Customer Segmentation Analysis
Taylor Johnson
2026-03-27
Working with the grocery shopping dataset gave me a new perspective on how customers view their shopping experiences. I noticed that opinions varied a lot when it came to things like customer service, product variety, and delivery or pickup options. Some customers were really satisfied, while others rated their experiences lower, which shows that people don’t all experience the same store in the same way.
I also found it interesting how the dataset included demographic details like age, gender, and education. It made it clear that shopping habits can differ depending on the type of customer. Because of that, businesses probably shouldn’t treat everyone the same and instead think about how to better meet the needs of different groups. Overall, this analysis showed me how useful it is to combine customer feedback with demographic information to help businesses improve and better serve their customers.
# 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")