Managing Employee Retention

Analysis of the Case Study, Store24 (A): Managing Employee Retention

This document gives Analysis of the Case Store24 (A): Managing Employee Retention.

setwd("~/Desktop/Data Analytics Internship/Employee Retention")
store  <- read.csv(paste("Store24.csv" , sep = ""))
View(store)

Describing the Dataset

library(psych)
describe(store)

##            vars  n       mean        sd     median    trimmed       mad
## store         1 75      38.00     21.79      38.00      38.00     28.17
## Sales         2 75 1205413.12 304531.31 1127332.00 1182031.25 288422.04
## Profit        3 75  276313.61  89404.08  265014.00  270260.34  90532.00
## MTenure       4 75      45.30     57.67      24.12      33.58     29.67
## CTenure       5 75      13.93     17.70       7.21      10.60      6.14
## Pop           6 75    9825.59   5911.67    8896.00    9366.07   7266.22
## Comp          7 75       3.79      1.31       3.63       3.66      0.82
## Visibility    8 75       3.08      0.75       3.00       3.07      0.00
## PedCount      9 75       2.96      0.99       3.00       2.97      1.48
## Res          10 75       0.96      0.20       1.00       1.00      0.00
## Hours24      11 75       0.84      0.37       1.00       0.92      0.00
## CrewSkill    12 75       3.46      0.41       3.50       3.47      0.34
## MgrSkill     13 75       3.64      0.41       3.59       3.62      0.45
## ServQual     14 75      87.15     12.61      89.47      88.62     15.61
##                  min        max      range  skew kurtosis       se
## store           1.00      75.00      74.00  0.00    -1.25     2.52
## Sales      699306.00 2113089.00 1413783.00  0.71    -0.09 35164.25
## Profit     122180.00  518998.00  396818.00  0.62    -0.21 10323.49
## MTenure         0.00     277.99     277.99  2.01     3.90     6.66
## CTenure         0.89     114.15     113.26  3.52    15.00     2.04
## Pop          1046.00   26519.00   25473.00  0.62    -0.23   682.62
## Comp            1.65      11.13       9.48  2.48    11.31     0.15
## Visibility      2.00       5.00       3.00  0.25    -0.38     0.09
## PedCount        1.00       5.00       4.00  0.00    -0.52     0.11
## Res             0.00       1.00       1.00 -4.60    19.43     0.02
## Hours24         0.00       1.00       1.00 -1.82     1.32     0.04
## CrewSkill       2.06       4.64       2.58 -0.43     1.64     0.05
## MgrSkill        2.96       4.62       1.67  0.27    -0.53     0.05
## ServQual       57.90     100.00      42.10 -0.66    -0.72     1.46

Task 4d

Measure the mean and standard deviation of Profit

mean(store$Profit)

## [1] 276313.6

sd(store$Profit)

## [1] 89404.08

Measure the mean and standard deviation of MTenure

mean(store$MTenure)

## [1] 45.29644

sd(store$MTenure)

## [1] 57.67155

Measure the mean and standard deviation of CTenure

mean(store$CTenure)

## [1] 13.9315

sd(store$CTenure)

## [1] 17.69752

TASK 4e

Sorting and Subsetting data in R

attach(mtcars)
View(mtcars)
newdata <- mtcars[order(mpg),] # sort by mpg (ascending)
View(newdata)
newdata[1:5,] # see the first 5 rows

##                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
## Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
## Duster 360          14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
## Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4

newdata <- mtcars[order(-mpg),] # sort by mpg (descending)
View(newdata)
detach(mtcars)

TASK 4f Replicate Exhibit 1 shown in the case, using R

Print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.

mp<- store[order(store$Profit), ]
mp[1:10, 1:5]

##    store   Sales Profit     MTenure   CTenure
## 57    57  699306 122180  24.3485700  2.956879
## 66    66  879581 146058 115.2039000  3.876797
## 41    41  744211 147327  14.9180200 11.926080
## 55    55  925744 147672   6.6703910 18.365500
## 32    32  828918 149033  36.0792600  6.636550
## 13    13  857843 152513   0.6571813  1.577002
## 54    54  811190 159792   6.6703910  3.876797
## 52    52 1073008 169201  24.1185600  3.416838
## 61    61  716589 177046  21.8184200 13.305950
## 37    37 1202917 187765  23.1985000  1.347023

