The number of users in the e-commerce market in the United States was modeled to stand at ************** users in 2024. Following a continuous upward trend, the number of users has risen by ************* users since 2017. Between 2024 and 2029, the number of users will rise by ************* users, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

Online purchases in the non-grocery retail category were more prevalent among online shoppers in Australia than in the groceries category in the 12 months to July 2024, according to a 2024 survey. Those aged between 18 and 49 years old were the key demographic for online non-grocery retail product purchases, with over ** percent of respondents across this demographic purchasing non-grocery products online every month during the survey period. In the groceries category, 30 to 39-year-olds were the leading age group buying groceries online at around ** percent of respondents.

Introduction

Online Shopping Statistics: Online shopping has revolutionized the retail industry, providing consumers with unparalleled convenience, a wide range of products, and easy access to services. Factors such as greater internet accessibility, the rise of mobile commerce, and shifting consumer preferences have contributed to the substantial growth of the e-commerce market.

Online shopping statistics offer key insights into market trends, consumer habits, demographic shifts, popular product categories, and the technologies driving the future of retail. Understanding these insights is essential for both businesses and consumers to successfully navigate the competitive online marketplace and keep up with emerging trends in digital shopping.

Dataset: Utilizing the Online Shoppers Purchasing Intention Dataset, which contains a comprehensive set of features extracted from online shopping sessions, including visitor demographics, session duration, pageviews, and more.

According to a 2022 global survey, millennials were the generation that made the highest number of direct purchases on online marketplaces. Over the six months leading up to the study, around ** percent of millennials bought from marketplaces. Gen Z online shoppers secured the second position, with ** percent of them opting to order goods directly from this channel.

According to data from an April 2021 survey of Canadian online shoppers, ** percent of online shoppers from the country were Millennials from ages 27 to 40. The second-largest group were Boomers from 65 to 75 years old, who made up ** percent of overall online shoppers in the country.

Percentage of individuals who shopped online and percentage of online shoppers by type of good and service purchased over the Internet during the past 12 months.

We monitor millions of online stores across 200+ countries, ensuring that this report provides accurate and up-to-date information. This report diverse eCommerce ecosystems in various countries/regions, including market penetration, regional preferences, consumer trends, and technological investments. Stay up-to-date with the latest data and gain a comprehensive understanding of the eCommerce market dynamics on a country/region level, enabling informed business decisions and strategic planning.

In March of 2023, the largest share of e-commerce shoppers in the United States consisted of adults aged 18 to 24 (**** percent), based on geolocated mobile user data. Adults between the ages of 25 and 34 made up almost ** percent of the e-commerce shopper mobile audience in the country.

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

Discover the latest eCommerce statistics in Philippines for 2025, including store count by category and platform, estimated sales amount by platform and category, products sold by platform and category, and total app spend by platform and category. Gain valuable insights into the retail landscape in Philippines, uncovering the distribution of stores across categories and platforms.

In 2024, around ** percent of 25- to 45-year-olds in Germany had ordered and purchased products online in the past three months. They were also the largest age group of online consumers. 65- to 75-year-olds were the group that shopped online the least. Where are people shopping online? Some of the most visited fashion websites in Germany included zalando.de, hm.com, and vinted.de. Zalando is especially popular because it sells items from multiple different brands, allowing the consumer to find all different types of clothes, shoes, accessories, and more in one place. Another advantage of shopping online is that consumers are not just limited to shopping in the country in which they live. Products can be ordered from almost anywhere in the world (if, of course, consumers are willing to pay a little extra). If a better deal is available elsewhere or a product is not available anywhere in the home country, then this can be a good reason to make a purchase on a foreign website. Challenges of online shopping Online shopping, however, is not without its challenges. Retailers themselves continued to be worried about challenges facing e-commerce. These included customer reluctance to buy due to higher prices, as well as competitive pressure from other businesses and supply bottlenecks. Most customers preferred to return products they did not want to keep via an online self-service, which means declaring a return online and then dropping off the package, e.g. at the post, a return point located in another establishment or a package pick-up station. While the return option is an integral part of online shopping, it brings with it a multitude of issues. These include putting a significant strain on the environment, transportation and logistics, as well as staff involved.

The eCommerce industry develops at different stages in various regions. Among the platforms we monitor, United States stands out with the highest number of online stores, indicating the prosperity of its eCommerce economy. Additionally, both United Kingdom and Brazil have a strong presence of online shops, accounting for 6.10% and 4.87% of the global online store market.

Online store owners across different regions have varying preferences when investing in apps. Our data reveals that United States is the leading destination for online businesses using apps, spending an impressive $1.58B per month. Online business owners in United Kingdom and Canada are also passionate about leveraging apps, with monthly app expenditure of $267.55M and $204.96M respectively. Additionally, Australia and Germany contribute significantly as well, representing a combined 8.41% of global monthly app spending.

In India, the distribution of stores across different platforms presents a dynamic picture of the market. WooCommerce, as a leading platform, hosts 120.58K stores, accounting for 45.14% of the total store count in the region. This is closely followed by Shopify, which supports 76.43K stores, representing 28.61% of the region's total. Custom Cart makes a significant contribution with 33.56K stores, or 12.56% of the total. The chart underscores the diversity and preferences of store owners in India regarding their choice of platform.

In India, the estimated sales amount across various store categories provides key insights into the market's dynamics. Gifts & Special Events, as a prominent category, generates significant sales, totaling $745.33B, which is 67.07% of the region's total sales in this sector. Home & Garden follows with robust sales figures, achieving $210.60B in sales and comprising 18.95% of the region's total. Beauty & Fitness contributes a considerable amount to the regional market, with sales of $66.49B, accounting for 5.98% of the total sales in India. This breakdown highlights the varying economic impacts of different categories within the region, showcasing the diversity and strengths of each sector.

This chart illustrates the estimated sales amounts generated by stores on various platforms within India. Magento shows a significant lead, with total sales amounting to $746.53B, which constitutes 67.17% of the region's total sales on platforms. Custom Cart reports sales of $275.49B, accounting for 24.79% of the total platform sales in India. Salesforce Commerce Cloud also holds a notable share, with its sales reaching $47.72B, representing 4.29% of the overall sales amount. This data provides a comprehensive view of the market dynamics in India, highlighting which platforms are driving the most sales.

Retail eCommerce sales surpassing a staggering $5.7 trillion globally in last year. This upward trajectory shows no signs of plateauing. As we unpack the latest statistics, from the dominance of platforms like WooCommerce and Shopify to the regional powerhouses of the United States and beyond, a picture emerges of a sector in constant evolution. This article dives into the heart of these statistics, offering a panoramic view of the eCommerce landscape today. We explore the dynamics of platform preference, regional market trends, and category-specific insights, providing a comprehensive snapshot of an industry that continues to reshape global retail.

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 chart provides a detailed overview of the number of India online retailers by Monthly Visitors. Most India stores' Monthly Visitors are Less than 100, there are 89.56K stores, which is 68.08% of total. In second place, 25.6K stores' Monthly Visitors are 100 to 1K, which is 19.46% of total. Meanwhile, 12.12K stores' Monthly Visitors are 1K to 10K, which is 9.22% of total. This breakdown reveals insights into India stores distribution, providing a comprehensive picture of the performance and efficient of online retailer.