part_1 and 2_carrefour_analysis
Esther_Wairimu_Kamau
1/21/2021
1.Problem statement
Esther and co.analytics have been contracted by Carrefour Kenya and are currently undertaking a project that will inform the marketing department on the most relevant marketing strategies that will result in the highest no. of sales (total price including tax). We will divide the project into four parts where we’ll explore a recent marketing dataset by performing various analysis and algorithms and later providing recommendations based on our insights.
2.Metrics of success
Performing indepth analysis and provide insights.Especially on the fast moving the most revenue growers products(product lines in our case).Carrefour deepest concern is simply strategies for shorter the conversion cycle.This invaluable supply chain metric will CARREFOUR Kenya can take the right measures to ensure that they run business with less money tied up in operations.
3.Business Understanding
On 17 May 2016, the Majid Al Futtaim Group - the official franchise partner opened its first Carrefour hypermarket in Kenya, in Nairobi county.The country’s .in 2019 it operated two hypermarket locations, one at the Hub and the other at Two Rivers.The European based supermarket Carrefour, which is the first hypermarket in the world has finally opened two branches in Kenya and is set out to open another one at Thika Road Mall.
4.Data Understanding
cost of goods sold ( COGS) efers to the direct costs of producing the goods sold by a company.(unit price*quantity)
Gross margin is a company’s net sales revenue minus its cost of goods sold ( COGS). In other words, it is the sales revenue a company retains .is more correctly defined as a percentage, and is used as a profitability metric(gross income/total * 100)
Gross incomeThe gross income for a company reveals how much money it has made on its products or services after subtracting the direct costs(COGS) to make the product or provide the service.
TotalIn our dataset we get this by adding COGS and gross income
3.Loading and Exploring our dataset
TaxIn our dataset this is equal to gross income.(Its identical)
unit pricethis is the price of one commodity purchased.
quantitythis are the number of units purchased at a particular day and time.
Gender this is whether the purchaser is male or female
productlinethe department the commodity being purchased belongs to
Branchthe branch the commodity being purchased is in our case there is A,B and C
customerif the person purchasing is a member or normal customer.Member meaning they have registered with the supermarkets loyalty points scheme.
invoiceidis the unique identifier for each purchase and also our primary key
paymentthis is the means that the customer uses to pay for their purchase example of one method is cash/ewallet/creditcard
library("readr")
cr <- read.csv("http://bit.ly/CarreFourDataset")
head(cr,n=3)## Invoice.ID Branch Customer.type Gender Product.line Unit.price
## 1 750-67-8428 A Member Female Health and beauty 74.69
## 2 226-31-3081 C Normal Female Electronic accessories 15.28
## 3 631-41-3108 A Normal Male Home and lifestyle 46.33
## Quantity Tax Date Time Payment cogs gross.margin.percentage
## 1 7 26.1415 1/5/2019 13:08 Ewallet 522.83 4.761905
## 2 5 3.8200 3/8/2019 10:29 Cash 76.40 4.761905
## 3 7 16.2155 3/3/2019 13:23 Credit card 324.31 4.761905
## gross.income Rating Total
## 1 26.1415 9.1 548.9715
## 2 3.8200 9.6 80.2200
## 3 16.2155 7.4 340.5255#previewing the bottom of the dataset
tail(cr,n=3)## Invoice.ID Branch Customer.type Gender Product.line Unit.price
## 998 727-02-1313 A Member Male Food and beverages 31.84
## 999 347-56-2442 A Normal Male Home and lifestyle 65.82
## 1000 849-09-3807 A Member Female Fashion accessories 88.34
## Quantity Tax Date Time Payment cogs gross.margin.percentage
## 998 1 1.592 2/9/2019 13:22 Cash 31.84 4.761905
## 999 1 3.291 2/22/2019 15:33 Cash 65.82 4.761905
## 1000 7 30.919 2/18/2019 13:28 Cash 618.38 4.761905
## gross.income Rating Total
## 998 1.592 7.7 33.432
## 999 3.291 4.1 69.111
## 1000 30.919 6.6 649.299#we check the column names we have
names(cr)## [1] "Invoice.ID" "Branch"
## [3] "Customer.type" "Gender"
## [5] "Product.line" "Unit.price"
## [7] "Quantity" "Tax"
## [9] "Date" "Time"
## [11] "Payment" "cogs"
## [13] "gross.margin.percentage" "gross.income"
## [15] "Rating" "Total"we observe that we have total of 16 columns heads we can confirm with dim function below
#we can also check the no of rows and columns
dim(cr)## [1] 1000 16#we can also go ahead and check for the datatypes
str(cr)## 'data.frame': 1000 obs. of 16 variables:
