Shopping Behavior and Product Rating Analysis
Martin Okwumuo
2026-01-10
#Introduction
This analysis explores customer behavior and product performance using a retail dataset. The dataset was gotten from Kaggle at “https://www.kaggle.com/datasets/nalisha/shopping-behaviour-and-product-ranking-dateset”. The goal is to draw insight from the dataset on which age bracket makes the most purchases, the season with the highest sales volume, the location with highest sales volume and general customer behavioral pattern to help improve sales and customer rating.
Data Preprocessing
Loading the Required Libraries
library(tidyverse)## -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
## v dplyr 1.1.4 v readr 2.1.6
## v forcats 1.0.1 v stringr 1.6.0
## v ggplot2 4.0.1 v tibble 3.3.0
## v lubridate 1.9.4 v tidyr 1.3.2
## v purrr 1.2.0
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(janitor)##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testlibrary(lubridate)
library(ggplot2)
library(dplyr)Downloading the data
# Dataset identifier
dataset <- "nalisha/shopping-behaviour-and-product-ranking-dateset"
# Create data directory if it doesn't exist
if (!dir.exists("../data")) {
dir.create("../data")
}
# Download only if file does not already exist
if (!file.exists("../data/shopping_behavior_updated.csv")) {
system(
paste(
"kaggle datasets download",
dataset,
"-p ../data --unzip"
)
)
}## [1] 0list.files("../data")## [1] "shopping_behavior_updated (1).csv"3. Load and Inspect the Data
Load dataset
customer_data <- read_csv("../data/shopping_behavior_updated (1).csv")## Rows: 3900 Columns: 16
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (11): Gender, Item Purchased, Category, Location, Size, Color, Season, S...
## dbl (5): Customer ID, Age, Purchase Amount (USD), Review Rating, Previous P...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(customer_data)## # A tibble: 6 x 16
## `Customer ID` Age Gender `Item Purchased` Category `Purchase Amount (USD)`
## <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 1 55 Male Blouse Clothing 53
## 2 2 19 Male Sweater Clothing 64
## 3 3 50 Male Jeans Clothing 73
## 4 4 21 Male Sandals Footwear 90
## 5 5 45 Male Blouse Clothing 49
## 6 6 46 Male Sneakers Footwear 20
## # i 10 more variables: Location <chr>, Size <chr>, Color <chr>, Season <chr>,
## # `Review Rating` <dbl>, `Subscription Status` <chr>,
## # `Discount Applied` <chr>, `Previous Purchases` <dbl>,
## # `Payment Method` <chr>, `Frequency of Purchases` <chr>Clean column names
data <- clean_names(customer_data)Preview data
head(data)## # A tibble: 6 x 16
## customer_id age gender item_purchased category purchase_amount_usd location
## <dbl> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 1 55 Male Blouse Clothing 53 Kentucky
## 2 2 19 Male Sweater Clothing 64 Maine
## 3 3 50 Male Jeans Clothing 73 Massachu~
## 4 4 21 Male Sandals Footwear 90 Rhode Is~
## 5 5 45 Male Blouse Clothing 49 Oregon
## 6 6 46 Male Sneakers Footwear 20 Wyoming
## # i 9 more variables: size <chr>, color <chr>, season <chr>,
## # review_rating <dbl>, subscription_status <chr>, discount_applied <chr>,
## # previous_purchases <dbl>, payment_method <chr>,
## # frequency_of_purchases <chr>glimpse(data)## Rows: 3,900
## Columns: 16
## $ customer_id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, ~
## $ age <dbl> 55, 19, 50, 21, 45, 46, 63, 27, 26, 57, 53, 30,~
## $ gender <chr> "Male", "Male", "Male", "Male", "Male", "Male",~
## $ item_purchased <chr> "Blouse", "Sweater", "Jeans", "Sandals", "Blous~
## $ category <chr> "Clothing", "Clothing", "Clothing", "Footwear",~
## $ purchase_amount_usd <dbl> 53, 64, 73, 90, 49, 20, 85, 34, 97, 31, 34, 68,~
## $ location <chr> "Kentucky", "Maine", "Massachusetts", "Rhode Is~
## $ size <chr> "L", "L", "S", "M", "M", "M", "M", "L", "L", "M~
## $ color <chr> "Gray", "Maroon", "Maroon", "Maroon", "Turquois~
## $ season <chr> "Winter", "Winter", "Spring", "Spring", "Spring~
## $ review_rating <dbl> 3.1, 3.1, 3.1, 3.5, 2.7, 2.9, 3.2, 3.2, 2.6, 4.~
## $ subscription_status <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"~
## $ discount_applied <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes"~
## $ previous_purchases <dbl> 14, 2, 23, 49, 31, 14, 49, 19, 8, 4, 26, 10, 37~
## $ payment_method <chr> "Venmo", "Cash", "Credit Card", "PayPal", "PayP~
## $ frequency_of_purchases <chr> "Fortnightly", "Fortnightly", "Weekly", "Weekly~Structure and summary
str(data)## spc_tbl_ [3,900 x 16] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ customer_id : num [1:3900] 1 2 3 4 5 6 7 8 9 10 ...
