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.

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.

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.

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.

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

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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.

Shopper Demographics Analytics Market Outlook

As per our latest research, the global shopper demographics analytics market size in 2024 is valued at USD 5.3 billion, with a robust CAGR of 14.7% projected through the forecast period. By 2033, the market is expected to reach USD 17.2 billion, reflecting the accelerating adoption of advanced analytics solutions in retail and related sectors. The primary growth driver is the increasing need for retailers and brands to understand and predict consumer behavior in an era characterized by omnichannel shopping and intense competition.

The growth of the shopper demographics analytics market is significantly propelled by the retail sector’s digital transformation. Retailers are increasingly leveraging analytics to gain granular insights into customer demographics, preferences, and purchasing behavior. The integration of artificial intelligence (AI) and machine learning (ML) into analytics platforms has enabled businesses to process vast amounts of data in real time, offering actionable insights that drive personalized marketing and operational efficiency. As consumer expectations for tailored experiences continue to rise, retailers are investing heavily in shopper analytics to enhance customer engagement, improve inventory management, and optimize store layouts, further fueling market expansion.

Another key growth factor is the proliferation of e-commerce and the corresponding surge in online data generation. E-commerce platforms are uniquely positioned to collect detailed demographic and behavioral data, which can be analyzed to segment customers, predict purchasing trends, and personalize marketing campaigns. The adoption of cloud-based analytics solutions has further democratized access to advanced analytics, allowing even small and medium-sized enterprises (SMEs) to harness the power of shopper demographics analytics. Moreover, the integration of analytics with customer relationship management (CRM) and point-of-sale (POS) systems has streamlined data collection and analysis, enabling businesses to respond swiftly to changing consumer preferences.

The increasing focus on omnichannel retail strategies is also driving demand for shopper demographics analytics. Retailers are striving to provide a seamless shopping experience across physical stores, online platforms, and mobile applications. Analytics solutions help bridge the gap between different channels by offering a unified view of customer behavior, enabling businesses to deliver consistent and personalized experiences. The rise of smart stores and the deployment of Internet of Things (IoT) devices have further enriched the data ecosystem, providing real-time insights into foot traffic, dwell times, and purchase patterns. These advancements are expected to sustain the market’s high growth trajectory over the coming years.

From a regional perspective, North America currently dominates the shopper demographics analytics market, owing to the presence of major technology providers and early adoption by leading retailers. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding retail infrastructure, and increasing digital adoption among consumers. Europe also holds a significant market share, supported by strong regulatory frameworks and a mature retail sector. The Middle East & Africa and Latin America are emerging as promising markets, as retailers in these regions invest in analytics to stay competitive and cater to evolving consumer demands. These regional dynamics underscore the global relevance and growth potential of shopper demographics analytics.

Component Analysis

The shopper demographics analytics market by component is bifurcated into software and services, with software solutions representing the larger share in 2024. The software segment encompasses a wide range of analytics platforms, including proprietary and open-source solutions designed to collect, process, and visualize demographic data. These platforms leverage advanced technologies such as AI, ML, and big data analytics to deliver actionable insights in real time. The growing adoption of cloud-based analytics software has further accelerated market growth, enabling retailers to scale their analytics capabilities without significant upfront investment in IT infrastructure. The continuous evolution of analytics software, with features such as predictive modeling, data v

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.

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.

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.

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.

The Online Shopping Behavior Dataset contains records of 999 individuals, providing insights into their purchasing habits, spending patterns, and platform preferences. It includes demographic details such as age group (19-30, 31-50) and gender (Male, Female), along with the preferred e-commerce platform (Amazon, Flipkart, Myntra, etc.). The dataset also captures average monthly spending (INR), categorized as "1000-5000," "5000-10000," or "10000+," as well as the device used for shopping (Laptop, Tablet, etc.). Additionally, it records payment methods (UPI, Cash on Delivery, etc.), purchase frequency (Daily, Weekly, Monthly), and the return rate (%) of purchases. A key feature of this dataset is the most purchased category, which highlights the type of products consumers buy most frequently, such as Electronics, Clothing, or Groceries. This dataset is valuable for businesses looking to analyze consumer behavior, optimize marketing strategies, and enhance customer engagement. Researchers and data analysts can use it for trend analysis, customer segmentation, and predictive modeling, making it an excellent resource for e-commerce analytics and decision-making.

Title: “Survey on Online Shopping Preferences”

Dataset Description: This dataset contains responses from an online survey conducted to understand consumer preferences and behavior when shopping online across various product categories. The survey covered topics such as the frequency of online shopping, age demographics, platform preferences for electronics, fashion, beauty products, groceries, and household essentials, as well as factors influencing platform choice, trust in product reviews, and the perceived quality of return and refund policies. The responses were collected from a diverse group of individuals, making this dataset suitable for analyzing trends in e-commerce behavior. Context: The dataset was compiled to gain insights into the changing dynamics of online shopping, especially in an era where digital platforms dominate retail. Understanding consumer preferences can help businesses tailor their offerings to better meet customer needs.