## $ Invoice.ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ Customer.type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product.line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit.price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross.margin.percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross.income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...we observe that,Customer type,payment,branch and gender are categorical we can hot encode them into numerical if needed
we also have one string column that is the product line
our primary key id invoice ID Since it has unique values to identify the rows
Rating column helps us profile our customers
we also have date and time columns
the tax and gross income have the same value so we can drop one
gross margin percentage is the same has one constant number we can also drop the column
#we can go ahead and check for the number missing values per column
colSums(is.na(cr))## Invoice.ID Branch Customer.type
## 0 0 0
## Gender Product.line Unit.price
## 0 0 0
## Quantity Tax Date
## 0 0 0
## Time Payment cogs
## 0 0 0
## gross.margin.percentage gross.income Rating
## 0 0 0
## Total
## 0we observe that we have no missing values
#we can go ahead and check for duplicated values
duplicated_rows <- cr[duplicated(cr),]
dim(duplicated_rows)## [1] 0 16we have o rows in the 16 columns that have been duplicated
#we can go ahead and check for the outliers
#first we can go ahead and choose the numerical columns only
nums <- subset(cr, select = c(Unit.price, Quantity, Tax,cogs, gross.margin.percentage,gross.income, Rating ,Total))
colnames(nums)## [1] "Unit.price" "Quantity"
## [3] "Tax" "cogs"
## [5] "gross.margin.percentage" "gross.income"
## [7] "Rating" "Total"boxplot(nums)
We observe that column cogs and total have a few outliers
5.Tidying the Dataset
#we can first start by changing the pesky column names
#we will first change them to lowercase then we will rename them
# First we Change the type of the loaded dataset to a dataframe
cr = as.data.frame(cr)
# Change column names, by making them uniform
colnames(cr) = tolower(colnames(cr))
#to confirm the change
names (cr)## [1] "invoice.id" "branch"
## [3] "customer.type" "gender"
## [5] "product.line" "unit.price"
## [7] "quantity" "tax"
## [9] "date" "time"
## [11] "payment" "cogs"
## [13] "gross.margin.percentage" "gross.income"
## [15] "rating" "total"library(reshape)cr <- rename(cr, c(invoice.id="invoice"))
cr <- rename(cr, c(gross.margin.percentage="grossmargin"))
cr <- rename(cr, c(product.line="product"))
cr <- rename(cr, c(customer.type="customer"))
cr <- rename(cr, c(gross.income="grossincome"))
cr <- rename(cr, c(unit.price="unitprice"))
names(cr)## [1] "invoice" "branch" "customer" "gender" "product"
## [6] "unitprice" "quantity" "tax" "date" "time"
## [11] "payment" "cogs" "grossmargin" "grossincome" "rating"
## [16] "total"#since we have columns that are identical tax and gross income we drop one
#we also drop the constant column grossmargin
#we dont need invoiceid in our modelling or analysis
cr[ ,c('tax', 'grossmargin','invoice')] <- list(NULL)#to confirm they have been deleted
names(cr)## [1] "branch" "customer" "gender" "product" "unitprice"
## [6] "quantity" "date" "time" "payment" "cogs"
## [11] "grossincome" "rating" "total"#we can also separate the date column into 'year','month','day'
library(tidyr)##
## Attaching package: 'tidyr'## The following objects are masked from 'package:reshape':
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## expand, smithslibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:reshape':
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## rename## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unioncrd <- separate(cr, date, c("month", "day", "year"))
head(crd,n=2) ## branch customer gender product unitprice quantity month day
## 1 A Member Female Health and beauty 74.69 7 1 5
## 2 C Normal Female Electronic accessories 15.28 5 3 8
## year time payment cogs grossincome rating total
## 1 2019 13:08 Ewallet 522.83 26.1415 9.1 548.9715
## 2 2019 10:29 Cash 76.40 3.8200 9.6 80.2200After splitting the data we observe that our data is for the year 2019
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v stringr 1.4.0
## v tibble 3.0.4 v forcats 0.5.0
## v purrr 0.3.4## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::expand() masks reshape::expand()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x dplyr::rename() masks reshape::rename()library(magrittr) ##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
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## set_names## The following object is masked from 'package:tidyr':
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## extract#Factors are used to represent categorical data. Factors can be ordered or unordered and are an important class for statistical analysis and for plotting.