## $ age : num [1:3900] 55 19 50 21 45 46 63 27 26 57 ...
## $ gender : chr [1:3900] "Male" "Male" "Male" "Male" ...
## $ item_purchased : chr [1:3900] "Blouse" "Sweater" "Jeans" "Sandals" ...
## $ category : chr [1:3900] "Clothing" "Clothing" "Clothing" "Footwear" ...
## $ purchase_amount_usd : num [1:3900] 53 64 73 90 49 20 85 34 97 31 ...
## $ location : chr [1:3900] "Kentucky" "Maine" "Massachusetts" "Rhode Island" ...
## $ size : chr [1:3900] "L" "L" "S" "M" ...
## $ color : chr [1:3900] "Gray" "Maroon" "Maroon" "Maroon" ...
## $ season : chr [1:3900] "Winter" "Winter" "Spring" "Spring" ...
## $ review_rating : num [1:3900] 3.1 3.1 3.1 3.5 2.7 2.9 3.2 3.2 2.6 4.8 ...
## $ subscription_status : chr [1:3900] "Yes" "Yes" "Yes" "Yes" ...
## $ discount_applied : chr [1:3900] "Yes" "Yes" "Yes" "Yes" ...
## $ previous_purchases : num [1:3900] 14 2 23 49 31 14 49 19 8 4 ...
## $ payment_method : chr [1:3900] "Venmo" "Cash" "Credit Card" "PayPal" ...
## $ frequency_of_purchases: chr [1:3900] "Fortnightly" "Fortnightly" "Weekly" "Weekly" ...
## - attr(*, "spec")=
## .. cols(
## .. `Customer ID` = col_double(),
## .. Age = col_double(),
## .. Gender = col_character(),
## .. `Item Purchased` = col_character(),
## .. Category = col_character(),
## .. `Purchase Amount (USD)` = col_double(),
## .. Location = col_character(),
## .. Size = col_character(),
## .. Color = col_character(),
## .. Season = col_character(),
## .. `Review Rating` = col_double(),
## .. `Subscription Status` = col_character(),
## .. `Discount Applied` = col_character(),
## .. `Previous Purchases` = col_double(),
## .. `Payment Method` = col_character(),
## .. `Frequency of Purchases` = col_character()
## .. )
## - attr(*, "problems")=<externalptr>summary(data)## customer_id age gender item_purchased
## Min. : 1.0 Min. :18.00 Length:3900 Length:3900
## 1st Qu.: 975.8 1st Qu.:31.00 Class :character Class :character
## Median :1950.5 Median :44.00 Mode :character Mode :character
## Mean :1950.5 Mean :44.07
## 3rd Qu.:2925.2 3rd Qu.:57.00
## Max. :3900.0 Max. :70.00
## category purchase_amount_usd location size
## Length:3900 Min. : 20.00 Length:3900 Length:3900
## Class :character 1st Qu.: 39.00 Class :character Class :character
## Mode :character Median : 60.00 Mode :character Mode :character
## Mean : 59.76
## 3rd Qu.: 81.00
## Max. :100.00
## color season review_rating subscription_status
## Length:3900 Length:3900 Min. :2.50 Length:3900
## Class :character Class :character 1st Qu.:3.10 Class :character
## Mode :character Mode :character Median :3.70 Mode :character
## Mean :3.75
## 3rd Qu.:4.40
## Max. :5.00
## discount_applied previous_purchases payment_method
## Length:3900 Min. : 1.00 Length:3900
## Class :character 1st Qu.:13.00 Class :character
## Mode :character Median :25.00 Mode :character
## Mean :25.35
## 3rd Qu.:38.00
## Max. :50.00
## frequency_of_purchases
## Length:3900
## Class :character
## Mode :character
##
##
## Data Preparation
Checking for missing data
sum(is.na(data))## [1] 0Checking for duplicate data
