According to a survey conducted in May 2021, more than half of consumers in the older age groups (** and over) in the United States preferred big box/department stores and pharmacy/convenience stores for their retail purchases compared to consumers in the younger age groups. Online marketplaces were popular across both younger and older consumers. Over ********* of respondents in the age groups 18-34 and 35-54 stated to have used online marketplaces such as Amazon and Etsy in the past three months. This rate was even higher with those aged over ** (at ** percent).

Concerning the five age groups, the group of 25-34 years has the largest share with **** percent. Contrastingly, the group of 18-24 years is ranked last, with **** percent. Their difference, compared to the 25-34 years, lies at *** percentage points. Find other insights concerning similar markets and segments, such as a ranking of subsegments in Russia regarding share in the segment Electronics and a ranking of subsegments in Russia regarding share in the e-commerce market as a whole.

Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Joseph Nowicki.

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

This dataset contains 150,000 retail interaction records representing customer journeys in both e-commerce and in-store environments. It captures detailed behavioral, demographic, and product-related information to support research in product sales history, customer demographics, purchase patterns, personalized shopping experiences, customer behavior analysis, and predictive modeling.

Each row corresponds to a unique customer–product interaction, including session details, browsing or purchasing behavior, and applied discounts. The purchase column serves as the binary target variable (1 = purchased, 0 = not purchased), making the dataset suitable for various classification and recommendation tasks.

Key Features

Size: 150,000 rows × 19 columns

Target Column: purchase (binary: 1 = purchased, 0 = not purchased)

Data Types:

Categorical: User ID, product ID, interaction type, device type, product category, brand, location, gender

Numerical: Price, discount, age, loyalty score, previous purchase count, average purchase value

Temporal: Timestamp (to study trends and patterns)

Text: Search keywords

Behavioral Data: Interaction type (view, click, add to cart, purchase), purchase history statistics

Product Metadata: Category, brand, price, discount percentage

User Demographics: Age, gender, loyalty score

Applications:

Retail personalization

Purchase prediction

Customer segmentation

Behavioral pattern analysis

People aged 25-34 visited the selected online stores more than any other age group in August 2025. Around **** percent of the amazon.com visitor base fell within this age range, while only *** percent were 65 years or older. In the same month, approximately ***** percent of ebay.com visitors were between the ages of 25 and 34.

Dataset Descriptions This analysis involves three main datasets—Sales Data, Customer Data, and Shopping Mall Data—which provide information on transactions, customer demographics, and shopping mall characteristics. Each dataset contributes unique aspects that, when combined, offer valuable insights into sales patterns, customer behavior, and the impact of mall features on sales.

Sales Data: This dataset records transaction-level details for products sold across shopping malls. Key columns include:

invoice_no: Unique identifier for each transaction. customer_id: Identifier for the customer making the purchase. category: Product category (e.g., Clothing, Shoes). quantity: Quantity of each product purchased. invoice date: Date of transaction. price: Price of each product purchased. shopping_mall: Mall where the transaction took place. Purpose: Analyzing this dataset allows us to understand product sales across different malls and track how sales change over time or by category.

Customer Data: This dataset provides demographic details for each customer, including:

customer_id: Unique identifier for each customer. gender: Customer’s gender. age: Customer’s age. payment_method: Preferred payment method for transactions. Purpose: This dataset supports customer segmentation by demographics, such as age and gender, and helps identify spending patterns and payment preferences.

Shopping Mall Data: This dataset contains details of various shopping malls in California where the transactions occur. The columns include:

shopping_mall: Name of the mall. construction_year: Year the mall was established. area_sqm: Total area of the mall in square meters. location: City in California where the mall is located. stores_count: Number of stores within the mall. Purpose: This dataset provides context on mall attributes and enables analysis of how mall features—such as size, store count, and location—might influence customer traffic, sales, and purchasing behaviors.

