Crucial issues faced by the managements are

  1. Whether or not employee retention contributes in increasing the profits.

  2. Whether or not a management should retain employees.

Questions that matter the most-

Q- 1 What should be the strategies for employee retention?

Q-2 when to give pay hike, bonus, or spend on training and skill enhancement programs?

Q-3 how to use data properly for analysing the impact of tenure of employees relative to site-locations factor on the store level financial performance?

Q-4 what sort of benefits do the increase in manager and crew tenure will give for the company with experienced employees and the company with relatively inexperienced employees? How to contrast between these two?

the dataset attached here will help us analyze the contrast between the effect of site-specific factors and the effect of employee retention on the profits of the store.

Q-read the data set in the dataframe called store

store<-read.csv(paste("Store24.csv",sep = ""))
View(store)

Q-1 Use R to measure the mean and standard deviation of Profit. Q-2 Use R to measure the mean and standard deviation of MTenure. Q-3 Use R to measure the mean and standard deviation of CTenure.

mean(store$Profit) #gives mean of the profit
## [1] 276313.6
sd(store$Profit) #gives standard deviation of the profit
## [1] 89404.08
mean(store$MTenure) #gives mean of the MTenure
## [1] 45.29644
sd(store$MTenure)# gives standard deviation of the MTenure
## [1] 57.67155
mean(store$CTenure)#gives mean of the CTenure
## [1] 13.9315
sd(store$CTenure)#gives standard deviation of the CTenure
## [1] 17.69752

mean and standard deviation of the profit= $276314 and $89404

mean and standard deviation of the MTenure= $45 and $58

mean and standard deviation of the CTenure= $14 and $18

attach(mtcars)
View(mtcars)
newdata <- mtcars[order(mpg),] # sort by mpg (ascending)

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
detach(mtcars)
attach(mtcars)
newdata1 <- mtcars[order(-mpg),] # sort by mpg (descending)
View(newdata1)

Q4 -Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the top 10 most profitable stores.

profitable<-store[order(-store$Profit),]
profitable[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

Q5-Use R to print the {StoreID, Sales, Profit, MTenure, CTenure} of the bottom 10 least profitable stores.

leastprofit<-store[order(store$Profit),]
leastprofit[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

Q6-Use R to draw a scatter plot of Profit vs. MTenure.

plot(store$MTenure,store$Profit,main = "Scatterplot of Profit vs MTenure",xlab = "MTenure"
     ,ylab = "Profit",pch=10,col=4)
abline(lm(store$Profit~store$MTenure), col=2)
lines(smooth.spline(store$Profit,store$MTenure),lty=1,lwd=2)

Q-7 Use R to draw a scatter plot of Profit vs. CTenure.

plot(store$MTenure,store$Profit,main = "Scatterplot of Profit vs CTenure",xlab = "CTenure"
     ,ylab = "Profit",pch=10,col=4)
abline(lm(store$Profit~store$CTenure), col=2)
lines(smooth.spline(store$Profit,store$CTenure),lty=1,lwd=2)

Q-8 Use R to construct a Correlation Matrix for all the variables in the dataset. (Display the numbers up to 2 Decimal places)

options(digits=2)
cor<-cor(store)
round(cor,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

this is by default pearson-product moment coorelation.

if we want the same exercise using spearman method then here is the code for that