sum(duplicated(data))## [1] 0Create Age Brackets
data <- data %>%
mutate(age_group = case_when(
age < 18 ~ "Under 18",
age >= 18 & age <= 25 ~ "18–25",
age >= 26 & age <= 35 ~ "26–35",
age >= 36 & age <= 45 ~ "36–45",
age >= 46 & age <= 60 ~ "46–60",
TRUE ~ "60+"
))Age Bracket with the Most Purchases
age_sales <- data %>%
group_by(age_group) %>%
summarise(
total_purchases = n(),
total_sales = sum(purchase_amount_usd, na.rm = TRUE)
) %>%
arrange(desc(total_purchases))
age_sales## # A tibble: 5 x 3
## age_group total_purchases total_sales
## <chr> <int> <dbl>
## 1 46–60 1135 67711
## 2 26–35 742 44342
## 3 36–45 729 43234
## 4 60+ 723 43164
## 5 18–25 571 34630Visualization
age_sales %>%
ggplot(aes(x = reorder(age_group, -total_purchases), y = total_purchases)) +
geom_col(fill = "steelblue") +
labs(title = "Purchases by Age Group",
x = "Age Group",
y = "Number of Purchases")
Season with the Highest Sales Volume
season_sales <- data %>%
group_by(season) %>%
summarise(
total_purchases = n(),
total_sales = sum(purchase_amount_usd, na.rm = TRUE)
) %>%
arrange(desc(total_sales))
season_sales## # A tibble: 4 x 3
## season total_purchases total_sales
## <chr> <int> <dbl>
## 1 Fall 975 60018
## 2 Spring 999 58679
## 3 Winter 971 58607
## 4 Summer 955 55777season_sales %>%
ggplot(aes(x = reorder(season, -total_sales), y = total_sales)) +
geom_col(fill = "darkgreen") +
labs(title = "Total Sales by Season",
x = "Season",
y = "Total Sales (USD)")
Location with the Highest Sales Volume
location_sales <- data %>%
group_by(location) %>%
summarise(
total_purchases = n(),
total_sales = sum(purchase_amount_usd, na.rm = TRUE)
) %>%
arrange(desc(total_sales))
location_sales## # A tibble: 50 x 3
## location total_purchases total_sales
## <chr> <int> <dbl>
## 1 Montana 96 5784
## 2 Illinois 92 5617
## 3 California 95 5605
## 4 Idaho 93 5587
## 5 Nevada 87 5514
## 6 Alabama 89 5261
## 7 New York 87 5257
## 8 North Dakota 83 5220
## 9 West Virginia 81 5174
## 10 Nebraska 87 5172
## # i 40 more rowslocation_sales %>%
top_n(10, total_sales) %>%
ggplot(aes(x = reorder(location, total_sales), y = total_sales)) +
geom_col(fill = "purple") +
coord_flip() +
labs(title = "Top 10 Locations by Sales",
x = "Location",
y = "Total Sales (USD)")
Top items purchased in a particular season
top_category_per_season <- data %>%
group_by(season, item_purchased) %>%
summarise(
total_sales = sum(purchase_amount_usd, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(season) %>%
slice_max(total_sales, n = 1)
top_category_per_season## # A tibble: 4 x 3
## # Groups: season [4]
## season item_purchased total_sales
## <chr> <chr> <dbl>
## 1 Fall Hat 3224
## 2 Spring Sweater 3145
## 3 Summer Jewelry 3006
## 4 Winter Shirt 3102top_category_per_season %>%
ggplot(aes(x = item_purchased, y = total_sales, fill = season)) +
geom_col(position = "dodge") +
labs(
title = "Clothing Categories by Season",
x = "Clothing purchased",
y = "Number of Purchases"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Customer Behavior Analysis