#For the categorical data we change to levels
cat2= c('customer', 'payment','branch', 'gender','product','year','month','day')
# Changing columns to factors
crd[,cat2] %<>% lapply(function(x) as.factor(as.character(x)))
str(crd)## 'data.frame': 1000 obs. of 15 variables:
## $ branch : Factor w/ 3 levels "A","B","C": 1 3 1 1 1 3 1 3 1 2 ...
## $ customer : Factor w/ 2 levels "Member","Normal": 1 2 2 1 2 2 1 2 1 1 ...
## $ gender : Factor w/ 2 levels "Female","Male": 1 1 2 2 2 2 1 1 1 1 ...
## $ product : Factor w/ 6 levels "Electronic accessories",..: 4 1 5 4 6 1 1 5 4 3 ...
## $ unitprice : num 74.7 15.3 46.3 58.2 86.3 ...
## $ quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ month : Factor w/ 3 levels "1","2","3": 1 3 3 1 2 3 2 2 1 2 ...
## $ day : Factor w/ 31 levels "1","10","11",..: 27 30 23 20 30 18 18 17 2 13 ...
## $ year : Factor w/ 1 level "2019": 1 1 1 1 1 1 1 1 1 1 ...
## $ time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ payment : Factor w/ 3 levels "Cash","Credit card",..: 3 1 2 3 3 3 3 3 2 2 ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ grossincome: num 26.14 3.82 16.22 23.29 30.21 ...
## $ rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...
## $ total : num 549 80.2 340.5 489 634.4 ...we observe that:
there two types of customers member and normal
also there 6 types of product line with one being electronic accessories
also there is male and female gender only
also there 3 types of payment with one of being credit card
there is also three branches A,B and C
We have one year 2019
we also have 3 months and 31 days
6.Preparing our dataset for Analysis
#we start by separating our datatypes columns we divide the into numerical and into categorical
#this helps alot when it comes to plotting and statistical analysis
# we had already defined both but need subsets with the cleaned dataset
numcols <- subset(crd, select = c(unitprice, quantity,cogs,grossincome, rating ,total))
head(numcols,n=3)## unitprice quantity cogs grossincome rating total
## 1 74.69 7 522.83 26.1415 9.1 548.9715
## 2 15.28 5 76.40 3.8200 9.6 80.2200
## 3 46.33 7 324.31 16.2155 7.4 340.5255catcols <- subset(crd, select = c(branch, customer,gender,product, month,day, year,payment))
head(catcols,n=3)## branch customer gender product month day year payment
## 1 A Member Female Health and beauty 1 5 2019 Ewallet
## 2 C Normal Female Electronic accessories 3 8 2019 Cash
## 3 A Normal Male Home and lifestyle 3 3 2019 Credit card#we can go ahead and change the above dataframes into tibbles for easier
numt<-as_tibble(numcols)
head(numt,n=3)## # A tibble: 3 x 6
## unitprice quantity cogs grossincome rating total
## <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 74.7 7 523. 26.1 9.1 549.
## 2 15.3 5 76.4 3.82 9.6 80.2
## 3 46.3 7 324. 16.2 7.4 341.cat3<-as_tibble(catcols)
head(cat3,n=3)## # A tibble: 3 x 8
## branch customer gender product month day year payment
## <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
## 1 A Member Female Health and beauty 1 5 2019 Ewallet
## 2 C Normal Female Electronic accessories 3 8 2019 Cash
## 3 A Normal Male Home and lifestyle 3 3 2019 Credit card7.Exploratory Data Analysis
Univariate Analysis
library(ggplot2)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
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## %+%, alpha#Descriptive analysis into measures of central Tendency
#this is usually carried out for the numerical data in the datasets
#we get the statistics for the numerics and draw conclusions from them
describe(numt)## vars n mean sd median trimmed mad min max range
## unitprice 1 1000 55.67 26.49 55.23 55.62 33.37 10.08 99.96 89.88
## quantity 2 1000 5.51 2.92 5.00 5.51 2.97 1.00 10.00 9.00
## cogs 3 1000 307.59 234.18 241.76 279.91 222.65 10.17 993.00 982.83
## grossincome 4 1000 15.38 11.71 12.09 14.00 11.13 0.51 49.65 49.14
## rating 5 1000 6.97 1.72 7.00 6.97 2.22 4.00 10.00 6.00
## total 6 1000 322.97 245.89 253.85 293.91 233.78 10.68 1042.65 1031.97
## skew kurtosis se
## unitprice 0.01 -1.22 0.84
## quantity 0.01 -1.22 0.09
## cogs 0.89 -0.09 7.41
## grossincome 0.89 -0.09 0.37
## rating 0.01 -1.16 0.05
## total 0.89 -0.09 7.78we observe that we 13 numerical columns:
the mean of cogs and total are high and grossmargin and quantity low
grossincome is mostly 15units of cost with the highest being 49.6 and the lowest being 0.51.
quantities of most units is 5 with the highest being 10pcs and lowest being 1pc
most units cost around 56 units cost price with the highest being 99 and lowest 10.
