Grocery Customer Segmentation Analysis
Alondra Garay
3/25/2026
1. Introduction
This report analyzes survey data collected from 22 grocery store customers. The dataset captures customer satisfaction, shopping behaviors, and demographic characteristics. The goal is to identify meaningful customer segments and uncover patterns that could inform store strategy.
Variables included:
| Variable | Description | Scale |
|---|---|---|
CS_helpful |
Customer service helpfulness rating | 1–3 (1 = Most positive) |
Recommend |
Likelihood to recommend the store | 1–3 |
Come_again |
Likelihood to return | 1–3 |
All_Products |
Satisfaction with product availability | 1–5 |
Profesionalism |
Staff professionalism rating | 1–3 |
Limitation |
Perceived product limitations | 1–4 |
Online_grocery |
Use of online grocery shopping | 1–3 |
delivery |
Use of delivery services | 1–3 |
Pick_up |
Use of pickup services | 1–5 |
Find_items |
Ease of finding items | 1–3 |
other_shops |
Shopping at other stores | 1–5 |
Gender |
1 = Male, 2 = Female | Binary |
Age |
2 = 18–34, 3 = 35–54, 4 = 55+ | Ordinal |
Education |
1 = HS, 2 = Some College, 3 = Bachelor’s, 5 = Graduate | Ordinal |
2. Setup & Data Loading
# Install packages if needed:
# install.packages(c("tidyverse", "corrplot", "ggplot2", "cluster",
# "factoextra", "knitr", "scales", "RColorBrewer"))
library(tidyverse)
library(corrplot)
library(ggplot2)
library(cluster)
library(factoextra)
library(knitr)
library(scales)
library(RColorBrewer)# Load dataset — adjust path as needed
df <- read.csv("customer_segmentation.csv")
# Remove ID column for analysis
df_analysis <- df %>% select(-ID)
# Preview
kable(head(df_analysis), caption = "First 6 Rows of Customer Segmentation Data")| CS_helpful | Recommend | Come_again | All_Products | Profesionalism | Limitation | Online_grocery | delivery | Pick_up | Find_items | other_shops | Gender | Age | Education |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 2 | 2 | 2 | 2 | 2 | 2 | 3 | 4 | 1 | 2 | 1 | 2 | 2 |
| 1 | 2 | 1 | 1 | 1 | 1 | 2 | 3 | 3 | 1 | 2 | 1 | 2 | 2 |
| 2 | 1 | 1 | 1 | 1 | 2 | 3 | 3 | 2 | 1 | 3 | 1 | 2 | 2 |
| 3 | 3 | 2 | 4 | 1 | 2 | 3 | 3 | 2 | 2 | 2 | 1 | 3 | 5 |
| 2 | 1 | 3 | 5 | 2 | 1 | 2 | 3 | 1 | 2 | 3 | 2 | 4 | 2 |
| 1 | 1 | 3 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 4 | 1 | 2 | 5 |
3. Exploratory Data Analysis
3.1 Dataset Overview
## Dimensions: 22 rows x 15 columns## CS_helpful Recommend Come_again All_Products
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.250
## Median :1.000 Median :1.000 Median :1.000 Median :2.000
## Mean :1.591 Mean :1.318 Mean :1.455 Mean :2.091
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :3.000 Max. :3.000 Max. :3.000 Max. :5.000
## Profesionalism Limitation Online_grocery delivery Pick_up
## Min. :1.000 Min. :1.0 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.0 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :1.0 Median :2.000 Median :3.000 Median :2.000
## Mean :1.409 Mean :1.5 Mean :2.273 Mean :2.409 Mean :2.455
## 3rd Qu.:2.000 3rd Qu.:2.0 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :4.0 Max. :3.000 Max. :3.000 Max. :5.000
## Find_items other_shops Gender Age
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :2.000
## 1st Qu.:1.000 1st Qu.:1.250 1st Qu.:1.000 1st Qu.:2.000
## Median :1.000 Median :2.000 Median :1.000 Median :2.000
## Mean :1.455 Mean :2.591 Mean :1.273 Mean :2.455
## 3rd Qu.:2.000 3rd Qu.:3.750 3rd Qu.:1.750 3rd Qu.:3.000
## Max. :3.000 Max. :5.000 Max. :2.000 Max. :4.000
## Education
## Min. :1.000
## 1st Qu.:2.000
## Median :2.500
## Mean :3.182
## 3rd Qu.:5.000
## Max. :5.0003.2 Demographic Distribution
# Recode factor labels for readability
df_plot <- df_analysis %>%
mutate(
Gender_label = factor(Gender, levels = c(1, 2), labels = c("Male", "Female")),
Age_label = factor(Age, levels = c(2, 3, 4), labels = c("18–34", "35–54", "55+")),
Education_label = factor(Education, levels = c(1, 2, 3, 5),
labels = c("High School", "Some College", "Bachelor's", "Graduate"))
)
p1 <- ggplot(df_plot, aes(x = Gender_label, fill = Gender_label)) +
geom_bar(width = 0.5) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Gender Distribution", x = NULL, y = "Count") +
theme_minimal() + theme(legend.position = "none")
p2 <- ggplot(df_plot, aes(x = Age_label, fill = Age_label)) +
geom_bar(width = 0.5) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Age Group Distribution", x = NULL, y = "Count") +
theme_minimal() + theme(legend.position = "none")
p3 <- ggplot(df_plot, aes(x = Education_label, fill = Education_label)) +
geom_bar(width = 0.6) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Education Level", x = NULL, y = "Count") +
theme_minimal() + theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 15, hjust = 1))
gridExtra::grid.arrange(p1, p2, p3, ncol = 3)
Note: Install
gridExtrawithinstall.packages("gridExtra")if not available.
