In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

In 2022, online retail accounted for *** percent of the total retail purchases made in South Africa, reporting ** billion South African rand (around **** billion U.S dollars). This presents an increase of almost ** percent when compared to the previous year. This is due to the change in consumers attitudes that was expedited largely by the COVID-19 pandemic.

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

When asked about "Attitudes towards online shopping", most Emirati respondents pick "Customer reviews on the internet are very helpful" as an answer. 62 percent did so in our online survey in 2024.The Global survey is part of Statista Consumer Insights, providing you with exclusive consumer survey results on more than 500 industries and topics.

1) Data Introduction • The Shopper's Behavior and Revenue Dataset contains more than 12,300 pieces of information about online shopping customers' purchasing behavior and revenue, including customer purchasing patterns, product reviews, discounts, and payment methods.

2) Data Utilization (1) Shopper's Behavior and Revenue Dataset has characteristics that: • This dataset includes a variety of variables related to your shopping behavior, including demographics, purchase history, products and categories, purchase frequency, review ratings, discounts, and promotion usage. • Provides information that can analyze e-commerce customer behavior from multiple angles, such as whether to purchase (Revenue), visitor type, traffic type, browser, operating system, region, and weekend visitation. (2) Shopper's Behavior and Revenue Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze customer behavior patterns and characteristics to establish customized marketing strategies, and use them to request reviews and induce repurchases. • Forecast and Sales Analysis: By analyzing purchase conversion rate, review impact, discount effect, etc., you can contribute to increased sales and improved customer satisfaction.

This AI-Driven Consumer Behavior Dataset captures key aspects of online shopping behavior, including purchase decisions, browsing activity, customer reviews, and demographic details. The dataset is designed for research in consumer behavior analysis, AI-driven recommendation systems, and digital marketing optimization.

Key Features: ✔ Consumer Purchase Data – Tracks product purchases, prices, discounts, and payment methods. ✔ Clickstream Data – Includes browsing behavior, pages visited, session duration, and cart abandonment. ✔ Customer Reviews & Sentiments – Provides ratings, textual reviews, and sentiment analysis scores. ✔ Demographic Information – Includes age, gender, location, and income levels. ✔ Target Column (purchase_decision) – Indicates whether a customer completed a purchase (1) or not (0).

The E-Commerce Trends and Insights dataset provides detailed information on global online shopping behavior, sales data, consumer preferences, marketing impact, and cross-border trade. It is designed to help brands and manufacturers analyze performance, optimize marketing, and expand globally.

1) Data Introduction • The Clickstream Data for Online Shopping is an e-commerce analysis dataset that summarizes user clickstream, product information, country, price, and other session-specific behavior data from April to August 2008 at an online shopping mall specializing in maternity clothing.

2) Data Utilization (1) Clickstream Data for Online Shopping has characteristics that: • Each row contains 14 key variables: year, month, day, click order, country (by access IP), session ID, main category, product code, color, photo location, model photo type, price, category average price, page number, etc. • Data is configured to enable analysis of various consumer behaviors such as click flows for each session, product attributes, and country-specific access patterns. (2) Clickstream Data for Online Shopping can be used to: • Online Shopping Mall User Behavior Analysis: Using clickstream, session, and product information, you can analyze purchase conversion routes, popular products, and behavioral patterns by country and category. • Improve marketing strategies and UI/UX: analyze the relationship between product photo location, color, price, etc. and click behavior and apply to establish effective marketing strategies and improvement of shopping mall UI/UX.

A 2023 study on generational trends in consumer behavior found that Gen Z consumers in the United States were more likely to purchase used products in response to inflation than other generations and the least likely to buy items on sale. On the other hand, Boomers are the least likely to buy used products as a response to inflation and the most likely to acquire them on sale.

In 2020, about nine out of ten Costa Rican consumers reported purchasing something online in the previous 12 months. Of these, ** percent stated using e-commerce to save time and avoid crowds. Before making their purchase, nearly half (** percent) of web shoppers said they reviewed products on Google. Furthermore, social networks have gained prominence as an online shopping channel, with ** percent of Costa Rican consumers saying they made their last purchase on Instagram.

