for example

Envestnet®| Yodlee®'s Online Purchase 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

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.

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 61 percent of them believed so, while only 52 percent of shoppers aged 35 to 49 years had the same opinion.

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.

Observed linkages between consumer and B2B emails and website domains, categorized into IAB classification codes.

This data provides an unprecedented view into individuals' in-market intent, interests, lifestyle indicators, online behavior, and propensity to purchase. It is highly predictive when measuring buyer intent leading up to a purchase being made.

Hashed emails can be linked to plain-text emails to append all consumer and B2B data fields for a full view of the individual and their online intent and behavior.

Files are updated daily. These are highly comprehensive datasets from multiple live sources. The linkages include first and last-seen dates and an "intent intensity" score derived from the frequency of similar intent categories over a period of time.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

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.

In 2023, e-commerce comprised over 15.6 percent of total retail sales in the United States. Forecasts suggest that this proportion will continue to rise steadily in the coming years, reaching approximately 20.6 percent by 2027. Fashion fever The digital revolution has significantly changed how retail is done, impacting a wide range of product categories. Out of all e-commerce product categories, apparel and accessories are the most purchased online in the United States. As of February 2023, roughly 18 percent of all fashion retail sales took place online. Furniture and home furnishing, as well as computer and consumer electronics, ranked second, with over 15 percent of each product category purchased via the internet. The product categories that are least purchased online are office equipment and supplies (1.4 percent) and books, music, and video (5.1 percent). Shopping hotspots Amazon dominates the e-commerce industry in the United States, though other competitors still have significant market share. In December 2023, amazon.com was the most-visited e-commerce and shopping site in the United States. That month, around 45 percent of all visits to e-commerce sites were made to Amazon. Other popular shopping sites include ebay.com, walmart.com, etsy.com, and target.com. The staggering proportion of online retail sales in the country attributed to Amazon is quite remarkable. In 2023, Amazon's website accounted for almost half of all online computer and consumer electronics sales. Similarly, nearly one-third of online fashion purchases in the country were made on Amazon.

China Internet Shopping: Purchase Rate: Catering data was reported at 32.700 % in 2015. This records an increase from the previous number of 15.300 % for 2014. China Internet Shopping: Purchase Rate: Catering data is updated yearly, averaging 15.400 % from Dec 2011 (Median) to 2015, with 5 observations. The data reached an all-time high of 32.700 % in 2015 and a record low of 8.500 % in 2012. China Internet Shopping: Purchase Rate: Catering 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.

CE Transact Signal USA is the premier merchant attributable alternative data set tracking consumer spend on credit and debit cards for brands and tickers in industries like leisure & recreation, available as a panelized aggregated feed.

For 2024's Black Friday and Cyber Monday sales event, also known as the 'Cyber Week', approximately ** 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 ** percent of respondents also planned to buy electronics.

In the fourth quarter 2024, the share of e-commerce in total U.S. retail sales stood at 16.4 percent, up from the previous quarter. From October to December 2024, retail e-commerce sales in the United States hit over 309 billion U.S. dollars, the highest quarterly revenue in history. How e-commerce measures up in total U.S. retail In 2023, the reported total value of retail e-commerce sales in the United States amounted to over one trillion U.S. dollars—impressive, but the figure pales compared to the total annual retail trade value of seven trillion U.S. dollars. E-commerce still accounts for a mere 15.4 percent of total retail sales in the United States. Rising e-commerce segments Online shopping is popular among all age groups, though digital purchases are most common among Millennial internet users. In 2022, around 55 percent of Millennials purchased items via the internet. Mobile commerce is also growing in popularity, as consumers increasingly rely on their smartphones and mobile apps for shopping activities. In the fourth quarter of 2022, m-commerce spending made up 38 percent of the overall online spending in the United States.

It contains all the log information of customers in the shopping mall.

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.

China Internet Shopping: Purchase Rate: Suitcase & Bag data was reported at 34.400 % in 2015. This records an increase from the previous number of 24.900 % for 2014. China Internet Shopping: Purchase Rate: Suitcase & Bag data is updated yearly, averaging 27.700 % from Dec 2011 (Median) to 2015, with 5 observations. The data reached an all-time high of 34.400 % in 2015 and a record low of 12.800 % in 2012. China Internet Shopping: Purchase Rate: Suitcase & Bag 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.

China Internet Shopping: Purchase Rate: Jewelry Accessories data was reported at 23.700 % in 2015. This records an increase from the previous number of 7.300 % for 2014. China Internet Shopping: Purchase Rate: Jewelry Accessories data is updated yearly, averaging 10.000 % from Dec 2010 (Median) to 2015, with 6 observations. The data reached an all-time high of 23.700 % in 2015 and a record low of 6.700 % in 2012. China Internet Shopping: Purchase Rate: Jewelry Accessories 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.

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_79958154e6f9d9a30c5655ddd41d83c5/view

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

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2. MONTH -> from April (4) to August (8)

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3. DAY -> day number of the month

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4. ORDER -> sequence of clicks during one session

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5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

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6. SESSION ID -> variable indicating session id (short record)

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7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

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8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

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9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

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10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

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11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

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12. PRICE -> price in US dollars

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13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

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14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Satisfaction, loyalty, and likelihood of referral are regarded by marketers and the Big Three diagnostics leading to retail profitability. However, as yet no-one has developed a model to capture all three of these constructs in the context of the internet. Moreover, although several attempts have been made to develop models to measure quality of website experience, no-one has sought to develop an instrument short enough to be of practical use as a quick customer satisfaction feedback form. In this research we sought to fill this void by developing and psychometrically testing a parsimonious model to capture the Big Three diagnostics, brief enough to be used in a commercial environment as a modal popup feedback form.