About

This data is from E-Commerce. I used postgreSQL for data cleaning. I transformed NULL values to 'Not defined' and orginal data have only category name column(which was 'category_code') and that was 'DOT' seperated value which show us the products class from wide to specific. So I split them with delimeter('.').

The orignal data have record with 5 months but I only used December of 2019. If you want more data you can visit the link above and use.

File structure

Acknowledgements

Many thanks Thanks to REES46 Marketing Platform for this dataset and Michael Kechinov

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform and Origin data provider

Inspiration

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In Hungary, the share of online shoppers grew significantly between 2010 and 2024. The number of e-commerce users in the country shot up in 2020 due to the coronavirus (COVID-19) pandemic, and the share of online shoppers in the total population reached over ** percent by 2024. In comparison, less than ** percent of Hungarians were online shoppers in 2019. How important are online sales for enterprises? Enterprises operating in Hungary source a considerable share of their revenue from online sales; however, this figure has declined over the past couple of years. In 2024, e-commerce sales accounted for ** percent of enterprises’ revenue, compared to ** percent recorded in 2019. When it comes to online sales channels’ share in Hungary’s total retail trade revenue, figures peaked in 2021 at over ** percent. By 2024, the e-commerce share in retail trade decreased to **** percent. Leading online marketplaces in Hungary In 2022, eMag was the leading online marketplace in Hungary based on the number of webshops using the platform. It was followed by Facebook Marketplace and Árukereső, with over ** percent of Hungarian online retailers using them. In the past decade, the Romanian-founded marketplace, eMag, has grown into a key player in the region, establishing a strong presence in Bulgaria and Poland as well.

In the United States, holiday season online retail sales grew by 8.6 percent in 2024 compared to the previous year. Forecasts suggested that this growth would decrease in 2025 to 5.3 percent. The new normal in holiday shopping In 2020, the COVID-19 pandemic prompted many U.S. consumers to do their holiday shopping online. A year later, although the situation once again allows for physical shopping, e-commerce is still gaining relevance. According to estimates, holiday season online retail sales in the United States were to reach new heights in 2024, amounting to 241 billion dollars. As in previous years, Cyber Monday and Black Friday would remain the most relevant holiday shopping days in 2025, expected to generate approximately 14 billion and 12 billion U.S. dollars in sales, respectively. A preference for online With Cyber Monday expected to generate 2.5 billion dollars more than Black Friday in 2025, it comes as no surprise that most holiday shoppers reported that their preferred type of retailer for holiday gifts were online-only retailers. Over six in ten consumers prefered to buy holiday gifts from e-commerce only merchants, while department stores only only preferred by about 24 percent of seasonal shoppers.

Online Retail Sales and Customer Data

Transactional Data with Product and Customer Details in Online Retail

By Marc Szafraniec [source]

About this dataset

The InvoiceNo column holds unique identifiers for each transaction conducted. This numerical code serves a twofold purpose: it facilitates effortless identification of individual sales or purchases while simultaneously enabling treasury management by offering a repository for record keeping.

In concordance with the invoice number is the InvoiceDate column. It provides a date-time stamp associated with every transaction, which can reveal patterns in purchasing behaviour over time and assists with record-keeping requirements.

The StockCode acts as an integral part of this dataset; it encompasses alphanumeric sequences allocated distinctively to every item in stock. Such a system aids unequivocally identifying individual products making inventory records seamless.

The Description field offers brief elucidations about each listed product, adding layers beyond just stock codes to aid potential customers' understanding of products better and make more informed choices.

Detailed logs concerning sold quantities come under the Quantity banner - it lists the units involved per transaction alongside aiding calculations regarding total costs incurred during each sale/purchase offering significant help tracking inventory levels based on products' outflow dynamics within given periods.

Retail isn't merely about what you sell but also at what price you sell- A point acknowledged via our inclusion of unit prices exerted on items sold within transactions inside our dataset's UnitPrice column which puts forth pertinent pricing details serving as pivotal factors driving metrics such as gross revenue calculation etc

Finally yet importantly is our dive into foreign waters - literally! With impressive international outreach we're looking into segmentation bases like geographical locations via documenting countries (under the name Country) where transactions are conducted & consumers reside extending opportunities for businesses to map their customer bases, track regional performance metrics, extend localization efforts and overall contributing to the formulation of efficient segmentation strategies.