c<-cor(store, method="spearman")
round(c,2)
##            store Sales Profit MTenure CTenure   Pop  Comp Visibility
## store       1.00 -0.23  -0.20    0.00    0.00 -0.26 -0.03      -0.02
## Sales      -0.23  1.00   0.93    0.38    0.20  0.30 -0.28       0.18
## Profit     -0.20  0.93   1.00    0.36    0.27  0.35 -0.39       0.17
## MTenure     0.00  0.38   0.36    1.00    0.14 -0.06  0.18       0.01
## CTenure     0.00  0.20   0.27    0.14    1.00 -0.19 -0.16       0.06
## Pop        -0.26  0.30   0.35   -0.06   -0.19  1.00 -0.21       0.01
## Comp       -0.03 -0.28  -0.39    0.18   -0.16 -0.21  1.00       0.08
## Visibility -0.02  0.18   0.17    0.01    0.06  0.01  0.08       1.00
## PedCount   -0.17  0.40   0.42    0.00   -0.08  0.57 -0.30      -0.13
## Res        -0.03 -0.16  -0.18    0.05   -0.12 -0.21  0.23       0.02
## Hours24     0.03  0.09   0.03   -0.11    0.03 -0.29  0.12       0.04
## CrewSkill  -0.04  0.18   0.17    0.17    0.25  0.23 -0.08      -0.22
## MgrSkill   -0.09  0.27   0.22    0.28    0.03  0.05  0.26       0.01
## ServQual   -0.33  0.40   0.39    0.24    0.09  0.09  0.07       0.20
##            PedCount   Res Hours24 CrewSkill MgrSkill ServQual
## store         -0.17 -0.03    0.03     -0.04    -0.09    -0.33
## Sales          0.40 -0.16    0.09      0.18     0.27     0.40
## Profit         0.42 -0.18    0.03      0.17     0.22     0.39
## MTenure        0.00  0.05   -0.11      0.17     0.28     0.24
## CTenure       -0.08 -0.12    0.03      0.25     0.03     0.09
## Pop            0.57 -0.21   -0.29      0.23     0.05     0.09
## Comp          -0.30  0.23    0.12     -0.08     0.26     0.07
## Visibility    -0.13  0.02    0.04     -0.22     0.01     0.20
## PedCount       1.00 -0.28   -0.31      0.16     0.06    -0.06
## Res           -0.28  1.00   -0.09     -0.19    -0.04     0.10
## Hours24       -0.31 -0.09    1.00      0.17     0.01     0.05
## CrewSkill      0.16 -0.19    0.17      1.00     0.07    -0.01
## MgrSkill       0.06 -0.04    0.01      0.07     1.00     0.35
## ServQual      -0.06  0.10    0.05     -0.01     0.35     1.00

some observations based on this:

1- there is a strong positive correlation between profits and sales.

2- there is a positive correlaton between Profits and MTenure but not so strong.

3- there is a positive correlation between Profits and CTenure but not so strong.

4- there is a negative correlation between profits and no. of competitors(Comp).

see correlation is only showing the linear association, how these variables are related to each other. one should not interpret this as any causal relationship between these variables. there may or may not be any causal relationship.

we are getting a square matrices by default – all variables are crossed with all other variables.

Q9 -Use R to measure the correlation between Profit and MTenure. (Display the numbers up to 2 Decimal places)

co<-cor(store$Profit,store$MTenure) # one method
round(co,2)
## [1] 0.44
#second method - a detailed analysis
cor.test(store[,"Profit"], store[,"MTenure"])
## 
##  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

here we are testing the null hypothesis that correlation between profit and MTenure(manager duration) is zero. on the basis of p-value(<0.05) we are rejeting this claim.

here we are finding somewhat good positive correlation and can say that true correlation between these variables is significantly not equal to zero.

correlation coefficient= 0.44

Q10- Use R to measure the correlation between Profit and CTenure. (Display the numbers up to 2 Decimal places)

corr<-cor(store$Profit,store$CTenure)#ist method
round(corr,2)
## [1] 0.26
#2nd method
cor.test(store[,"Profit"],store[,"CTenure"])
## 
##  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

here we are testing the null hypothesis that correlation between profit and CTenure(crew duration) is zero. on the basis of p-value(<0.05) we are rejeting this claim.

here we are finding somewhat good positive correlation and can say that true correlation between these variables is significantly not equal to zero.

correlation coefficient=0.26

Q11- Use R to construct the following Corrgram based on all variables in the dataset.

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

Q12- Critically think about how the Profit is correlated with the other variables (e.g. MTenure, CTenure, Sales, Pop, Comp etc).

using corrplot command

library(corrplot)    # install if needed
## corrplot 0.84 loaded
corrplot(corr=cor(store[ , c(2, 3, 1:12)], use="complete.obs"), 
         method ="ellipse")

using gplot for better representation

library(gplots)
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
par(mfrow=c(1, 1))
corrplot.mixed(corr=cor(store[ , c(2, 3, 1:12)], use="complete.obs"), 
               upper="ellipse", tl.pos="lt", color = colorpanel(50, "red", "gray60", "blue4"))
## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt
## = tl.srt, : "color" is not a graphical parameter
## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col
## = tl.col, : "color" is not a graphical parameter
## Warning in title(title, ...): "color" is not a graphical parameter