Print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.

lp<- store[order(-store$Profit), ]
lp[1:10, 1:5]

##    store   Sales Profit   MTenure    CTenure
## 74    74 1782957 518998 171.09720  29.519510
## 7      7 1809256 476355  62.53080   7.326488
## 9      9 2113089 474725 108.99350   6.061602
## 6      6 1703140 469050 149.93590  11.351130
## 44    44 1807740 439781 182.23640 114.151900
## 2      2 1619874 424007  86.22219   6.636550
## 45    45 1602362 410149  47.64565   9.166325
## 18    18 1704826 394039 239.96980  33.774130
## 11    11 1583446 389886  44.81977   2.036961
## 47    47 1665657 387853  12.84790   6.636550

TASK 4g - Scatter Plots

Draw a scatter plot of Profit vs. MTenure.

library(car)

## 
## Attaching package: 'car'

## The following object is masked from 'package:psych':
## 
##     logit

scatterplot(Profit~MTenure, data=store,
            xlab="MTenure", ylab="Profit", 
            main="Scatterplot of MTenure v/s Profit")

TASK 4h - Scatter Plots (contd.)

Draw a scatter plot of Profit vs. CTenure.

scatterplot(Profit~CTenure, data=store,
            xlab="CTenure", ylab="Profit", 
            main="Scatterplot of CTenure v/s Profit")

TASK 4i - Correlation Matrix

Construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)

options(digits=2)
cor(store)

##             store  Sales Profit MTenure CTenure     Pop   Comp Visibility
## store       1.000 -0.227 -0.200  -0.057  0.0199 -0.2894  0.032     -0.026
## Sales      -0.227  1.000  0.924   0.455  0.2543  0.4035 -0.235      0.131
## Profit     -0.200  0.924  1.000   0.439  0.2577  0.4306 -0.335      0.136
## MTenure    -0.057  0.455  0.439   1.000  0.2434 -0.0609  0.181      0.157
## CTenure     0.020  0.254  0.258   0.243  1.0000 -0.0015 -0.070      0.067
## Pop        -0.289  0.403  0.431  -0.061 -0.0015  1.0000 -0.268     -0.050
## Comp        0.032 -0.235 -0.335   0.181 -0.0703 -0.2683  1.000      0.028
## Visibility -0.026  0.131  0.136   0.157  0.0665 -0.0500  0.028      1.000
## PedCount   -0.221  0.424  0.450   0.062 -0.0841  0.6076 -0.146     -0.141
## Res        -0.031 -0.167 -0.159  -0.062 -0.3403 -0.2369  0.219      0.022
## Hours24     0.027  0.063 -0.026  -0.165  0.0729 -0.2218  0.130      0.047
## CrewSkill   0.049  0.164  0.160   0.102  0.2572  0.2828 -0.042     -0.197
## MgrSkill   -0.072  0.312  0.323   0.230  0.1240  0.0836  0.224      0.073
## ServQual   -0.322  0.386  0.362   0.182  0.0812  0.1239  0.018      0.210
##            PedCount    Res Hours24 CrewSkill MgrSkill ServQual
## store       -0.2212 -0.031   0.027     0.049   -0.072  -0.3225
## Sales        0.4239 -0.167   0.063     0.164    0.312   0.3864
## Profit       0.4502 -0.159  -0.026     0.160    0.323   0.3625
## MTenure      0.0620 -0.062  -0.165     0.102    0.230   0.1817
## CTenure     -0.0841 -0.340   0.073     0.257    0.124   0.0812
## Pop          0.6076 -0.237  -0.222     0.283    0.084   0.1239
## Comp        -0.1463  0.219   0.130    -0.042    0.224   0.0181
## Visibility  -0.1411  0.022   0.047    -0.197    0.073   0.2099
## PedCount     1.0000 -0.284  -0.276     0.214    0.087  -0.0054
## Res         -0.2844  1.000  -0.089    -0.153   -0.032   0.0908
## Hours24     -0.2760 -0.089   1.000     0.105   -0.039   0.0583
## CrewSkill    0.2137 -0.153   0.105     1.000   -0.021  -0.0335
## MgrSkill     0.0875 -0.032  -0.039    -0.021    1.000   0.3567
## ServQual    -0.0054  0.091   0.058    -0.034    0.357   1.0000