We also observe that the mean of tax columns is the same with that of grosss income
par(mfrow = c(2, 2))
hist(numt$unitprice)
hist(numt$quantity)
hist(numt$cogs)
hist(numt$grossincome) 
hist(numt$rating)
hist(numt$total)
par(mfrow = c(2, 2))
f1 <- cat3$branch
f1y<- table(f1)
head.matrix(f1y)## f1
## A B C
## 340 332 328f2 <- cat3$customer
f2y<- table(f2)
head.matrix(f2y)## f2
## Member Normal
## 501 499f3 <- cat3$gender
f3y<- table(f3)
head.matrix(f3y)## f3
## Female Male
## 501 499f4 <- cat3$product
f4y<- table(f4)
head.matrix(f4y)## f4
## Electronic accessories Fashion accessories Food and beverages
## 170 178 174
## Health and beauty Home and lifestyle Sports and travel
## 152 160 166f5 <- cat3$month
f5y<- table(f5)
head.matrix(f5y)## f5
## 1 2 3
## 352 303 345f6 <- cat3$day
f6y<- table(f6)
head.matrix(f6y)## f6
## 1 10 11 12 13 14
## 28 32 27 31 28 39f7 <- cat3$payment
f7y<- table(f7)
head.matrix(f7y)## f7
## Cash Credit card Ewallet
## 344 311 345par(mfrow = c(2, 2))
barplot(f1y,col="green")
barplot(f2y,col="red")
barplot(f3y,col="blue")
barplot(f4y,col="black")
barplot(f5y,col="yellow")
barplot(f6y,col="dark green")
barplot(f7y,col="purple")
Bivariate Analysis
crd %>%
ggplot() +
aes(x = quantity, month = ..count../nrow(crd), fill = month) +
geom_bar() +
ylab("monthly sale trends")
crd %>%
ggplot() +
aes(x = day, grossincome = ..count../nrow(crd), fill = grossincome) +
geom_bar() +
ylab("daily revenue trends")
crd %>%
ggplot() +
aes(x = quantity, product = ..count../nrow(crd), fill = product) +
geom_bar() +
ylab("product sales trends")
crd %>%
ggplot() +
aes(x = quantity, payment = ..count../nrow(crd), fill = payment) +
geom_bar() +
ylab("payment sales trends")
crd %>%
ggplot() +
aes(x = quantity, gender = ..count../nrow(crd), fill = gender) +
geom_bar() +
ylab("product quantity trends")
crd %>%
ggplot() +
aes(x = quantity, customer = ..count../nrow(crd), fill = customer) +
geom_bar() +
ylab("product sale trends")
crd %>%
ggplot() +
aes(x = quantity, branxh = ..count../nrow(crd), fill = branch) +
geom_bar() +
ylab("branch sale trends")
#Distribution of income per Gender
ggplot(crd,aes(x = total, fill = gender)) +geom_density(alpha = 0.4) +labs(title = "Distribution of total sales per Gender")
#Distribution of income per Gender
ggplot(crd,aes(x = total, fill = branch)) +geom_density(alpha = 0.4) +labs(title = "Distribution of total sales per branch")
#Distribution of income per Gender
ggplot(crd,aes(x = total, fill = customer)) +geom_density(alpha = 0.4) +labs(title = "does membership affect sales")
library(ggcorrplot)
library(corrplot)## corrplot 0.84 loaded# Plotting the Correlation Heatmap
crcor<-cor(numt)
library(ggcorrplot)
ggcorrplot(crcor, hc.order = F,type =
"lower", lab = T ,
ggtheme = ggplot2::theme_gray,
colors = c("#00798c", "white", "#edae49"))
The highly correlated features in this dataset were the Cogs , grossincome and total
features with a correlation coefficient of 1(greater than 0.75).We can just drop the
above highly correlated columns during modelling.This is how feature selection is done
during multivariate analysis.
