Store24(A)
Poornachandra Rao
December 21, 2017
The RMd document contains the analysis of “Store24(A):Managing Employee Retention” harvard case study.
Reading Store24(A) data in to R.
store <- read.csv(paste("Store24.csv",sep=""))
summary(store)## store Sales Profit MTenure
## Min. : 1.0 Min. : 699306 Min. :122180 Min. : 0.00
## 1st Qu.:19.5 1st Qu.: 984579 1st Qu.:211004 1st Qu.: 6.67
## Median :38.0 Median :1127332 Median :265014 Median : 24.12
## Mean :38.0 Mean :1205413 Mean :276314 Mean : 45.30
## 3rd Qu.:56.5 3rd Qu.:1362388 3rd Qu.:331314 3rd Qu.: 50.92
## Max. :75.0 Max. :2113089 Max. :518998 Max. :277.99
## CTenure Pop Comp Visibility
## Min. : 0.8871 Min. : 1046 Min. : 1.651 Min. :2.00
## 1st Qu.: 4.3943 1st Qu.: 5616 1st Qu.: 3.151 1st Qu.:3.00
## Median : 7.2115 Median : 8896 Median : 3.629 Median :3.00
## Mean : 13.9315 Mean : 9826 Mean : 3.788 Mean :3.08
## 3rd Qu.: 17.2156 3rd Qu.:14104 3rd Qu.: 4.230 3rd Qu.:4.00
## Max. :114.1519 Max. :26519 Max. :11.128 Max. :5.00
## PedCount Res Hours24 CrewSkill
## Min. :1.00 Min. :0.00 Min. :0.00 Min. :2.060
## 1st Qu.:2.00 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:3.225
## Median :3.00 Median :1.00 Median :1.00 Median :3.500
## Mean :2.96 Mean :0.96 Mean :0.84 Mean :3.457
## 3rd Qu.:4.00 3rd Qu.:1.00 3rd Qu.:1.00 3rd Qu.:3.655
## Max. :5.00 Max. :1.00 Max. :1.00 Max. :4.640
## MgrSkill ServQual
## Min. :2.957 Min. : 57.90
## 1st Qu.:3.344 1st Qu.: 78.95
## Median :3.589 Median : 89.47
## Mean :3.638 Mean : 87.15
## 3rd Qu.:3.925 3rd Qu.: 99.90
## Max. :4.622 Max. :100.00Three very important variables in this analysis are the store Profit, the management tenure (MTenure) and the crew tenure (CTenure).
Measuring the mean and standard deviation of Profit.
Mean <-mean(store$Profit)
Standard_D <- sd(store$Profit)
table1 <- xtabs(~Mean+Standard_D)
table1## Standard_D
## Mean 89404.0763380618
## 276313.613333333 1Measuring the mean and standard deviation of MTenure.
Mean <-mean(store$MTenure)
Standard_D <- sd(store$MTenure)
table2 <- xtabs(~Mean+Standard_D)
table2## Standard_D
## Mean 57.6715511971043
## 45.296443888 1Measuring the mean and standard deviation of CTenure.
Mean <-mean(store$CTenure)
Standard_D <- sd(store$CTenure)
table3 <- xtabs(~Mean+Standard_D,data=store)
table3## Standard_D
## Mean 17.6975170602746
## 13.9314986093333 1Printing the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.
most_profit <- store[order(-store$Profit),]