Content: The dataset consists of the following columns:  Timestamp: The date and time the response was submitted.  How often do you shop online?: Frequency of online shopping (e.g., weekly, monthly).  What is your age group?: Age range of the respondent.  Which platform do you prefer for buying electronics?: Respondent’s preferred platform for purchasing electronics.  Where do you usually shop for fashion and apparel?: Preferred platform for fashion items.  Which platform do you prefer for beauty and skincare products?: Platform choice for beauty and skincare products.  Where do you typically buy groceries and household essentials?: The go-to platform for groceries and essentials.  What is the most important factor for you when choosing a platform to shop from?: Key consideration when selecting a shopping platform (e.g., price, product quality, platform security).  Do you trust the product reviews and ratings on these platforms?: Level of trust in reviews and ratings (e.g., fully trust, somewhat trust, neutral).  Which platform’s return and refund policy do you find the best?: Respondent’s opinion on the platform with the best return and refund policy.

Use Cases: This dataset can be used for a variety of research and analytical purposes, including:  Studying consumer behavior trends in online shopping.  Analyzing platform preferences across different age groups.  Identifying key factors influencing online shopping decisions.  Exploring trust in product reviews and return policies on popular e-commerce platforms.

Acknowledgments: This dataset was compiled through a survey done using google form. Special thanks to all the respondents for their time and valuable insights.

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 ...

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.

Online shopping has cemented its place in the retail market, buoyed by rising adoption and better technology. 2024 data shows 9.8 million households shopping online, up from 8.2 million in 2019, a clear sign of growing penetration. This performance has benefited from safer payments, easier returns and smoother mobile access, while new competitors like Shein and Temu push prices down and keep pressure on margins. Augmented reality, chat-enabled service and social shopping are blurring the lines between instore and online, letting shoppers try before they buy and discover products through feeds on Instagram, YouTube and TikTok. In this environment, faster broadband and the rollout of 5G coverage are expanding the audience, enabling more impulse buys and seamless checkouts. Over the past five years, the online market’s growth has wavered with the pandemic, then settled into a more price-aware rhythm. The 'search and compare' habit means shoppers cut back when discretionary income tightens and 62% switched brands in 2024 to save money. The share of weekly online shoppers rose from 27% in 2021 to 29% in 2025, with a similar increase in the number of consumers shopping every two to three weeks. (26% in 2021 to 30% in 2025). Profitability lagged early on due to fierce competition and high fixed costs, but retailers trimmed overheads, modernised fulfilment networks and used social content to sustain margins. The market also saw international entrants intensify competition, contributing to the demise of some domestic platforms. Industry revenue is anticipated to grow at an annualised 3.4% over the five years through 2025-26 and is expected to total $64.9 billion in the current year, when revenue will climb by an estimated 6.8%. Going forwards, online sales should keep climbing thanks to broader product ranges, better mobile experiences and pay-later options that streamline purchases. AR-enabled sizing and virtual try-ons will reduce friction in fashion and accessories, while loyalty schemes and free shipping will reward repeat customers. Profit is set to climb as pricing becomes more responsive and import costs ease from a stronger Australian dollar. With omnichannel strategies, showrooming and social commerce, the line between online and offline will stay blurred and hybrid stores will become mainstream rather than niche. Overall, industry revenue is forecast to climb at an annualised 5.9% over the five years through 2030-31 to total $86.6 billion.

Survey on Equipment and Use of Information and Communication Technologies in Households: Persons who have used the Internet at least once by demographic characteristics and knowledge of the online shopping consumer rights in the EU. National.

Context:

This dataset, which was gathered via an online retail platform, offers comprehensive information about consumer purchasing habits. It records different facets of consumer behavior, such as transactional patterns, buying preferences, and demographic information. The dataset is intended to assist in the analysis of purchasing trends, the assessment of the impact of variables like seasons, payment methods, and discounts on decisions to buy, and the comprehension of the relationship between consumer qualities and product choices and spending patterns.

Content:

Each customer's personal information (age, gender, location), purchase details (item purchased, category, price, color, and size), and transactional aspects (discounts, shipping type, payment method, and use of promo codes) are among the many attributes included in the dataset. Behavioral data like subscription status, past purchases, and frequency of transactions are also included, as well feedback measures like review scores. All things considered, the dataset offers a thorough understanding of consumer interactions and preferences in an online shopping setting.

In 2024, convenience was the leading reason to spend more money online during Cyber Week than in the previous year. Prices being lower online was the second most common reason for U.S. Cyber Week shoppers.