All this invaluable information can be found in a sortable CSV file titled online_retail.csv. This dataset will prove incredibly advantageous for anyone interested in or researching online sales trends, developing customer profiles, or gaining insights into effective inventory management practices

How to use the dataset

Identifying Products: StockCode is the unique identifier for each product. You can use it to identify individual products, track their sales, or discover patterns related to specific items.

Assessing Sales Volume: Quantity column tells you about the number of units of a product involved in each transaction. Along with InvoiceNo, you can analyze overall sales volume or specific purchases throughout your selected period.

Observing Price Fluctuations: By using the UnitPrice, not only can the total cost per transaction be calculated (by multiplying with Quantity), but also insightful observations like price fluctuations over time or determining most profitable items could be derived.

Analyzing Description Patterns/Trends: The Description field sheds light upon what kind of products are being traded. This could provide some inspiration for text analysis like term frequency-inverse document frequency (TF-IDF), sentiment analysis on descriptions, etc., to figure out popular trends at given times.

Analysing Geographical Trends: With the help of Country column, geographical trends in sales volumes across different nations can easily be analyzed i.e., which location has more customers or which country orders more quantity or expensive units based on unit price and quantity columns respectively.

Keep in mind that proper extraction and transformation methodology should be applied while handling data from different columns as per their datatypes (textual/alphanumeric/numeric) requirements.

This dataset not only allows retailers to gain an immediate understanding into their operations but could also serve as a base dataset for those interested in machine learning regarding predicting future transactions

Research Ideas

• Inventory Management: By tracking the 'Quantity' and 'StockCode' over time, a business could use this data to notice if certain products are frequently purchased together or in specific seasons, allowing them to better stock their inventory.
• Pricing Strategy:...

Expected to reach ** billion U.S. dollars, Cyber Monday is the shopping day with the highest e-commerce sales revenue in the United States in 2023. Black Friday ranks second, with over **** billion dollars in online revenue according to the latest forecasts.

This dataset, identified by the series ID RSXFS, is sourced from the U.S. Census Bureau and is available through the Federal Reserve Economic Data (FRED) system of the St. Louis Fed. It provides a monthly measure of retail sales across the United States. The data represents the total value of sales at retail and food services stores, measured in millions of dollars and adjusted for seasonal variations. It is important to note that the most recent month's value is an advance estimate, which is subject to revision in subsequent months as more comprehensive data becomes available. As a key economic indicator, this series is widely used by economists and analysts to gauge consumer spending and assess the overall health of the U.S. economy.

Suggested Use Cases: - This dataset is highly valuable for economic analysis and can be used to: - Conduct time series analysis and modeling. - Track consumer spending patterns. - Forecast future retail sales. - Analyze the impact of economic events on the retail sector.

License The RSXFS dataset is sourced from the U.S. Census Bureau and is considered Public Domain: Citation Requested. This means the data is freely available for use, but you must cite the source and acknowledge that the data was obtained from FRED. If you plan on using any copyrighted series from other data providers on FRED for commercial purposes, you would need to contact the original data owner for permission.

Data Fields: The dataset primarily contains two columns: - observation_date: The date of the monthly data point, recorded as the first day of each month from January 1992 to July 2025. - RSXFS: The value of advance retail sales in millions of dollars.

Citation and Provenance:
Source: U.S. Census Bureau
Release: Advance Monthly Sales for Retail and Food Services
FRED Link: https://fred.stlouisfed.org/series/RSXFS
Citation: U.S. Census Bureau, Advance Retail Sales: Retail Trade [RSXFS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RSXFS, September 8, 2025.

In 2024, the in-store or brick-and-mortar retail channel was forecast to account for **** percent of total retail sales in the United States. By 2028, e-commerce is expected to make up ** percent of all retail sales.

Description:

• The dataset comprises anonymized data on online sales transactions, capturing various aspects of product purchases, customer details, and order characteristics.
• This dataset can be utilized for analyzing sales trends, customer purchase behavior, and order management in e-commerce or retail.
• It can aid in understanding the impact of discounts, payment methods, and shipment providers on sales performance and customer satisfaction.
• This dataset can be utilized for analyzing sales performance, customer purchasing patterns, and operational efficiency in order management.
• It can help in evaluating the effects of discounts and payment methods on sales, optimizing inventory by studying product demand, and improving customer satisfaction through better shipping and return handling.