## Warning in title(title, ...): "color" is not a graphical parameter

here warning is showing that color is not a graphical paramete. actually we use col keyword ,but it is not accepting here.

by looking on this chart we can say blue shades are showing positive correlation and red shades are negatively correlated.

profit is having a high positive correlation with sales. more sales is associated with more profits.

similarly profit is strongly correlated positively with MTenure and relatively strongly correlated positively with Ctenure

the higher the tenure of the manager and crew tenure the higher the profit is.

again profits are showing positive correlation with visibility factor. the more rating means more visible more consumers will come and buy so definitely profits will go up.

comp(the no. of competitors) is negatively related with profits. it makes sense that the more the no. of competitors available in the market the more they are likely to steal your customers away which will affect your profits negatively.

profit is again showing positive correlation with pop(population within 1/2 miles radius). the more no. of people the more they are likely to purchase the more the profit will grow.

profit is again positively correlated with pedcount(pedestrian counting). the more pedestrain purchase the more profit will go up.

similarly we can interpret rest of the variables. the more sharper the shape of ellipse(the more flat it is) is the more the intensity of correlaton depending on the color.

Q13- Qualitatively discuss the managerially relevant correlations.

here sales is highly impacting the profits in positive manner. so as a manager always think of increasing the sales of your product. a manger should critically think of cutting the competition in the market. how to be innovative while product promotion so that consumers whenever think of buying a product sholud always remeber your product first.also rating is an important factor so mangers should establish their stores in such a way that it is visible from even a long distance as it will in turn increase the profits. if we see the data closely and the top ten profitable stores we found that manager and crew tenure was at least four times higher than those in the ten least profitable stores. working with the same manger and same crew member developes a bonding and understanding between a manger and crew members. they both understand the working ideas of each other and are able to communicate with each other efficiently. so any organization should think about a manager’s tenure and in turn a manager shuold focus on employee retention by giving some incentives time to time for crew member’s good performance, encouragin them , sometimes a word of praise also do the trick , make the crew members realise that they are an inseparable and important part of organization. employees over the period of time develop new skills so keeping them intact with the organization will cut the cost of hiring and training the new employees. there will be huge saving in terms of hiring cost and training cost. employee retention will keep employees focused which will definitely affect profits positively. a manager should decide which areas he/she should target for product sale . a better segmentation of market will help boost the profits. the densely populated area will definitely increase the sales which in tun increase the profits. sometimes the location of your store should be such that it is easily accessble to passerby other thab the residents. critical examination of such areas will benefit the orrganization in the long run. like we see small departmental stores on the highway. though highways are not densely populated but highways are busy place and people passing through the highway will have no choice other than buying from your store located there. even high price will not deter them from purchasing. it will be like added advantage to charge high price for the same product.

Q14 -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
# by deafault this formula gives pearson value
cor.test(store[,"Profit"],store[,"MTenure"])
## 
##  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

correlation between profit and MTenure is positivel,it is 0.44 which is significantly different from 0.

p-value- 8e-05= 0.00008

Q15 -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
# by deafault this formula gives pearson value
cor.test(store[,"Profit"],store[,"CTenure"])
## 
##  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

correlation here between profit and MTenure is positivel,it is 0.26,though not so strong yet significantly different from 0.

p-value- 0.03

Q16 -Run a regression of Profit on {MTenure, CTenure Comp, Pop, PedCount, Res, Hours24, Visibility}.

# regression model 
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)   7610.04   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
fit$coefficients
## (Intercept)     MTenure     CTenure        Comp         Pop    PedCount 
##      7610.0       761.0       945.0    -25286.9         3.7     34087.4 
##         Res     Hours24  Visibility 
##     91584.7     63233.3     12625.4

so model is :-

profit= 7610.0+761.0MTenure+945.0CTenure-25286.9comp+3.7Pop+34087.4Pedcount+91584.7Res+63233.3Hours24+12625.4Visibility +error

Q17- List the explanatory variable(s) whose beta-coefficients are statistically significant (p < 0.05) are-

stars indicate that the beta coefficient of the variables are significant

beta - coefficients which are statistically significant are-

B1(beta1)- MTenure(manager tenure) coefficient= 761, p-value =0.000000097<0.05.

expected increase in profit is 761 unit due to 1 unit increase in MTenure(manager tenure)

B2(beta2)- CTenure(crew tenure) coefficient=945.0 p-value= 0.02840<0.05.