TASK 4j - Correlations

Measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)

cor(store$Profit, store$MTenure)

## [1] 0.44

Measure the correlation between Profit and CTenure.(Display the numbers up to 2 Decimal places)

cor(store$Profit, store$CTenure)

## [1] 0.26

TASK 4k

Construct the Corrgram based on all variables in the dataset.

library(corrgram)

## Warning: replacing previous import by 'magrittr::%>%' when loading
## 'dendextend'

corrgram(store,order=TRUE, lower.panel = panel.shade,
         upper.panel = panel.pie, text.panel = panel.txt, 
         main="Corrgram of store24 dataset")

Critically think about how the Profit is correlated with the other variables (e.g. MTenure, CTenure, Sales, Pop, Comp etc). Qualitatively discuss the managerially relevant correlations.

Profit is positive for MTenure,CTenure,Sales,Pop,Comp,Visibility,ServQual,CrewSkill and Profit is negatively affected by Comp,Hours24,store and Res. We notice that the manager’s tenure(MTenure) is negatively affected by longer working hours (Hours24) and are not interested in continuing their jobs. Crews don’t in continuing their tenure in 24 hour stores as this gives a chance of having flexible hours for their shift. Profit is positively affected by Crew’s Skill and Manager’s Skill as more the customers are satisfied due to efficient customer service, more returning customers the store has.

TASK 4l - Pearson’s Correlation Tests

Run a Pearson’s Correlation test on the correlation between Profit and MTenure. What is the p-value?

cor.test(store$Profit, store$MTenure, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  store$Profit and store$MTenure
## t = 4, df = 70, p-value = 8e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.24 0.61
## sample estimates:
##  cor 
## 0.44

P-value : 8e-05

Run a Pearson’s Correlation test on the correlation between Profit and CTenure. What is the p-value?

cor.test(store$Profit, store$CTenure, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  store$Profit and store$CTenure
## t = 2, df = 70, p-value = 0.03
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.033 0.458
## sample estimates:
##  cor 
## 0.26

P-value: 0.03

TASK 4m - Regression Analysis

Regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.

rp<- lm(Profit~MTenure+CTenure+Comp+Pop+PedCount+Res+Hours24+Visibility, data=store)
summary(rp)

## 
## Call:
## lm(formula = Profit ~ MTenure + CTenure + Comp + Pop + PedCount + 
##     Res + Hours24 + Visibility, data = store)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -105789  -35946   -7069   33780  112390 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   7610.04   66821.99    0.11  0.90967    
## MTenure        760.99     127.09    5.99  9.7e-08 ***
## CTenure        944.98     421.69    2.24  0.02840 *  
## Comp        -25286.89    5491.94   -4.60  1.9e-05 ***
## Pop              3.67       1.47    2.50  0.01489 *  
## PedCount     34087.36    9073.20    3.76  0.00037 ***
## Res          91584.68   39231.28    2.33  0.02262 *  
## Hours24      63233.31   19641.11    3.22  0.00199 ** 
## Visibility   12625.45    9087.62    1.39  0.16941    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57000 on 66 degrees of freedom
## Multiple R-squared:  0.638,  Adjusted R-squared:  0.594 
## F-statistic: 14.5 on 8 and 66 DF,  p-value: 5.38e-12

TASK 4n

List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05).

MTenure, CTenure, Comp, Pop, PedCount, Res, Hours24

List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05).

Visibility

TASK 4o

What is 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?

From the above analysis, we can decipher that if the Manager’s tenure is increased by a month, the profit changes by $760.99.

What is 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?

From the above analysis, we can decipher that if the Crew’s tenure is increased by a month, the profit changes by $944.98.

Task 4p Executive 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.