Subscription vs Non‑Subscription Behavior
subscription_analysis <- data %>%
group_by(subscription_status) %>%
summarise(
avg_purchase_amount = mean(purchase_amount_usd, na.rm = TRUE),
avg_previous_purchases = mean(previous_purchases, na.rm = TRUE),
avg_rating = mean(review_rating, na.rm = TRUE)
)
subscription_analysis## # A tibble: 2 x 4
## subscription_status avg_purchase_amount avg_previous_purchases avg_rating
## <chr> <dbl> <dbl> <dbl>
## 1 No 59.9 25.1 3.75
## 2 Yes 59.5 26.1 3.74Effect of Discounts on Purchases
discount_analysis <- data %>%
group_by(discount_applied) %>%
summarise(
avg_purchase_amount = mean(purchase_amount_usd, na.rm = TRUE),
total_purchases = n()
)
discount_analysis## # A tibble: 2 x 3
## discount_applied avg_purchase_amount total_purchases
## <chr> <dbl> <int>
## 1 No 60.1 2223
## 2 Yes 59.3 1677Purchase Frequency and Ratings
frequency_rating <- data %>%
group_by(frequency_of_purchases) %>%
summarise(
avg_rating = mean(review_rating, na.rm = TRUE),
avg_spend = mean(purchase_amount_usd, na.rm = TRUE)
)
frequency_rating## # A tibble: 7 x 3
## frequency_of_purchases avg_rating avg_spend
## <chr> <dbl> <dbl>
## 1 Annually 3.76 60.2
## 2 Bi-Weekly 3.71 60.7
## 3 Every 3 Months 3.77 60.1
## 4 Fortnightly 3.76 59.1
## 5 Monthly 3.78 59.3
## 6 Quarterly 3.73 60.0
## 7 Weekly 3.76 59.0Conclusion and Recommendation
Recommendations to Improve Sales and Customer Ratings
Sales Improvement
- People within the age bracket of 46-60 years has shown to be the most active customer segment. A Focus marketing on this top‑spending age group with loyalty and promotions is key to improving sales.
- Some clothing sell higher during certain seasons. Falls generally has the highest sales record with Hats selling more followed by sweater in spring. Increasing inventory and promotions during high‑performing seasons will impact sales greatly.
- Locations with the highest sales volumes are strong markets that may need targeted marketing campaign. Under-performing regions may need localized promotions or better logistics.
- Subscribed customers often show higher loyalty, more repeat purchases, and better ratings. Encouraging subscriptions with exclusive benefits will surely impact sales and rating.
- Discounts can increase purchase frequency but may reduce average order, balance is key. Using personalized discounts based on purchase history or quantity order will yield better result.
Customer Experience & Ratings
The following measures will have positive impact on customer rating
- Improved product quality and sizing consistency.
- Faster delivery and more flexible and seamless payment methods.
- Rewarding loyal customers with points or cash back.
- Responding actively to customer reviews and feedback.
Conclusion
Understanding customer demographics, seasonal trends, and behavioral patterns provides actionable insights to boost sales and enhance customer satisfaction. Data‑driven decisions will lead to sustainable growth and stronger customer relationships.