Goal of Analysis The goal of analyzing this data is to uncover patterns and insights that can inform decisions for optimizing sales strategies, enhancing customer engagement, and understanding the effects of mall characteristics on customer behavior. By exploring connections among sales performance, customer demographics, and mall attributes, this analysis seeks to answer questions like:

Which mall characteristics (e.g., size, age, store count) are most strongly associated with higher sales volumes? How do customer demographics, such as age and gender, impact spending patterns across malls? What product categories are more popular in specific malls, and how does this vary with mall characteristics?

Expected Outcomes With this analysis, we aim to develop actionable insights into the sales dynamics in California's shopping malls, identify customer preferences by mall characteristics, and understand how mall attributes drive retail success. These insights can be valuable for mall operators, retailers, and marketing teams looking to improve customer experience, tailor product offerings, and maximize sales performance across different mall locations.

Customer ID: A unique identifier assigned to each customer for tracking and analysis purposes.

Gender: The gender of the customer, which can be categorized as male, female, or non-binary. Understanding the gender distribution of customers can help retailers tailor their marketing strategies and product offerings to different demographic segments.

Income: The income level of the customer, typically categorized into income brackets or ranges. Income data provides insights into the purchasing power of different customer segments and helps retailers determine pricing strategies and product affordability.

Spending Score: A numerical score assigned to each customer based on their spending behavior, often calculated using factors such as purchase frequency, average transaction value, and total expenditure. Spending scores help retailers identify high-value customers who contribute significantly to sales and profitability.

Age: The age of the customer, usually categorized into age groups or ranges. Age data is essential for understanding the demographic composition of the customer base and tailoring marketing messages and product offerings to different age segments. Additionally, age information can inform decisions related to product design, packaging, and store layout to appeal to specific age demographics.

Welcome to the Retail Sales and Customer Demographics Dataset! This synthetic dataset has been meticulously crafted to simulate a dynamic retail environment, providing an ideal playground for those eager to sharpen their data analysis skills through exploratory data analysis (EDA). With a focus on retail sales and customer characteristics, this dataset invites you to unravel intricate patterns, draw insights, and gain a deeper understanding of customer behavior.

****Dataset Overview:**

This dataset is a snapshot of a fictional retail landscape, capturing essential attributes that drive retail operations and customer interactions. It includes key details such as Transaction ID, Date, Customer ID, Gender, Age, Product Category, Quantity, Price per Unit, and Total Amount. These attributes enable a multifaceted exploration of sales trends, demographic influences, and purchasing behaviors.

Why Explore This Dataset?

• Realistic Representation: Though synthetic, the dataset mirrors real-world retail scenarios, allowing you to practice analysis within a familiar context.
• Diverse Insights: From demographic insights to product preferences, the dataset offers a broad spectrum of factors to investigate.
• Hypothesis Generation: As you perform EDA, you'll have the chance to formulate hypotheses that can guide further analysis and experimentation.
• Applied Learning: Uncover actionable insights that retailers could use to enhance their strategies and customer experiences.

Questions to Explore:

• How does customer age and gender influence their purchasing behavior?
• Are there discernible patterns in sales across different time periods?
• Which product categories hold the highest appeal among customers?
• What are the relationships between age, spending, and product preferences?
• How do customers adapt their shopping habits during seasonal trends?
• Are there distinct purchasing behaviors based on the number of items bought per transaction?
• What insights can be gleaned from the distribution of product prices within each category?

Your EDA Journey:

Prepare to immerse yourself in a world of data-driven exploration. Through data visualization, statistical analysis, and correlation examination, you'll uncover the nuances that define retail operations and customer dynamics. EDA isn't just about numbers—it's about storytelling with data and extracting meaningful insights that can influence strategic decisions.

Embrace the Retail Sales and Customer Demographics Dataset as your canvas for discovery. As you traverse the landscape of this synthetic retail environment, you'll refine your analytical skills, pose intriguing questions, and contribute to the ever-evolving narrative of the retail industry. Happy exploring!

This statistic looks at which age demographic retailers aim for in the United Kingdom in 2016. Of the retailers surveyed ** percent focus on the 18 to 34 year age group compared to just *** percent of the over ** market.

