Market Segmentation
1. Introduction
This document will perform Market Segmentation Analysis on the data provided by KTC. We will be looking into importing and performing cluster analysis on the data to find useful patterns in Customer data.
2. Descriptive Mining
We are going to explore the data and find the patterns and do the segmentation.
2.1. Data Exploration
We have information regarding 30 customers of KTC Company. We have details of their Age, Income, Marital Status, No. of Children and their financial status in the means of whether they have a mortgage loan and other loans.
## # A tibble: 30 × 7
## Age Female Income Married Children Loan Mortgage
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 48 1 17546 0 1 0 0
## 2 40 0 30085. 1 3 1 1
## 3 51 1 16575. 1 0 1 0
## 4 23 1 20375. 1 3 0 0
## 5 57 1 50576. 1 0 0 0
## 6 57 1 37870. 1 2 0 0
## 7 22 0 8877. 0 0 0 0
## 8 58 0 24947. 1 0 1 0
## 9 37 1 25304. 1 2 1 0
## 10 54 0 24212. 1 2 1 0
## # ℹ 20 more rows
##
## Data frame:crs$dataset[, c(crs$input, crs$risk, crs$target)] 30 observations and 7 variables Maximum # NAs:0
##
##
## Storage
## Age double
## Female double
## Income double
## Married double
## Children double
## Loan double
## Mortgage double
## Age Female Income Married Children Loan Mortgage
## Min. :22.00 Min. :0.0000 Min. : 8877 Min. :0.0 Min. :0.0000 Min. :0.0000 Min. :0.0
## 1st Qu.:37.25 1st Qu.:0.0000 1st Qu.:18166 1st Qu.:1.0 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0
## Median :47.00 Median :1.0000 Median :24241 Median :1.0 Median :0.5000 Median :0.0000 Median :0.0
## Mean :45.97 Mean :0.5667 Mean :28012 Mean :0.8 Mean :0.9333 Mean :0.4333 Mean :0.4
## 3rd Qu.:56.75 3rd Qu.:1.0000 3rd Qu.:35923 3rd Qu.:1.0 3rd Qu.:2.0000 3rd Qu.:1.0000 3rd Qu.:1.0
## Max. :66.00 Max. :1.0000 Max. :59804 Max. :1.0 Max. :3.0000 Max. :1.0000 Max. :1.0
2.1.1. Age
## crs$dataset["Age"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Age
## n missing distinct Info Mean pMedian Gmd .05 .10 .25 .50 .75 .90 .95
## 30 0 23 0.998 45.97 46.5 15.12 22.45 26.60 37.25 47.00 56.75 61.10 64.20
##
## lowest : 22 23 27 31 36, highest: 57 58 61 62 66
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.2. Female (Gender Column
## crs$dataset["Female"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Female
## n missing distinct Info Sum Mean
## 30 0 2 0.737 17 0.5667
##
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.3. Income
## crs$dataset["Income"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Income
## n missing distinct Info Mean pMedian Gmd .05 .10 .25 .50 .75 .90 .95
## 30 0 30 1 28012 25590 14919 13945 15716 18166 24241 35923 51039 56676
##
## lowest : 8877.07 12640.3 15538.8 15735.8 16497.3, highest: 41034 50576.3 55204.7 57880.7 59803.9
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.4. Marriage
## crs$dataset["Married"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Married
## n missing distinct Info Sum Mean
## 30 0 2 0.481 24 0.8
##
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.5. Children
## crs$dataset["Children"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Children
## n missing distinct Info Mean pMedian Gmd
## 30 0 4 0.858 0.9333 1 1.163
##
## Value 0 1 2 3
## Frequency 15 5 7 3
## Proportion 0.500 0.167 0.233 0.100
##
## For the frequency table, variable is rounded to the nearest 0
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.6. Loan
## crs$dataset["Loan"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Loan
## n missing distinct Info Sum Mean
## 30 0 2 0.737 13 0.4333
##
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.7. Mortgage
## crs$dataset["Mortgage"]
##
## 1 Variables 30 Observations
## ------------------------------------------------------------------------------------------------------------------------------------
## Mortgage
## n missing distinct Info Sum Mean
## 30 0 2 0.721 12 0.4
##
## ------------------------------------------------------------------------------------------------------------------------------------
2.1.8. Distributions
# Generate the plot.
p01 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Age) %>%
ggplot2::ggplot(ggplot2::aes(x=Age)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Age\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Age") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Female
# Generate the plot.
p02 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Female) %>%
ggplot2::ggplot(ggplot2::aes(x=Female)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Female\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Female") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Income
# Generate the plot.
p03 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Income) %>%
ggplot2::ggplot(ggplot2::aes(x=Income)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Income\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Income") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Married
# Generate the plot.
p04 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Married) %>%
ggplot2::ggplot(ggplot2::aes(x=Married)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Married\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Married") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Children
# Generate the plot.
p05 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Children) %>%
ggplot2::ggplot(ggplot2::aes(x=Children)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Children\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Children") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Loan
# Generate the plot.
p06 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Loan) %>%
ggplot2::ggplot(ggplot2::aes(x=Loan)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Loan\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Loan") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Mortgage
# Generate the plot.
p07 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Mortgage) %>%
ggplot2::ggplot(ggplot2::aes(x=Mortgage)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Mortgage\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Mortgage") +
ggplot2::labs(y="Density")
# Display the plots.
gridExtra::grid.arrange(p01, p02, p03, p04, p05, p06, p07)
2.2. Dendrogram
Observing the above dendrogram we can observe that
2.3. Elbow Method
# Elbow method for finding the no of clusters
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
fviz_nbclust(crs$dataset[, c(1:7)], kmeans, method = "wss") +
labs(subtitle = "Elbow Method")
We can observe that when the no. of clusters is 2 there is a sharp change in the total within sum of squares. This shows that 2 is the optimal no. of clusters to have for this dataset
3. Segmentation and Clustering
Clustering is a method of grouping the observation based on their similarities. We use distance measures for assessing the dissimilarity among the observations. There are many measures of distance including Euclidean, Manhattan etc, Similarly we have different types of clustering algorithms such as K Means, Hierarchical, BiClustering etc. We will begin with Hierarchical clustering as part of our data exploration analysis.
3.1. Hierarchical Clustering
No. of Clusters = 5
No. of Clusters = 4
No. of Clusters = 3
No. of Clusters = 2
3.2. K-means Clustering
## [1] "12 10 8"
## Age Female Income Married Children Loan Mortgage
## 4.596667e+01 5.666667e-01 2.801187e+04 8.000000e-01 9.333333e-01 4.333333e-01 4.000000e-01
## Age Female Income Married Children Loan Mortgage
## 1 37.000 0.5833333 16826.18 0.6666667 1.000 0.25 0.4166667
## 2 47.200 0.5000000 25661.02 0.8000000 1.100 0.80 0.4000000
## 3 57.875 0.6250000 47728.97 1.0000000 0.625 0.25 0.3750000
## [1] 131352595 61338314 586111857
4. Conclusion
We have successfully explored the data and performed the appropriate clustering methods to identify the pattern in the data.
From this we can see the formed clusters clearly, and we can say that all the data points within each cluster are significantly similar to each other. From this we can do various analysis like classifying a new entry to the dataset or identifying largest common cluster to find the most common type of customers.