Customer Segmentation Analysis
Jaylene Felix
2026-03-25
Looking at the grocery shopping dataset helped me better understand how customers feel about their shopping experience. The data showed that people rated customer service, product options, and delivery and pickup differently. Some customers had more positive experiences, while others gave lower ratings, which shows that not everyone is equally satisfied.
The dataset also included information like age, gender, and education, which helped show that different groups of people shop differently. This means businesses can’t treat all customers the same and should instead focus on different groups when making decisions. Overall, this analysis shows how customer feedback and basic demographic information can help businesses improve their services and better meet customer needs.
# Automatically install and load required packages
packages <- c("tidyverse")
for (p in packages) {
if (!require(p, character.only = TRUE)) {
install.packages(p, dependencies = TRUE)
library(p, character.only = TRUE)
}
}## Loading required package: tidyverse## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.0 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Load the CSV dataset
data <- read.csv("customer_segmentation.csv")
head(data)## ID CS_helpful Recommend Come_again All_Products Profesionalism Limitation
## 1 1 2 2 2 2 2 2
## 2 2 1 2 1 1 1 1
## 3 3 2 1 1 1 1 2
## 4 4 3 3 2 4 1 2
## 5 5 2 1 3 5 2 1
## 6 6 1 1 3 2 1 1
## Online_grocery delivery Pick_up Find_items other_shops Gender Age Education
## 1 2 3 4 1 2 1 2 2
## 2 2 3 3 1 2 1 2 2
## 3 3 3 2 1 3 1 2 2
## 4 3 3 2 2 2 1 3 5
## 5 2 3 1 2 3 2 4 2
## 6 1 2 1 1 4 1 2 5# Basic summary
summary(data)## ID CS_helpful Recommend Come_again
## Min. : 1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.: 6.25 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :11.50 Median :1.000 Median :1.000 Median :1.000
## Mean :11.50 Mean :1.591 Mean :1.318 Mean :1.455
## 3rd Qu.:16.75 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000
## Max. :22.00 Max. :3.000 Max. :3.000 Max. :3.000
## All_Products Profesionalism Limitation Online_grocery delivery
## Min. :1.000 Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000
## 1st Qu.:1.250 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :1.000 Median :1.0 Median :2.000 Median :3.000
## Mean :2.091 Mean :1.409 Mean :1.5 Mean :2.273 Mean :2.409
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :3.000 Max. :4.0 Max. :3.000 Max. :3.000
## Pick_up Find_items other_shops Gender
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.250 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2.000 Median :1.000
## Mean :2.455 Mean :1.455 Mean :2.591 Mean :1.273
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.750 3rd Qu.:1.750
## Max. :5.000 Max. :3.000 Max. :5.000 Max. :2.000
## Age Education
## Min. :2.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.500
## Mean :2.455 Mean :3.182
## 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :4.000 Max. :5.000str(data)## 'data.frame': 22 obs. of 15 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CS_helpful : int 2 1 2 3 2 1 2 1 1 1 ...
## $ Recommend : int 2 2 1 3 1 1 1 1 1 1 ...
## $ Come_again : int 2 1 1 2 3 3 1 1 1 1 ...
## $ All_Products : int 2 1 1 4 5 2 2 2 2 1 ...
## $ Profesionalism: int 2 1 1 1 2 1 2 1 2 1 ...
## $ Limitation : int 2 1 2 2 1 1 1 2 1 1 ...
## $ Online_grocery: int 2 2 3 3 2 1 2 1 2 3 ...
## $ delivery : int 3 3 3 3 3 2 2 1 1 2 ...
## $ Pick_up : int 4 3 2 2 1 1 2 2 3 2 ...
## $ Find_items : int 1 1 1 2 2 1 1 2 1 1 ...
## $ other_shops : int 2 2 3 2 3 4 1 4 1 1 ...
## $ Gender : int 1 1 1 1 2 1 1 1 2 2 ...
## $ Age : int 2 2 2 3 4 2 2 2 2 2 ...
## $ Education : int 2 2 2 5 2 5 3 2 1 2 ...colSums(is.na(data))## ID CS_helpful Recommend Come_again All_Products
## 0 0 0 0 0
## Profesionalism Limitation Online_grocery delivery Pick_up
## 0 0 0 0 0
## Find_items other_shops Gender Age Education
## 0 0 0 0 0# Customer Service Helpfulness
ggplot(data, aes(x = CS_helpful)) +
geom_bar(fill = "blue") +
theme_minimal() +
labs(title = "Customer Service Helpfulness Ratings")
# Likelihood to Recommend
ggplot(data, aes(x = Recommend)) +
geom_bar(fill = "green") +
theme_minimal() +
labs(title = "Likelihood to Recommend")
# Likelihood to Return
ggplot(data, aes(x = Come_again)) +
geom_bar(fill = "purple") +
theme_minimal() +
labs(title = "Likelihood to Return")
# Online Grocery vs Delivery
ggplot(data, aes(x = Online_grocery, y = delivery)) +
geom_point(color = "red") +
theme_minimal() +
labs(title = "Online Grocery vs Delivery Ratings")
# Age Distribution
ggplot(data, aes(x = Age)) +
geom_bar(fill = "orange") +
theme_minimal() +
labs(title = "Age Distribution")
# Customer Segmentation
set.seed(123)
data_scaled <- scale(data[, sapply(data, is.numeric)])
kmeans_result <- kmeans(data_scaled, centers = 3)
data$Cluster <- as.factor(kmeans_result$cluster)
ggplot(data, aes(x = Recommend, y = Come_again, color = Cluster)) +
geom_point(size = 3) +
theme_minimal() +
labs(title = "Customer Segments")