for example

Consumer-based survey responders of the brands to which they are most loyal. From acne products to baby wipes, coffee to pet food, this file has the most responsive data from consumers who respond to Direct to Consumer (DTC) offers. Compiled using a variety of surveying techniques including point of purchase surveying as part of the check out process. 30-day hotline available to ensure the freshest information possible.

Fields Include but are not limited to: Product Categories - Acne Products - Tooth Whiteners - Allergy/Cold Remedies - Baby Wipes - Dog Treats - Imported Beer - Energy Bars - Meat Alternatives -Product Brands, such as: - L'Oreal Paris - Crest - Pepcid - Tylenol - Pampers - Purina - Meow Mix - Budweiser - Keurig - Beyond Meat - Recency of purchase - Email

Competitive Pricing - Available for transactional orders. Yearly data licenses available for unlimited use cases, including marketing and analytics.

Online Purchase Data Consumer Purchase Data consumer Behavior Data Brand Data Online Shopping Data

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.

According to a survey on e-commerce and online shopping in Thailand as of January 2023, around **** percent of the respondents said they were fine with sharing information if it helped the platforms to suggest items they might like. In contrast, almost ** percent of the survey participants were not aware of the personal information that online shopping platforms collected and kept.

In France, consumers aged over 65 years old are the most concerned about AI-powered technologies using personal data excessively in e-commerce. A 2025 survey showed ** percent of them believed so, while only ** percent of shoppers aged 35 to 49 years had the same opinion.

Morocco ICT Usage: Individuals: Online Shopping data was reported at 14.200 % in 2018. This records an increase from the previous number of 12.800 % for 2017. Morocco ICT Usage: Individuals: Online Shopping data is updated yearly, averaging 12.250 % from Dec 2015 (Median) to 2018, with 4 observations. The data reached an all-time high of 14.200 % in 2018 and a record low of 6.400 % in 2015. Morocco ICT Usage: Individuals: Online Shopping data remains active status in CEIC and is reported by National Telecommunication Regulatory Agency. The data is categorized under Global Database’s Morocco – Table MA.TB001: Internet Statistics.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

The percentage of respondents who report that they used the internet to buy something online for the first time after COVID-19 started.

China Internet Shopping: Purchase Rate: Household Appliance data was reported at 39.100 % in 2015. This records an increase from the previous number of 26.600 % for 2014. China Internet Shopping: Purchase Rate: Household Appliance data is updated yearly, averaging 22.800 % from Dec 2010 (Median) to 2015, with 6 observations. The data reached an all-time high of 39.100 % in 2015 and a record low of 11.200 % in 2010. China Internet Shopping: Purchase Rate: Household Appliance data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICG: Internet Shopping: Rate of Purchase.

Envestnet®| Yodlee®'s Online Shopping Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

China Internet Service: Usage Rate: Online Shopping data was reported at 87.900 % in Dec 2024. This records an increase from the previous number of 82.300 % for Jun 2024. China Internet Service: Usage Rate: Online Shopping data is updated semiannually, averaging 60.000 % from Dec 2005 (Median) to Dec 2024, with 37 observations. The data reached an all-time high of 87.900 % in Dec 2024 and a record low of 22.100 % in Dec 2007. China Internet Service: Usage Rate: Online Shopping data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage. Affected by the COVID-19, the data cut-off time for the 2019 is March 2020.

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.

China Internet Service: Number of User: Online Shopping data was reported at 974.430 Person mn in Dec 2024. This records an increase from the previous number of 904.600 Person mn for Jun 2024. China Internet Service: Number of User: Online Shopping data is updated semiannually, averaging 430.485 Person mn from Jun 2007 (Median) to Dec 2024, with 36 observations. The data reached an all-time high of 974.430 Person mn in Dec 2024 and a record low of 41.310 Person mn in Jun 2007. China Internet Service: Number of User: Online Shopping data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Information and Communication Sector – Table CN.ICE: Internet: Purpose of Internet Usage. Affected by the COVID-19, the data cut-off time for the 2019 is March 2020.

Employment statistics on the Online Shopping industry in China

More than half of the interviewed Turkish people were somewhat concerned about sharing their personal data requested when shopping on websites or apps. On the other hand, ** percent of the respondents stated they were not concerned about their personal data when shopping online at all.

Market Size statistics on the Online Shopping industry in China

CE Transact Signal USA is the premier merchant attributable alternative data set tracking consumer spend on credit and debit cards, available as a panelized aggregated feed.

The percentage of respondents who report using a mobile phone or the internet to buy something online in the past year. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

China Internet Shopping: Business Turnover data was reported at 15,426.420 RMB bn in 2023. This records an increase from the previous number of 13,785.320 RMB bn for 2022. China Internet Shopping: Business Turnover data is updated yearly, averaging 2,789.800 RMB bn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 15,426.420 RMB bn in 2023 and a record low of 15.730 RMB bn in 2005. China Internet Shopping: Business Turnover data remains active status in CEIC and is reported by China Internet Network Information Center. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Online Retail Sales.

The percentage of respondents who report that they buy a data package to use the internet. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.