UK-Based E-Commerce Business Analysis
Martin Okwumuo
2026-01-06
#Summary
- This analysis evaluates one year of sales transaction data from a UK-based online retail business specializing in gifts and home wares for adults and children. The dataset can be accessed through https://www.kaggle.com/datasets/gabrielramos87/an-online-shop-business. For the purpose of this analysis, the dataset was downloaded directly from kaggle. It includes approximately 500,000 transactions from customers across multiple countries, comprising both individual consumers and small businesses purchasing in bulk. The analysis was carried out to reveal how the sales trend over the months, the most frequently purchased products, how many products the customer purchase in each transaction and the most profitable segment customers.The insights gained from the analysis will help with recommendations on how to make the business more profitable.
#Data preprocessing
##Creating the target directory
# Create directory
dir.create("UK-Based_business_data", showWarnings = FALSE)
# Download dataset using Kaggle CLI
system(
"kaggle datasets download -d gabrielramos87/an-online-shop-business -p UK-Based_business_data --unzip"
)## [1] 0#Reading the dataset
retail <- read.csv(
"UK-Based_business_data/Sales Transaction v.4a.csv",
stringsAsFactors = FALSE
)
str(retail)## 'data.frame': 536350 obs. of 8 variables:
## $ TransactionNo: chr "581482" "581475" "581475" "581475" ...
## $ Date : chr "12/9/2019" "12/9/2019" "12/9/2019" "12/9/2019" ...
## $ ProductNo : chr "22485" "22596" "23235" "23272" ...
## $ ProductName : chr "Set Of 2 Wooden Market Crates" "Christmas Star Wish List Chalkboard" "Storage Tin Vintage Leaf" "Tree T-Light Holder Willie Winkie" ...
## $ Price : num 21.5 10.6 11.5 10.6 11.9 ...
## $ Quantity : int 12 36 12 12 6 24 18 12 12 24 ...
## $ CustomerNo : int 17490 13069 13069 13069 13069 13069 13069 13069 13069 13069 ...
## $ Country : chr "United Kingdom" "United Kingdom" "United Kingdom" "United Kingdom" ...#Loading the packages
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(lubridate)
library(knitr)
library(ggplot2)
library(scales)##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factor#Data cleaning and preparation
##This cleaning was done to Remove cancelled transactions, negative quantities, prices and Convert the Date to proper date format
Create Revenue variable
clean_retail <- retail %>%
filter(!grepl("^C", TransactionNo)) %>% # Remove cancelled invoices
filter(Quantity > 0, Price > 0) %>% # Remove negative values
mutate(
Date = as.Date(Date),
Month = floor_date(Date, "month"),
Revenue = Quantity * Price
)
summary(clean_retail)## TransactionNo Date ProductNo ProductName
## Length:527765 Min. :0001-04-20 Length:527765 Length:527765
## Class :character 1st Qu.:0005-09-20 Class :character Class :character
## Mode :character Median :0009-05-20 Mode :character Mode :character
## Mean :0008-07-12
## 3rd Qu.:0011-10-20
## Max. :0012-12-20
## NA's :301119
## Price Quantity CustomerNo Country
## Min. : 5.13 Min. : 1.0 Min. :12004 Length:527765
## 1st Qu.: 10.99 1st Qu.: 1.0 1st Qu.:13813 Class :character
## Median : 11.94 Median : 3.0 Median :15159 Mode :character
## Mean : 12.63 Mean : 10.6 Mean :15232
## 3rd Qu.: 14.09 3rd Qu.: 11.0 3rd Qu.:16729
## Max. :660.62 Max. :80995.0 Max. :18287
## NA's :1
## Month Revenue
## Min. :0001-04-01 Min. :5.130e+00
## 1st Qu.:0005-09-01 1st Qu.:1.717e+01
## Median :0009-05-01 Median :4.383e+01
## Mean :0008-06-23 Mean :1.193e+02
## 3rd Qu.:0011-10-01 3rd Qu.:1.194e+02
## Max. :0012-12-01 Max. :1.003e+06
## NA's :301119colnames(clean_retail)## [1] "TransactionNo" "Date" "ProductNo" "ProductName"
## [5] "Price" "Quantity" "CustomerNo" "Country"
## [9] "Month" "Revenue"#Sales trend over the month
monthly_sales <- clean_retail %>%
group_by(Month) %>%
summarise(Total_Sales = sum(Revenue))