8.Feature Enginnering
#We had already ran the numericals and dropped redundant columns
#we named the numt
pca <- prcomp(numt[,c(1:6)], center = TRUE, scale = TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 1.9817 1.0002 0.9939 0.2909 2.89e-16 1.377e-16
## Proportion of Variance 0.6545 0.1667 0.1646 0.0141 0.00e+00 0.000e+00
## Cumulative Proportion 0.6545 0.8213 0.9859 1.0000 1.00e+00 1.000e+00we observe that after carrying the first principal component explained 65.45% of the total variance while the last three components explained 34.45% of the variance
#used the string function to know which variables were to be consindered
str(pca)## List of 5
## $ sdev : num [1:6] 1.98 1.00 9.94e-01 2.91e-01 2.89e-16 ...
## $ rotation: num [1:6, 1:6] -0.3281 -0.3649 -0.5029 -0.5029 0.0217 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:6] "unitprice" "quantity" "cogs" "grossincome" ...
## .. ..$ : chr [1:6] "PC1" "PC2" "PC3" "PC4" ...
## $ center : Named num [1:6] 55.67 5.51 307.59 15.38 6.97 ...
## ..- attr(*, "names")= chr [1:6] "unitprice" "quantity" "cogs" "grossincome" ...
## $ scale : Named num [1:6] 26.49 2.92 234.18 11.71 1.72 ...
## ..- attr(*, "names")= chr [1:6] "unitprice" "quantity" "cogs" "grossincome" ...
## $ x : num [1:1000, 1:6] -1.781 2.087 -0.173 -1.343 -2.497 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : chr [1:6] "PC1" "PC2" "PC3" "PC4" ...
## - attr(*, "class")= chr "prcomp"we observe that unit pricr,quantity,cogs and grossincome were thr four variables to be highly consindered during modelling.
#plotting the PCA to see the above visually
#we will use ggbiplot library
library(ggbiplot)## Loading required package: plyr## ------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following object is masked from 'package:purrr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
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## rename, round_any## Loading required package: scales##
## Attaching package: 'scales'## The following objects are masked from 'package:psych':
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## discard## The following object is masked from 'package:readr':
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## col_factor## Loading required package: gridggbiplot(pca, obs.scale = 0.5, var.scale = 0.5, groups = crd$customer,ellipse = TRUE, circle = TRUE)
we observe that whether the customer is loyal or new customer the four variables will explain 65.5 plus 16.7 making it 81.2 variance which is fairly high.
9.Wrapper methods
# Sequential forward greedy search (default)
# ---
#
library(clustvarsel)## Loading required package: mclust## Package 'mclust' version 5.4.7
## Type 'citation("mclust")' for citing this R package in publications.##
## Attaching package: 'mclust'## The following object is masked from 'package:psych':
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## sim## The following object is masked from 'package:purrr':
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## map## Package 'clustvarsel' version 2.3.4## Type 'citation("clustvarsel")' for citing this R package in publications.library(mclust)
out = clustvarsel(numt)
out## ------------------------------------------------------
## Variable selection for Gaussian model-based clustering
## Stepwise (forward/backward) greedy search
## ------------------------------------------------------
##
## Variable proposed Type of step BICclust Model G BICdiff Decision
## quantity Add -4308.761 E 9 687.4466 Accepted
## cogs Add -16306.851 VEV 9 1083.0132 Accepted
## unitprice Add -21393.079 EVV 7 2812.3733 Accepted
## unitprice Remove -16306.851 VEV 9 2812.3733 Rejected
## rating Add -25510.859 EVV 7 -184.0917 Rejected
## unitprice Remove -16306.851 VEV 9 2812.3733 Rejected
##
## Selected subset: quantity, cogs, unitpricewe observe that the columns quantity,cogs and unitprice have been accepted and can be used for modelling.This is a more accurate way of feature selection it can be used to furtherbreakdown from PCA and CORRELATION matrix .It is a form of hyperparametee tuning.
# The selection algorithm would indicate that the subset
# we use for the clustering model is composed of variables X1 and X2
# and that other variables should be rejected.
# Having identified the variables that we use, we proceed to build the clustering model:
#
Subset1 = numt[,out$subset]
crc = Mclust(Subset1)
summary(crc)## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust EVV (ellipsoidal, equal volume) model with 7 components:
##
## log-likelihood n df BIC ICL
## -10478.95 1000 63 -21393.08 -21446.89
##
## Clustering table:
## 1 2 3 4 5 6 7
## 173 134 126 148 128 142 149