expected increase in profit is 945.0 unit due to 1 unit increase in Comp(competition)

B3(beta3)- Comp(competitors) coefficient=-25286.9, p-value=0.000019 <0.05

expected decrease in profit is 25286.9unit due to 1 unit increase in competition

B4(beta4)- Pop (population) coefficient=3.67, p-value=0.01489<0.05

B5(beta5)- pedcount(rating based on pedestrian traffic) coefficient= 34087.36, p-value=0.00037<0.05.

expected increase in profit is 34087.36 unit due to 1 unit increase in PedCount(pedestrian 5-star rating)

B6(beta6)- coefficient of Res(resendial vs. industrial area)=91584.68, p-value=0.02262<0.05

B7(beta7)- coefficient of Hours24 =63233.3, p-value=0.00199<0.05

Q18 -List the explanatory variable(s) whose beta-coefficients are not statistically significant (p > 0.05).

beta coefficients which are not significant(p-value>0.05) are-

b0- coefficient of intercept 7610.0

b8-coefficient of Visibility 12625.4

Q19 -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?

the expected change in profit at a store is profit will go up by 761 units due to one month increase in the tenure of manager with store24

Q20 -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?

the expected increase in profit is 945 units due to 1 month increase in tenure of crew members.

Q21- “Executive Summary”

Here we have analyzed the store performance based on the factors separated in two groups one is site-location factors and employee-retention factor by the help of data available with us. Though we are concerned relatively more with the importance of employee-retention factors. the author here wanted to discuss about the strategies of employees retention. How these strategies will have impact on the financial performance of the store.Based on the regression analysis analysis of the data available here we can say that There is some sort of positive relationship between tenure of employees and the financial performance of the store and one can also expect this performance to vary with the tenure of employee. here we have regressed profit over ather variables using the model:

profit= 7610.0+761.0MTenure+945.0CTenure-25286.9comp+3.7Pop+34087.4Pedcount+91584.7Res+63233.3Hours24+12625.4Visibility+error.

the coefficients -we got after performing the regression analysis. if we see the results we can say some of the independent variables are significantly affecting the financial performance of the store. employee retention factors are contributing significantly in increasing the profits. if we increase the tenure of manager by one month we can see here that the profit is expected to be up by 761. and at the same time the increase in one month in the tenure of crew members is set to increase the profit of store by 945 which recommends the management to retain the employees i.e employee retention is very much needed. this in turn led to the another important decision regarding how to retain employees. A mangement shuold think of timely bonus, wage increase, recognition of the work of employees by giving out certificates like best employee or star performer of the month. This will keep them motivated and in turn they will push them little bit further to achieve excellence in their work. After all who doesn’t want to be noticed for all the good reasons.other ways of doing this is to institute new training programs,or develop a carrer development program which will enhance thier skill for work. Make the employees feel that they are special contributors towards the growth of the company and their work and opinions really matter the most. By retaining employees ,a company wouldn’t be spending unnecessary time and cost of hiring new employees everytime. giving training to existing employees will relatively much convenent, cheaper and time saving. This is applicable to both kind of companies which have inexperienced and relatively experienced workers. Experienced workers need to upadte their skill set and be accomodative to today’s fast changing technology.long tenures of employees make them feel connected ,secure and stable which highly motivates an employee to focus more on work. While among other group of factors ,the site-location factor, comp(no of competitors) factor is affecting the profits negatively. Any company should think also about the strategies which will help in reducing the competition. Make your product little bit swanky in appearance.Even a slight differentiation of your product can make a huge difference. population of area is also significantly affecting the profits positively. launch the product with large customer base as more demand leads to more profit. If the store is opened for 24 hours means availability of products round the clock will definitely create a positive environment about the store in the minds of consumers. Many people nowadays worklate night , so there is demand of readymade product especially food and beverages all the time . So opening of outlets in the office area or some place which remains busy at night will boost the profits . For this company should rotate the employees , change shifts and don’t overburden any employee. Easy accessibilty of store will definitely make the store popular among consumers and demand will go up which will eventually be reflected in the profits The visibilty factor according to the model is not affecting the profits because it’s coeficient is insignificant.

So strategies should be focused on both employee-retention and site-location factors. Leaving any of these two will cost heavily. The proper policy making giving importance to both these factor, is something that every company shuold focus on.