The dataset comprises transactional information from previous 5 years from Walmart retail stores, with diverse details such as customer demographics, order specifics, product attributes, and sales logistics. It includes data on the city where purchases were made, customer age, names, and segments, along with any applied discounts and the quantity of products ordered. Each transaction is uniquely identified by an order ID, accompanied by order date, priority, and shipping details like mode, cost, and dates. Product-related information encompasses base margins, categories, containers, names, and sub-categories, enabling insights into profitability, sales, and regional performance. The dataset also provides granular details such as profit margins, unit prices, and ZIP codes, facilitating analysis at multiple levels like customer behavior, product performance, and operational efficiencies within Walmart's retail ecosystem.

The columns in dataset are:

1. City: The city where the purchase was made.
2. Customer Age: Age of the customer making the purchase.
3. Customer Name: Name of the customer.
4. Customer Segment: Segment to which the customer belongs (like retail, wholesale, etc.).
5. Discount: Any discount applied to the purchase.
6. Number of Records: The count of records for each transaction.
7. Order Date: Date when the order was placed.
8. Order ID: Unique identifier for each order.
9. Order Priority: Priority level of the order (like high, medium, low).
10. Order Quantity: Quantity of products ordered.
11. Product Base Margin: Base margin percentage for the product.
12. Product Category: Category to which the product belongs (like electronics, groceries, etc.).
13. Product Container: Container type of the product.
14. Product Name: Name of the product.
15. Product Sub-Category: Sub-category to which the product belongs.
16. Profit: Profit earned from the transaction.
17. Region: Region where the purchase was made.
18. Row ID: Unique identifier for each row.
19. Sales: Total sales amount.
20. Ship Date: Date when the order was shipped.
21. Ship Mode: Mode of shipping (like standard, express, etc.).
22. Shipping Cost: Cost associated with shipping.
23. State: State where the purchase was made.
24. Unit Price: Price per unit of the product.
25. Zip Code: ZIP code of the customer or store location.

This dataset compares the age distribution of UK convenience store shoppers with the national population profile in 2025. It highlights over- and under-representation of age groups within the convenience channel.

The 2018 edition of Woods and Poole Complete U.S. Database provides annual historical data from 1970 (some variables begin in 1990) and annual projections to 2050 of population by race, sex, and age, employment by industry, earnings of employees by industry, personal income by source, households by income bracket and retail sales by kind of business. The Complete U.S. Database contains annual data for all economic and demographic variables for all geographic areas in the Woods & Poole database (the U.S. total, and all regions, states, counties, and CBSAs). The Complete U.S. Database has following components: Demographic & Economic Desktop Data Files: There are 122 files covering demographic and economic data. The first 31 files (WP001.csv – WP031.csv) cover demographic data. The remaining files (WP032.csv – WP122.csv) cover economic data. Demographic DDFs: Provide population data for the U.S., regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), and counties. Each variable is in a separate .csv file. Variables: Total Population Population Age (breakdown: 0-4, 5-9, 10-15 etc. all the way to 85 & over) Median Age of Population White Population Population Native American Population Asian & Pacific Islander Population Hispanic Population, any Race Total Population Age (breakdown: 0-17, 15-17, 18-24, 65 & over) Male Population Female Population Economic DDFs: The other files (WP032.csv – WP122.csv) provide employment and income data on: Total Employment (by industry) Total Earnings of Employees (by industry) Total Personal Income (by source) Household income (by brackets) Total Retail & Food Services Sales ( by industry) Net Earnings Gross Regional Product Retail Sales per Household Economic & Demographic Flat File: A single file for total number of people by single year of age (from 0 to 85 and over), race, and gender. It covers all U.S., regions, states, CSAs, MSAs and counties. Years of coverage: 1990 - 2050 Single Year of Age by Race and Gender: Separate files for number of people by single year of age (from 0 years to 85 years and over), race (White, Black, Native American, Asian American & Pacific Islander and Hispanic) and gender. Years of coverage: 1990 through 2050. DATA AVAILABLE FOR 1970-2019; FORECASTS THROUGH 2050