While the penetration of the Internet in consumers’ daily lives still continue with an increasing momentum, this new medium has experienced a radical transformation from being a communication tool to be an economic platform where consumers do not only communicate but also transact. In this perspective, online consumer behavior became an important area for both academics and professionals, which needs to be investigated and explored. As the Internet is a new technological channel for shopping, consumers need first to decide to use this new channel for shopping and then make their online retailer preference. Thus, online consumer behavior involves a two-step process composed of intention to shop online and selection of e-store. The purpose of this study is to develop and test a two steps online consumer behavior model which explains the dynamics of the intention and selection processes. A two staged research design has been implemented in the study. At the first stage, parallel to the existing literature, the effects of risk perceptions, technology acceptance factors, and benefit perceptions on the intention to shop online has been measured. The effect of retailer brand equity on e-store selection process has been measured on the second stage. The research hypotheses have been developed based on both the existing theoretical ground and current findings in the literature. The results of the study confirmed that risk perceptions, technology acceptance factors and benefit perceptions regarding online shopping play a decisive role in the intention of consumers to shop online. A second important finding of the study is that once consumers’ involve into online shopping activity, the strength of retailers’ brand equity directly affects the consumers’ store preference.

The dataset is from a B2C e-commerce platform in China, with massive product negative reviews of four representative sectors including Computers, Phone&Accessories, Gifts&Flowers and Clothing.Here the negative reviews are defined as the reviews with scores 1. After the raw data was collected, deduplication, user anonymization & categorization and text classification was employed to process the raw data. The data contains fields of id for comment, anonymous id for user, review text, timestamp of the posting, negative reason label and user level.

The dataset contains four JSON files, with each file titled by the corresponding sector name.In each JSON file, each line represents a record of a negative review from this sector, in which the filed ‘id’ is the unique code we created for reviews, the filed ‘userID’ is the unique code we created for users, the field ‘userLevel’ is the user’s level in the platform, the field ‘creationTime’ is the timestamp a review was posted, the filed ‘content’ is the review text in Chinese and the field ‘label’ represent why the consumers post the negative reviews, in which 0 for Logistic, 1 for Product function, 2 for Consumer Service and 3 for False Marketing.

The dataset comes from our paper:

Sun M, Zhao J. Behavioral Patterns beyond Posting Negative Reviews Online: An Empirical View. Journal of Theoretical and Applied Electronic Commerce Research. 2022; 17(3):949-983. https://doi.org/10.3390/jtaer17030049

If it is helpful, please cite the paper.

This work was supported by NSFC (Grant No. 71871006).

Introduction

Online Vs In-Store Shopping Statistics: Consumer behavior keeps changing, and in a scenario like this, online shopping becomes more in competition with indoor shopping. A good example could be taken from Walmart itself. Due to the increase in digitalization and globalization, online shopping has increased crazily during the last ten years.

In 2024, convenience, technology, and economics will all be the strong forces behind the new trends. The online vs. In-store shopping statistics refer to the general percentage of consumers worldwide who prefer to shop in a certain way.

When asked if their shopping habits had changed after the coronavirus (COVID-19) pandemic, approximately 70 percent of consumers worldwide said they now shopped online more often. About a fifth of respondents said they only changed their shopping habits temporarily to avoid in-store shopping during the pandemic.