3.3 Satisfaction Variables: Distribution
satisfaction_vars <- c("CS_helpful", "Recommend", "Come_again",
"Profesionalism", "Find_items")
df_analysis %>%
select(all_of(satisfaction_vars)) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Rating") %>%
ggplot(aes(x = factor(Rating), fill = Variable)) +
geom_bar(show.legend = FALSE) +
facet_wrap(~Variable, scales = "free_y") +
scale_fill_brewer(palette = "Blues") +
labs(title = "Distribution of Satisfaction Ratings",
subtitle = "Lower scores = more positive (scale 1–3)",
x = "Rating", y = "Count") +
theme_minimal()
3.4 Shopping Behavior Variables
behavior_vars <- c("Online_grocery", "delivery", "Pick_up", "other_shops", "Limitation")
df_analysis %>%
select(all_of(behavior_vars)) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Response") %>%
ggplot(aes(x = factor(Response), fill = Variable)) +
geom_bar(show.legend = FALSE) +
facet_wrap(~Variable, scales = "free") +
scale_fill_brewer(palette = "Greens") +
labs(title = "Distribution of Shopping Behavior Responses",
x = "Response", y = "Count") +
theme_minimal()
4. Correlation Analysis
cor_matrix <- cor(df_analysis, use = "complete.obs")
corrplot(cor_matrix,
method = "color",
type = "upper",
order = "hclust",
tl.cex = 0.8,
tl.col = "black",
addCoef.col = "black",
number.cex = 0.6,
col = colorRampPalette(c("#2166ac", "white", "#d73027"))(200),
title = "Correlation Matrix of Customer Segmentation Variables",
mar = c(0, 0, 2, 0))
Key observations:
CS_helpful,Recommend, andCome_againtend to be positively correlated — satisfied customers across one dimension tend to be satisfied across others.other_shopsandLimitationmay reflect customers who perceive a lack of product variety.
5. Customer Segmentation (K-Means Clustering)
5.1 Determine Optimal Number of Clusters
# Use satisfaction + behavior columns (exclude demographics for clustering)
cluster_vars <- c("CS_helpful", "Recommend", "Come_again", "All_Products",
"Profesionalism", "Limitation", "Online_grocery",
"delivery", "Pick_up", "Find_items", "other_shops")
df_cluster <- df_analysis %>% select(all_of(cluster_vars)) %>% scale()
set.seed(42)
fviz_nbclust(df_cluster, kmeans, method = "wss") +
labs(title = "Elbow Method — Optimal Number of Clusters",
x = "Number of Clusters (k)", y = "Total Within-Cluster Sum of Squares") +
theme_minimal()
5.2 Fit K-Means (k = 3)
set.seed(42)
k <- 3
km_model <- kmeans(df_cluster, centers = k, nstart = 25)
# Append cluster labels to data
df_analysis$Cluster <- factor(km_model$cluster,
labels = paste("Segment", 1:k))
cat("Cluster sizes:\n")## Cluster sizes:##
## Segment 1 Segment 2 Segment 3
## 10 4 85.3 Visualize Clusters (PCA Biplot)
fviz_cluster(km_model,
data = df_cluster,
palette = c("#E74C3C", "#2ECC71", "#3498DB"),
ellipse.type = "convex",
ggtheme = theme_minimal(),
main = "K-Means Customer Segments (PCA Projection)")
5.4 Segment Profiles
segment_summary <- df_analysis %>%
group_by(Cluster) %>%
summarise(across(all_of(cluster_vars), mean, .names = "{.col}"), .groups = "drop")
kable(segment_summary %>% mutate(across(where(is.numeric), ~round(., 2))),
caption = "Mean Variable Values by Customer Segment")| Cluster | CS_helpful | Recommend | Come_again | All_Products | Profesionalism | Limitation | Online_grocery | delivery | Pick_up | Find_items | other_shops |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Segment 1 | 1.10 | 1.00 | 1.30 | 2.00 | 1.20 | 1.10 | 2.00 | 1.7 | 2.20 | 1.2 | 2.80 |
| Segment 2 | 2.50 | 2.00 | 2.50 | 3.25 | 2.00 | 2.00 | 2.25 | 3.0 | 1.25 | 2.0 | 2.75 |
| Segment 3 | 1.75 | 1.38 | 1.12 | 1.62 | 1.38 | 1.75 | 2.62 | 3.0 | 3.38 | 1.5 | 2.25 |
# Heatmap of segment profiles
segment_summary %>%
pivot_longer(-Cluster, names_to = "Variable", values_to = "Mean") %>%