most_profit[1:10,2:5]## Sales Profit MTenure CTenure
## 74 1782957 518998 171.09720 29.519510
## 7 1809256 476355 62.53080 7.326488
## 9 2113089 474725 108.99350 6.061602
## 6 1703140 469050 149.93590 11.351130
## 44 1807740 439781 182.23640 114.151900
## 2 1619874 424007 86.22219 6.636550
## 45 1602362 410149 47.64565 9.166325
## 18 1704826 394039 239.96980 33.774130
## 11 1583446 389886 44.81977 2.036961
## 47 1665657 387853 12.84790 6.636550Printing the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.
least_profit <- store[order(-store$Profit),]
least_profit[66:75,2:5]## Sales Profit MTenure CTenure
## 37 1202917 187765 23.1985000 1.347023
## 61 716589 177046 21.8184200 13.305950
## 52 1073008 169201 24.1185600 3.416838
## 54 811190 159792 6.6703910 3.876797
## 13 857843 152513 0.6571813 1.577002
## 32 828918 149033 36.0792600 6.636550
## 55 925744 147672 6.6703910 18.365500
## 41 744211 147327 14.9180200 11.926080
## 66 879581 146058 115.2039000 3.876797
## 57 699306 122180 24.3485700 2.956879Scatter plot of Profit vs. MTenure.
library(car)
scatterplot(Profit~MTenure, main ="Scatter plot of Profit vs. MTenure",pch=19,data=store)
Sscatter plot of Profit vs. CTenure.
library(car)
scatterplot(Profit~CTenure, main ="Scatter plot of Profit vs. CTenure",data=store,pch=19)
Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)
#Correlation Matrix
cor_M <- cor(store)
round(cor_M,2)## store Sales Profit MTenure CTenure Pop Comp Visibility
## store 1.00 -0.23 -0.20 -0.06 0.02 -0.29 0.03 -0.03
## Sales -0.23 1.00 0.92 0.45 0.25 0.40 -0.24 0.13
## Profit -0.20 0.92 1.00 0.44 0.26 0.43 -0.33 0.14
## MTenure -0.06 0.45 0.44 1.00 0.24 -0.06 0.18 0.16
## CTenure 0.02 0.25 0.26 0.24 1.00 0.00 -0.07 0.07
## Pop -0.29 0.40 0.43 -0.06 0.00 1.00 -0.27 -0.05
## Comp 0.03 -0.24 -0.33 0.18 -0.07 -0.27 1.00 0.03
## Visibility -0.03 0.13 0.14 0.16 0.07 -0.05 0.03 1.00
## PedCount -0.22 0.42 0.45 0.06 -0.08 0.61 -0.15 -0.14
## Res -0.03 -0.17 -0.16 -0.06 -0.34 -0.24 0.22 0.02
## Hours24 0.03 0.06 -0.03 -0.17 0.07 -0.22 0.13 0.05
## CrewSkill 0.05 0.16 0.16 0.10 0.26 0.28 -0.04 -0.20
## MgrSkill -0.07 0.31 0.32 0.23 0.12 0.08 0.22 0.07
## ServQual -0.32 0.39 0.36 0.18 0.08 0.12 0.02 0.21
## PedCount Res Hours24 CrewSkill MgrSkill ServQual
## store -0.22 -0.03 0.03 0.05 -0.07 -0.32
## Sales 0.42 -0.17 0.06 0.16 0.31 0.39
## Profit 0.45 -0.16 -0.03 0.16 0.32 0.36
## MTenure 0.06 -0.06 -0.17 0.10 0.23 0.18
## CTenure -0.08 -0.34 0.07 0.26 0.12 0.08
## Pop 0.61 -0.24 -0.22 0.28 0.08 0.12
## Comp -0.15 0.22 0.13 -0.04 0.22 0.02
## Visibility -0.14 0.02 0.05 -0.20 0.07 0.21
## PedCount 1.00 -0.28 -0.28 0.21 0.09 -0.01
## Res -0.28 1.00 -0.09 -0.15 -0.03 0.09
## Hours24 -0.28 -0.09 1.00 0.11 -0.04 0.06
## CrewSkill 0.21 -0.15 0.11 1.00 -0.02 -0.03
## MgrSkill 0.09 -0.03 -0.04 -0.02 1.00 0.36
## ServQual -0.01 0.09 0.06 -0.03 0.36 1.00#Visualizing Correlation Matrix
library(corrplot)## corrplot 0.84 loadedcorrplot(corr = cor(store),method="ellipse")
Measuring the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)
cor1 <- cor(store$Profit,store$MTenure)
round(cor1,2)## [1] 0.44Measuring the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)
cor2 <-cor(store$Profit,store$CTenure)
round(cor2,2)## [1] 0.26Corrgram of all variables in the Store24(A) dataset.