monthly_sales## # A tibble: 121 x 2
## Month Total_Sales
## <date> <dbl>
## 1 0001-04-01 102906.
## 2 0001-05-01 225004.
## 3 0001-06-01 267325.
## 4 0001-07-01 189552.
## 5 0001-09-01 96938.
## 6 0001-10-01 155159.
## 7 0001-11-01 358480.
## 8 0001-12-01 132940.
## 9 0002-01-01 183508.
## 10 0002-02-01 113883.
## # i 111 more rowsggplot(monthly_sales, aes(Month, Total_Sales)) +
geom_line(color = "steelblue", linewidth = 1) +
geom_point() +
scale_y_continuous(
labels = scales::label_dollar(prefix = "£")
) +
labs(
title = "Monthly Sales Trend",
x = "Month",
y = "Total Sales (£)"
) +
theme_minimal()## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
#Checking for most frequently purchased product
top_products <- clean_retail %>%
group_by(ProductName) %>%
summarise(Total_Quantity = sum(Quantity)) %>%
arrange(desc(Total_Quantity)) %>%
slice_head(n = 10)
kable(top_products)| ProductName | Total_Quantity |
|---|---|
| Paper Craft Little Birdie | 80995 |
| Medium Ceramic Top Storage Jar | 78033 |
| Popcorn Holder | 56921 |
| World War 2 Gliders Asstd Designs | 55047 |
| Jumbo Bag Red Retrospot | 48478 |
| Cream Hanging Heart T-Light Holder | 37956 |
| Pack Of 72 Retrospot Cake Cases | 36515 |
| Assorted Colour Bird Ornament | 36493 |
| Rabbit Night Light | 30788 |
| Mini Paint Set Vintage | 26633 |
ggplot(top_products, aes(reorder(ProductName, Total_Quantity), Total_Quantity)) +
geom_col(fill = "darkgreen") +
coord_flip() +
labs(
title = "Top 10 Most Frequently Purchased Products",
x = "ProductName",
y = "Total Quantity Purchased"
) +
theme_minimal()
#Checking for number of products purcased per transaction
transaction_items <- clean_retail %>%
group_by(TransactionNo) %>%
summarise(Total_Items = sum(Quantity))
summary(transaction_items$Total_Items)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 71.0 153.0 282.5 298.0 80995.0ggplot(transaction_items, aes(Total_Items)) +
geom_histogram(bins = 50, fill = "orange", color = "black") +
labs(
title = "Distribution of Products Purchased Per Transaction",
x = "Number of Items",
y = "Frequency"
) +
theme_minimal()
#Most profitable customer segment
##Profit by country
country_profit <- clean_retail %>%
group_by(Country) %>%
summarise(Total_Revenue = sum(Revenue)) %>%
arrange(desc(Total_Revenue))
kable(head(country_profit, 10))| Country | Total_Revenue |
|---|---|
| United Kingdom | 52524658.5 |
| Netherlands | 2151553.6 |
| EIRE | 1713410.9 |
| Germany | 1371543.3 |
| France | 1330652.9 |
| Australia | 995607.9 |
| Sweden | 401879.9 |
| Switzerland | 361969.2 |
| Japan | 293155.4 |
| Spain | 281012.3 |
##Profit by Customer
customer_profit <- clean_retail %>%
group_by(CustomerNo) %>%
summarise(Total_Revenue = sum(Revenue)) %>%
arrange(desc(Total_Revenue))
kable(head(customer_profit, 10))| CustomerNo | Total_Revenue |
|---|---|
| 14646 | 2112282.0 |
| 16446 | 1002741.6 |
| 14911 | 914633.5 |
| 12415 | 900545.5 |
| 18102 | 897137.4 |
| 17450 | 891438.5 |
| 12346 | 840113.8 |
| 14156 | 694965.0 |
| 13694 | 646116.8 |
| 17511 | 639006.2 |
#Conclusion and Recommendation
##Conclusion
- The analysis shows that sales exhibit strong seasonality, with revenue increasing steadily toward the end of the year and peaking during the final months. This pattern is consistent with heightened consumer demand during holiday and festive periods, indicating that seasonal factors play a significant role in overall business performance.
- Product-level analysis reveals that a small group of products accounts for a disproportionately large share of total units sold. These high-frequency items form the core of the retailer’s sales volume and present opportunities for product bundling, targeted promotions, and inventory optimization.
- Transaction-level analysis indicates that most customers purchase a small number of items per transaction, reflecting typical consumer buying behavior. However, a notable portion of transactions involves large quantities, highlighting the presence of bulk-buying customers, likely small businesses or retailers.
- Customer profitability analysis shows that UK-based customers generate the highest revenue, and that a relatively small number of customers contribute a significant share of total sales. These high-value customers represent a critical segment for revenue stability and growth.
##Business Recommendations
- Focus on High-Value Customers; Introduction of loyalty programs for top-spending customers. Offering volume discounts for bulk buyers.
- Optimize Product Strategy; Bundle top-selling products. Ensure high-demand items are always in stock.
- Seasonal Marketing; Increase advertising and promotions during high-sales months especially towards th end of the year. Launching gift-themed campaigns before holidays.
- International Expansion; Target high-performing non-UK countries with localized shipping and marketing.
- Reduce Cancellations; Improve order confirmation and payment validation to reduce negative quantities and cancellations.