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.

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.

For 2024's Black Friday and Cyber Monday sales event, also known as the 'Cyber Week', approximately 77 percent of shoppers in the United States that planned to visit online retailers during Cyber Week specifically intended to buy clothing and accessories, making it the most popular product category. Just over 70 percent of respondents also planned to buy electronics.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

In 2020, a total of over ** percent of consumers across the globe shopped online: reaching nearly ** percent each, the leading regions that year were South America and Asia. North America had the lowest share with just over ***** in **** consumers buying items on the internet. The online store that was used most frequently by shoppers worldwide was Amazon.com.

Favorite online stores in the U.S. As of November 2020, an estimated ** percent of U.S. consumers stated that their online shop of choice was Amazon, making it by far the favorite e-commerce shop among online shoppers. With less than ** percent, Walmart’s web shop ranked second. Both male and female consumers in the country had a clear preference for Amazon, however, certain online stores were more popular among specific genders. For instance, more men liked visiting eBay, while a higher percentage of women had a preference for Target.

Why do consumers like Amazon? There were various reasons why U.S. shoppers used Amazon to buy products in 2020, the leading reason being the fast and free shipping services provided. Other key factors consumers mentioned, included Amazon’s broad selection, the easy return process, and the platform having some of the lowest prices.

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.

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.