Features:

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

Thailand E-Commerce Transactions: Value: E-Commerce & Shopping: E-Commerce & Shopping data was reported at 180.027 USD in 13 Mar 2025. This records a decrease from the previous number of 185.302 USD for 12 Mar 2025. Thailand E-Commerce Transactions: Value: E-Commerce & Shopping: E-Commerce & Shopping data is updated daily, averaging 30,542.278 USD from Dec 2018 (Median) to 13 Mar 2025, with 2118 observations. The data reached an all-time high of 660,820.207 USD in 13 Mar 2022 and a record low of 47.413 USD in 03 Apr 2021. Thailand E-Commerce Transactions: Value: E-Commerce & Shopping: E-Commerce & Shopping data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Thailand – Table TH.GI.EC: E-Commerce Transactions: by Category.

More than **** of consumers belonging to Generation Z bought something on social media platforms, according to a survey in 2024. Almost a ***** of overall consumers bought on social media platforms. The consumer experience In a 2023 survey, Facebook and Instagram were the social media platforms offering the best shopping experience. To gain deeper insights into the elements constituting a satisfactory social commerce shopping journey from the user's viewpoint, key factors shaping consumers' heightened engagement with social commerce included, but were not limited to, deals and discounts, seamless purchasing processes, exclusive offers, and increased availability of customer reviews. Social shopping destinations Facebook is the leading social commerce platform globally, except among Gen Z, who favor Instagram and TikTok. However, the types of social media accounts that shoppers followed and purchased from varied by age group. Gen Z and Millennials predominantly bought from brand accounts, with Gen Z also showing a preference for social media influencers. Conversely, Gen X and Boomers preferred purchasing from trusted retailer accounts.

To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

United States Retail Sales: Shoe Stores data was reported at 3.072 USD bn in May 2018. This records an increase from the previous number of 2.829 USD bn for Apr 2018. United States Retail Sales: Shoe Stores data is updated monthly, averaging 2.053 USD bn from Jan 1992 (Median) to May 2018, with 317 observations. The data reached an all-time high of 4.268 USD bn in Dec 2016 and a record low of 1.161 USD bn in Feb 1993. United States Retail Sales: Shoe Stores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H001: Retail Sales: By NAIC System.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

Retail Sales in Finland increased 0.40 percent in October of 2025 over the previous month. This dataset provides - Finland Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Comprehensive dataset of Black Friday 2024 spending patterns including average spend per shopper ($104), channel-specific spending (online $124, in-store $82), generational spending differences, payment method analysis, and five-year historical trends from 2020-2024 covering 152 million U.S. shoppers.

Sri Lanka E-Commerce Transactions: Value: E-Commerce & Shopping data was reported at 127.813 USD in 30 Apr 2024. This records an increase from the previous number of 87.196 USD for 26 Apr 2024. Sri Lanka E-Commerce Transactions: Value: E-Commerce & Shopping data is updated daily, averaging 174.380 USD from Dec 2018 (Median) to 30 Apr 2024, with 984 observations. The data reached an all-time high of 497,145.557 USD in 18 Sep 2023 and a record low of 0.318 USD in 10 Jan 2023. Sri Lanka E-Commerce Transactions: Value: E-Commerce & Shopping data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Sri Lanka – Table LK.GI.EC: E-Commerce Transactions: by Category.

Retail Trade, sales by industries based on North American Industry Classification System (NAICS), monthly.

The pie chart showcases the distribution of app/software spending by store category in Israel, providing insights into how eCommerce stores allocate their resources on the app or software they utilize. Among the store categories, Apparel exhibits the highest spending, with a total expenditure of $1.97M units representing 22.04% of the overall spending. Following closely behind is Beauty & Fitness with a spend of $483.16K units, comprising 5.41% of the total. Home & Garden also contributes significantly with a spend of $403.13K units, accounting for 4.52% of the overall app/software spending. This data sheds light on the investment patterns of eCommerce stores within each category, reflecting their priorities and resource allocation towards app or software solutions.

Asos E-Commerce Dataset - 30,845 products, text classification dataset

Using web scraping, we collected information on over 30,845 clothing items from the Asos website. The dataset can be applied in E-commerce analytics in the fashion industry. The dataset is similar to SheIn E-Commerce Dataset.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Dataset Info

For each item, we extracted:

• url - link to the item on the website
• name - item's name
• size - sizes available on the website
• category - product's category
• price - item's price
• color - item's color
• SKU - unique identifier of the item
• date - date of web scraping; for all items - March 11, 2023
• description - additional description, including product's brand, composition, and care instructions, in JSON format
• images - photographs from the item description

🧩 This is just an example of the data. Leave a request here to learn more

🚀 You can learn more about our high-quality unique datasets here

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