Region
3
1
7
7
3
3
1
Dist
4111
4053
4210
4215
4106
4112
4053
-0.162
Store
2063
2039
524
170
1020
1552
714
2008
Ext Apps
Shrink
Per 100
LPO HRS
Rate
2.24%
2.05
2.70%
1.67
1.99%
3.08
1.08%
1.76
1.68%
2.53
0.82%
2.52
1.39%
1.43
0.411
O/S Cash
%
0.0125%
0.0465%
0.0299%
0.0204%
0.0181%
0.0158%
0.0187%
-0.164
0.145
Customer
Survey..Variety
of Merch
0%
38%
41%
0%
0%
46%
36%
Store
Associate
Voluntary
Turnover
44%
67%
78%
85%
65%
87%
77%
Build your own Shrink Predictor Report
Update your information in these 5 columns
Shrink Predictor Tool
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.441283549
0.194731171
0.177506169
0.00604101
192
From the Summary Output, Copy the
highlighted cells and paste them into the
Coefficient Updater Tab in the Shrink
Predictor Workbook.
ANOVA
df
Regression
Residual
Total
Intercept
Internal Apps per $1M Sales
O/S Cash %
Customer Survey..Variety of Merch
Store Associate Voluntary Turnover
4
187
191
SS
MS
F
Significance F
0.001650271 0.000412568 11.30514669 3.08277E-08
0.006824341 3.64938E-05
0.008474612
Coefficients Standard Error
0.012501899 0.002179971
-0.000411294 0.000291278
17.06975635
3.18630046
-0.004943738 0.002577291
0.001559057 0.002519633
t Stat
5.734891415
-1.412032782
5.357233745
-1.918191974
0.618763313
P-value
3.8634E-08
0.159602203
2.46469E-07
0.056610062
0.536825302
Lower 95%
0.008201402
-0.000985907
10.78404269
-0.010028039
-0.003411502
Upper 95%
0.016802396
0.000163319
23.35547001
0.000140563
0.006529615
Lower 95.0%
0.008201402
-0.000985907
10.78404269
-0.010028039
-0.003411502
Upper 95.0%
0.016802396
0.000163319
23.35547001
0.000140563
0.006529615
Here we will take the information
from the Summary Output and
paste it into the yellow boxes on
the Coefficient Updater tab.
Once you paste the information
in the shaded boxes click on the
Shrink Predictor tool tab.
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.
Equation
With Current
based Data
on Historical
- After
Data
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.
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.
Roll-out
• Selling point, “Predict versus React”.
• One version of the truth, one focus.
Focus on Exceptions
Region Dist
1 Total
3 Total
5 Total
7 Total
9 Total
Grand Total
Store
2008
Shrink
Rate
Internal
Apps per O/S Cash
$1M Sales
%
Customer
Survey..Variety
of Merch
Store
Associate
Voluntary
Turnover
Predicted
Shrink
Variance
To 2008
-7.47%
0.58%
7.43%
-3.75%
3.21%
0.00%
Focus On Exceptions
Dist
4207
4207
4207
4207
4207
4207
4207
4207
4207
4207
4207
4207
Store
747
778
875
893
1014
1406
1457
1469
1996
2117
2119
2129
2008
Internal
Shrink Apps per
$1M Sales
Rate
1.30%
1.83
1.13%
2.21
1.17%
5.77
1.20%
1.12
0.37%
2.12
0.61%
1.43
1.32%
1.91
2.28%
1.71
1.73%
0.96
1.40%
2.67
1.43%
0.12
1.13%
1.77
O/S
Cash
%
0.02%
0.01%
0.03%
0.04%
0.02%
0.02%
0.04%
0.06%
0.03%
0.03%
0.01%
0.01%
Customer
Store
Survey..
Associate
Variety of Voluntary Predicted
Merch
Turnover
Shrink
36%
81%
1.44%
39%
66%
1.26%
36%
47%
1.42%
35%
85%
1.88%
38%
65%
1.35%
39%
57%
1.42%
0%
102%
2.03%
0%
129%
2.34%
37%
75%
1.71%
37%
98%
1.56%
40%
71%
1.39%
41%
57%
1.26%
Variance
To 2008
0.14%
0.13%
0.25%
0.68%
0.98%
0.81%
0.71%
0.06%
-0.02%
0.16%
-0.05%
0.13%
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.)
Questions
Michael Sanders
Shrinkage Control Analyst
J.C. Penney Company, Inc.
msande45@jcpenney.com
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To download the Shrink Predictor Tool, visit:
http://www.rila.org/protection/resources/Documents/SHRINKPRE
DICTORTOOL.xls
To view the help guide for the Shrink Predictor Tool, visit:
http://www.rila.org/protection/resources/Documents/SHRINKPRE
DICTORTOOLHELPGUIDE.pdf
Google “Regression Analysis” or “Pearson Correlation”
Search Excel Help for “Regression Analysis” or “Pearson Correlation”
www.visualstatistics.net