The digital fashion sector is dynamic, competitive, and multichannel. Online sales operations require agility in data analysis for decisions about products, prices, campaigns, and inventory, all in near real time. With this in mind, this dataset was developed to faithfully simulate the operation of a fictitious fashion store operating in several European countries, operating exclusively via E-commerce and Mobile App.

This dataset is completely fictitious, created for educational purposes by a curious data analyst, as part of a portfolio project that aims to reflect real challenges faced by data professionals in digital retail.

Data Structure

The dataset is composed of 7 relational tables, clean, consistent and interconnected. Below is a description of each one:

• customers: 1.000 rows, 4 columns
• sales: 905 rows, 7 columns
• sales_items: 2.253 rows, 13 columns
• products: 500 rows, 9 columns
• stock: 1.000 rows, 3 columns
• campaigns: 7 rows, 7 columns
• channels: 2 rows, 2 columns

Ideas about what you can do with these datasets

• Create interactive sales dashboards by country, channel, category and brand
• Calculate metrics such as average ticket, profit margin and product turnover
• Analyze the impact of promotional campaigns on volume and revenue
• Simulate stockouts and estimate financial losses
• Evaluate comparative performance between Mobile App and E-commerce
• Identify products with high turnover or stagnant stock by country
• Perform customer segmentation by purchasing behavior
• Analyze churn: identify inactive customers based on their last purchase
• Apply RFM Analysis to prioritize customers with the greatest potential value
• Build a data pipeline (ETL/ELT) for ingestion, transformation and visualization

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Concerns about generative AI in e-commerce are growing, with three out of four consumers familiar with the use of generative AI for online shopping expressing concerns about bias in these models leading to embarrassing results. Other key concerns included the impersonation of individuals to provide false testimonials or reviews, and the potential use of deep fakes to create content, among other issues. Consumer perceptions and usage of AI in online shopping While consumers express concerns, many are still engaging with AI-powered features in e-commerce. In 2024, 43 percent of consumers familiar with generative AI in online shopping used chatbots for queries. Interestingly, over 70 percent of Generation Z consumers showed interest in using AI while shopping, significantly higher than the global average of around 50 percent. This suggests a generational divide in AI acceptance that retailers will need to navigate. Retailer adoption and investment in AI technologies Retailers are increasingly investing in AI technologies to enhance various aspects of their operations. A 2025 survey revealed that 33 percent of retailers planned to invest in AI to support sales and marketing efforts, while 31 percent focused on product-related AI investments. The adoption of AI has already shown benefits, with nearly 70 percent of e-commerce companies reporting improved efficiency and speed of work due to generative AI in 2024. Additionally, about 50 percent of these businesses managed to reduce operational costs through AI-powered tools.

By 2027, most investment in AI-powered technologies will involve demand forecasting and spend analysis in procurement departments. A survey indicated that about 40 percent of B2B professionals envisaged investments in the two adoption areas. The same percentage of respondents planned to adopt AI tools to automate procurement processes.