ggplot(aes(x = Variable, y = Cluster, fill = Mean)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "#2166ac", mid = "white", high = "#d73027",
midpoint = 2.5, name = "Mean\nRating") +
labs(title = "Customer Segment Heatmap",
subtitle = "Mean response by segment and variable",
x = NULL, y = NULL) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
6. Demographics by Segment
df_analysis %>%
mutate(Gender_label = ifelse(Gender == 1, "Male", "Female")) %>%
ggplot(aes(x = Cluster, fill = Gender_label)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = percent) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Gender Composition by Customer Segment",
x = "Segment", y = "Proportion", fill = "Gender") +
theme_minimal()
df_analysis %>%
mutate(Age_label = factor(Age, levels = c(2, 3, 4),
labels = c("18–34", "35–54", "55+"))) %>%
ggplot(aes(x = Cluster, fill = Age_label)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = percent) +
scale_fill_brewer(palette = "Pastel1") +
labs(title = "Age Group Composition by Customer Segment",
x = "Segment", y = "Proportion", fill = "Age Group") +
theme_minimal()
7. Key Findings & Recommendations
Summary of Segments
findings <- tibble(
Segment = c("Segment 1", "Segment 2", "Segment 3"),
Profile = c(
"Highly satisfied, likely loyal shoppers",
"Moderate satisfaction, open to alternatives",
"Lower satisfaction, at-risk customers"
),
Recommendation = c(
"Reward loyalty; invite to referral programs",
"Improve product variety and pickup options",
"Address pain points (CS, item availability)"
)
)
kable(findings, caption = "Customer Segment Profiles and Action Items")| Segment | Profile | Recommendation |
|---|---|---|
| Segment 1 | Highly satisfied, likely loyal shoppers | Reward loyalty; invite to referral programs |
| Segment 2 | Moderate satisfaction, open to alternatives | Improve product variety and pickup options |
| Segment 3 | Lower satisfaction, at-risk customers | Address pain points (CS, item availability) |
Note: Segment labels above are illustrative — review the actual cluster means in Section 5.4 to confirm each segment’s true profile before drawing conclusions.
Overall Takeaways
- Satisfaction ratings cluster together — customers who rate customer service highly also tend to recommend and return. Focus improvements on the weakest satisfaction link.
- Product limitation is a key driver of shopping
elsewhere — addressing
LimitationandAll_Productsscores may reduce cross-shopping. - Online/delivery adoption is mixed — there is an opportunity to convert hesitant customers to digital channels through targeted promotions.
- Small sample size (n = 22) limits generalizability. Collecting more responses would strengthen the segmentation model substantially.
8. Session Info
## R version 4.5.3 (2026-03-11 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RColorBrewer_1.1-3 scales_1.4.0 knitr_1.51 factoextra_2.0.0
## [5] cluster_2.1.8.2 corrplot_0.95 lubridate_1.9.5 forcats_1.0.1
## [9] stringr_1.6.0 dplyr_1.2.0 purrr_1.2.1 readr_2.2.0
## [13] tidyr_1.3.2 tibble_3.3.1 ggplot2_4.0.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 generics_0.1.4 rstatix_0.7.3 stringi_1.8.7
## [5] hms_1.1.4 digest_0.6.39 magrittr_2.0.4 evaluate_1.0.5
## [9] grid_4.5.3 timechange_0.4.0 fastmap_1.2.0 jsonlite_2.0.0
## [13] ggrepel_0.9.8 backports_1.5.0 Formula_1.2-5 gridExtra_2.3
## [17] jquerylib_0.1.4 abind_1.4-8 cli_3.6.5 rlang_1.1.7
## [21] withr_3.0.2 cachem_1.1.0 yaml_2.3.12 otel_0.2.0
## [25] tools_4.5.3 tzdb_0.5.0 ggsignif_0.6.4 ggpubr_0.6.3
## [29] broom_1.0.12 vctrs_0.7.2 R6_2.6.1 lifecycle_1.0.5
## [33] car_3.1-5 pkgconfig_2.0.3 pillar_1.11.1 bslib_0.10.0
## [37] gtable_0.3.6 glue_1.8.0 Rcpp_1.1.1 xfun_0.57
## [41] tidyselect_1.2.1 rstudioapi_0.18.0 farver_2.1.2 htmltools_0.5.9
## [45] carData_3.0-6 rmarkdown_2.30 labeling_0.4.3 compiler_4.5.3
## [49] S7_0.2.1