library(corrgram)
corrgram(store, main="Corrgram of store variables", lower.panel=panel.shade,upper.panel=panel.pie,text.panel=panel.txt)
Pearson’s Correlation test on the correlation between Profit and MTenure.
cor.test(store$Profit,store$MTenure)##
## Pearson's product-moment correlation
##
## data: store$Profit and store$MTenure
## t = 4.1731, df = 73, p-value = 8.193e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2353497 0.6055175
## sample estimates:
## cor
## 0.4388692Pearson’s Correlation test on the correlation between Profit and CTenure.
cor.test(store$Profit,store$CTenure)##
## Pearson's product-moment correlation
##
## data: store$Profit and store$CTenure
## t = 2.2786, df = 73, p-value = 0.02562
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03262507 0.45786339
## sample estimates:
## cor
## 0.2576789Linear regression(OLS) of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.
model <- Profit ~ MTenure + CTenure + Comp + Pop + PedCount + Res + Hours24 + Visibility
fit <- lm(model,data=store)
summary(fit)##
## Call:
## lm(formula = model, data = store)
##
## Residuals:
## Min 1Q Median 3Q Max
## -105789 -35946 -7069 33780 112390
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7610.041 66821.994 0.114 0.909674
## MTenure 760.993 127.086 5.988 9.72e-08 ***
## CTenure 944.978 421.687 2.241 0.028400 *
## Comp -25286.887 5491.937 -4.604 1.94e-05 ***
## Pop 3.667 1.466 2.501 0.014890 *
## PedCount 34087.359 9073.196 3.757 0.000366 ***
## Res 91584.675 39231.283 2.334 0.022623 *
## Hours24 63233.307 19641.114 3.219 0.001994 **
## Visibility 12625.447 9087.620 1.389 0.169411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 56970 on 66 degrees of freedom
## Multiple R-squared: 0.6379, Adjusted R-squared: 0.594
## F-statistic: 14.53 on 8 and 66 DF, p-value: 5.382e-12Explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05).
MTenure (p=9.72e-08) CTenure (p=0.02)
Comp (p=1.94e-05) Pop (p=0.014)
PedCount (p=0.0003) Res (p= 0.023)
Hours24 (p=0.002)
Explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05).
Visibility (p=0.17)
Expected change in the Profit at a store, if the Manager’s tenure i.e. number of months of experience with Store24, increases by one month?
There will be an increase of 761(approx.) in profit value.
Expected change in the Profit at a store, if the Crew’s tenure i.e. number of months of experience with Store24, increases by one month?
There will be an increase of 945 (approx.) in profit value
Summary
Looking at the correlation stats, we can deduce that profit has more correlation with the management tenure (0.44), Population (0.43), Pedestrian Count (0.45). So, definitely we can say that the financial Performance of the Store24 (A) is affected by the managerial tenure. The Performance also depends on the ‘site location factors’ like Population and Pedestrian count.
With the probability (p<0.05) from Pearson’s correlation test, the crew tenure surely indulges in profit margin.
One can also look at the linear regression with profit (response variable), with others as predictor variables(MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility).
So increasing in the management tenure and crew tenure can surely boost the financial performance.
Also the company has to consider the population criteria, as it sees more profit margin. So, they have to consider this site location factors, in case of a relocation. Management skill (0.32) and Service Quality (0.36) also puts a load on profits to certain extent. So, the company should surely take measures of implement new career development programs, thereby improving the managerial skills.