In such a volatile environment, it is difficult to understand customer behavior, especially in online market scenarios, and understanding this area is critical for developing online retailers’ strategies. In the Kingdom of Saudi Arabia (KSA), the number of online users is growing very rapidly, before covid-19 online shopping is at a nascent stage, but now it is entering a growth stage with indefinite lockdowns and restrictions growing worldwide. In this scenario, the lack of knowledge regarding online shopping behavior makes it very difficult to measure it. Therefore, this research is designed to understand online shopping behavior and at the same time develop empirical measures for the assessment of online shopping behavior. This study utilizes the extended Technology Acceptance Model (TAM) to examine the factors that influence Saudi college students’ online shopping behavior for the assessment and testing of hypotheses. Structural equation Modeling (SEM) was used. The outcomes of this research are as follows: Perceived enjoyment, perceived ease of use, social norms, and perceived risk tend to have significant influences on online shopping behavior among college students in the Kingdom of Saudi Arabia (KSA). As stated above that before covid-19, online shopping is at a nascent stage in the Kingdom of Saudi Arabia and is now growing rapidly. To address this research gap, this study analyzes the factors influencing customers’ decisions to shop online through a sample of students from Saudi universities. This research makes a unique contribution to understanding, developing, and empirically testing these measures. This study also contributes to the empirical application of the technology acceptance model (TAM) to Saudi consumers.

Factors that explain adoption of online grocery shopping: Logit estimation results.

"Online Shopping Trends – Coronavirus (COVID-19) Consumer Survey Insights – Weeks 1-10" analyses how consumer behavior online has changed over a period of 10 weeks during the pandemic. The report identifies how income groups, national markets, and categories, have fared during this unprecedented public health crisis. Read More

Online Shopping Guide Platform Market Outlook

The global market size for Online Shopping Guide Platforms was valued at USD 2.5 billion in 2023 and is projected to reach USD 7.8 billion by 2032, growing at a CAGR of 13.1% during the forecast period. The growth of this market is driven by increasing internet penetration, consumer preference for online shopping, and advancements in AI and machine learning technologies that enhance user experience.

The rapid growth in the number of internet users globally has significantly contributed to the expansion of the Online Shopping Guide Platform market. With more people gaining access to high-speed internet, the number of online shoppers has seen a substantial increase. This trend is bolstered by the convenience and variety that online shopping offers compared to traditional retail shopping. As a result, online shopping guide platforms are becoming increasingly essential for consumers to navigate the vast array of products available online and make informed purchasing decisions.

Another significant growth factor is the advancement in AI and machine learning technologies. These technologies enable online shopping guide platforms to offer personalized recommendations, improve search functionalities, and provide detailed product comparisons. AI-driven algorithms can analyze user behavior and preferences to tailor the shopping experience, making it more relevant and engaging. This personalized approach not only enhances user satisfaction but also drives higher conversion rates for e-commerce businesses, thereby fueling the growth of the market.

Moreover, the COVID-19 pandemic has accelerated the adoption of online shopping, further propelling the demand for online shopping guide platforms. With lockdowns and social distancing measures in place, consumers turned to online channels to fulfill their shopping needs. This shift in consumer behavior has led to a surge in the number of online shopping guide platforms, as businesses seek to capture the growing online market. Additionally, the ease of setting up and maintaining online platforms, along with lower operational costs, has made it an attractive option for businesses to reach a broader audience.

In recent years, Social Purchasing has emerged as a transformative trend in the online shopping landscape. This concept leverages social media platforms to facilitate and influence purchasing decisions, creating a more interactive and engaging shopping experience. By integrating social elements, such as user-generated content, reviews, and recommendations, online shopping guide platforms can enhance consumer trust and drive sales. Social Purchasing not only helps in building a community around products but also allows consumers to discover new items through their social networks, making the shopping process more dynamic and personalized. As social media continues to play a pivotal role in consumer behavior, incorporating Social Purchasing strategies can significantly boost the effectiveness of online shopping guide platforms.

Regionally, North America holds the largest market share, driven by the high adoption rate of technology and the presence of major e-commerce players. Europe is also a significant market, with countries like the UK and Germany leading the charge in online shopping adoption. The Asia Pacific region is expected to witness the highest growth rate, owing to the increasing internet penetration and rising disposable incomes in countries like China and India. Latin America and the Middle East & Africa are also emerging markets with substantial growth potential, driven by improving internet infrastructure and growing e-commerce activities.

