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

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

This dataset provides a comprehensive overview of online sales transactions across different product categories. Each row represents a single transaction with detailed information such as the order ID, date, category, product name, quantity sold, unit price, total price, region, and payment method.

Columns:

• Order ID: Unique identifier for each sales order.
• Date:Date of the sales transaction.
• Category:Broad category of the product sold (e.g., Electronics, Home Appliances, Clothing, Books, Beauty Products, Sports).
• Product Name:Specific name or model of the product sold.
• Quantity:Number of units of the product sold in the transaction.
• Unit Price:Price of one unit of the product.
• Total Price: Total revenue generated from the sales transaction (Quantity * Unit Price).
• Region:Geographic region where the transaction occurred (e.g., North America, Europe, Asia).
• Payment Method: Method used for payment (e.g., Credit Card, PayPal, Debit Card).

Insights:

• 1. Analyze sales trends over time to identify seasonal patterns or growth opportunities.
• 2. Explore the popularity of different product categories across regions.
• 3. Investigate the impact of payment methods on sales volume or revenue.
• 4. Identify top-selling products within each category to optimize inventory and marketing strategies.
• 5. Evaluate the performance of specific products or categories in different regions to tailor marketing campaigns accordingly.

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.

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.

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

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 beauty products and services, available as a panelized aggregated feed.

In 2020, the share of online repeat purchases of private labels was the largest in the cosmetics category with a share of ** percent repeat purchases. Moreover, the data shows that the share of online repeat purchases for private labels is more than ** percent in all categories. Among online grocery, wellness and furniture sales the share of repeat purchases with a little more than ** percent is lower than among other categories.

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

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

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

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.

In 2024, about ** percent of internet users in Thailand made an online purchase each week. In contrast, the share of internet users who made weekly purchases in Japan was around ** percent in 2024.

This statistic shows data on internet usage during the purchase process of consumers in the United States in 2014. During the survey period it was found that ** percent of US respondents had recently bought a product online.

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.

The dataset "isoc_ec_ibhv" has been discontinued since 08/02/2024.

Context

This data set is based on transactions of customers who bought occasion gift ware from an online store.

Content

This data is blended with the real time transactions of different online retail stores. The description of the data is defined as below:

Acknowledgements

Center for Machine Learning and Intelligent Systems (UCI)

Inspiration

Question: Which customers are more important for the business? Question: What is the recent visiting period of each customer? Question: What is the purchasing frequency of the customer? Question: What is the spending frequency of the customer?

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.

This dataset contains longitudinal purchases data from 5027 Amazon.com users in the US, spanning 2018 through 2022: amazon-purchases.csv It also includes demographic data and other consumer level variables for each user with data in the dataset. These consumer level variables were collected through an online survey and are included in survey.csv fields.csv describes the columns in the survey.csv file, where fields/survey columns correspond to survey questions. The dataset also contains the survey instrument used to collect the data. More details about the survey questions and possible responses, and the format in which they were presented can be found by viewing the survey instrument. A 'Survey ResponseID' column is present in both the amazon-purchases.csv and survey.csv files. It links a user's survey responses to their Amazon.com purchases. The 'Survey ResponseID' was randomly generated at the time of data collection. amazon-purchases.csv Each row in this file corresponds to an Amazon order. Each such row has the following columns: Survey ResponseID Order date Shipping address state Purchase price per unit Quantity ASIN/ISBN (Product Code) Title Category The data were exported by the Amazon users from Amazon.com and shared by users with their informed consent. PII and other information not listed above were stripped from the data. This processing occurred on users' machines before sharing with researchers.