Platform Type Analysis

The Online Shopping Guide Platform market is segmented into Web-Based and Mobile Apps. The Web-Based platforms have been the traditional medium for online shopping guides, offering extensive features and functionalities that cater to a wide range of users. These platforms typically provide detailed product information, price comparisons, user reviews, and recommendations, making them a comprehensive resource for shoppers. The ease of access and the ability to use these platforms from any device with an internet connection have contributed to their widespread adoption.

However, the advent of smartphones and the increasing preference for mobile shopping have given rise to Mobile Apps as a significant segment within this market. Mobile apps offer a m

Dataset

This dataset was created by Shahadat Hossain

Contents

Online Shopping Market Outlook

The global online shopping market size was valued at approximately $4.9 trillion in 2023 and is projected to reach around $9.9 trillion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. One of the primary growth factors driving this market is the increasing penetration of internet connectivity and smartphones, which has made online shopping more accessible to a wider audience worldwide.

Several factors contribute to the growth of the online shopping market. Firstly, convenience plays a significant role. Consumers can shop from the comfort of their homes, avoiding long lines and crowds typically found in physical stores. This convenience is particularly appealing to busy professionals, parents, and those who live in remote areas with limited access to brick-and-mortar stores. Additionally, the availability of 24/7 shopping options and the ability to easily compare prices across different platforms further enhances the appeal of online shopping.

Another critical driver of market growth is the extensive range of products available online. From everyday essentials to luxury items, online platforms offer a diverse array of products, often at competitive prices. The integration of advanced technologies like Artificial Intelligence and Machine Learning has also enhanced the online shopping experience by providing personalized recommendations based on past purchases and browsing behavior. This level of customization not only improves customer satisfaction but also boosts sales for retailers.

The rise of e-commerce platforms and the digital transformation of traditional retail businesses have also fueled market growth. Companies are increasingly adopting omnichannel strategies that integrate their online and offline operations to provide a seamless shopping experience. Furthermore, the COVID-19 pandemic has accelerated the shift towards online shopping, as consumers turned to e-commerce platforms for their shopping needs due to lockdowns and social distancing measures. This shift is expected to have a lasting impact, with many consumers likely to continue preferring online shopping even post-pandemic.

Regionally, North America and Europe have traditionally dominated the online shopping market due to high internet penetration and a well-established e-commerce infrastructure. However, Asia Pacific is emerging as a significant growth region, driven by rapid urbanization, rising disposable incomes, and an expanding middle-class population. Countries like China and India are witnessing exponential growth in e-commerce activities, supported by government initiatives to promote digital transactions and the proliferation of affordable smartphones.

Product Category Analysis

In the online shopping market, the product category segment is diverse, encompassing various types of goods that consumers can purchase online. Among these, electronics remain one of the most popular categories, driven by the continuous innovation in consumer electronics and the increasing demand for gadgets like smartphones, laptops, and smart home devices. The convenience of comparing features and prices and reading user reviews online has made electronics a staple in the online shopping realm.

The fashion category also commands a significant share of the online shopping market. With the rise of fast fashion and the influence of social media, consumers are increasingly turning to online platforms for clothing, footwear, and accessories. The availability of trendy and affordable fashion options, along with the ease of returns and exchanges, has made online fashion shopping extremely popular. Additionally, personalized recommendations and virtual try-on features are enhancing the shopping experience, driving further growth in this segment.

Home and kitchen products are another vital segment in the online shopping market. From furniture and home decor to kitchen appliances and utensils, consumers find a wide range of options online. The ability to read detailed product descriptions, view images, and compare prices has made online shopping a preferred choice for home and kitchen items. Seasonal sales and discounts offered by e-commerce platforms also attract a large number of buyers in this segment.

Health and beauty products have seen a surge in online sales, particularly during the COVID-19 pandemic when physical stores faced restrictions. Consumers are increasingly purchasing skincare, cosmetics, personal care products, and health supplements online due to