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

Buy consumer data from us to find the target audience for b2c marketing. FrescoData offer the Highest Value for People and consumer marketing.

CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data was reported at 5.700 % in Apr 2025. This records a decrease from the previous number of 6.000 % for Mar 2025. CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data is updated monthly, averaging 5.400 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 11.300 % in Jan 2016 and a record low of 2.900 % in Oct 2009. CCI: Plans to Buy Within 6 Mos: sa: Major Appliances: Vacuum Cleaner data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

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.

Studies of electoral clientelism—the contingent exchange of material benefits for electoral support—frequently presume the presence of strong parties. Parties facilitate monitoring and enforcement of vote-buying and allow brokers to identify core voters for turnout-buying. Where money fuels campaigns but elections center around candidates, not parties, how do candidates pitch electoral handouts? We analyze candidates’ distribution of cash during an Indonesian election. Drawing upon varied data, including surveys of voters and brokers, candidates’ cash-distribution lists, and focus-group discussions, we find heavy spending, but little evidence of vote-buying or turnout-buying. Instead, candidates buy brokers more than voters: with little loyalty or party brand to draw on, candidates seek to establish credibility with well-networked brokers, who then protect their turf with token payments for their own presumed bloc of voters. Consistent with our argument that these are non-contingent payments, we find little evidence of monitoring of either voter or broker behavior.

United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data was reported at 4.500 % in Apr 2025. This records a decrease from the previous number of 5.600 % for Mar 2025. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data is updated monthly, averaging 3.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 7.700 % in Jul 2020 and a record low of 1.700 % in Dec 2009. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish.

In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package.

Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records.

It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record.

***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

E-commerce Purchases data / online Basket data. User shopping user journey & online product check-out. Online purchases on multiple Marketplaces for Germany and USA.

Consultancy firms use our ecomm data to support strategic decision-making with real market data on house hold spend, consumer journey, and eCommerce purchase intent.

Financial institutions, hedge funds, investors, and Market Research companies utilize our ecomm data to uncover market trends, validate investment theses, and identify early signals of brand performance. With actual real online transactions empowering smarter, faster, and more data-driven investment decisions.

This dataset includes Events such as: add_to_cart and remove_from_cart ; allowing visibility into consumer hesitations and brand choice.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Social media's influence on Gen Z shopping habits has surged dramatically in recent years. A 2024 survey reveals that ** percent of Gen Z consumers discovered new products or brands through social media influencers, up from ** percent in 2023. This shift underscores the growing importance of digital platforms in shaping consumer behavior, particularly among younger demographics. Beauty and personal care e-commerce growth The rising impact of social media on Gen Z purchasing decisions aligns with broader trends in e-commerce, particularly in the beauty and personal care sector. Global revenue in this market is forecast to increase by ***** percent between 2024 and 2029, reaching a new peak of ****** billion U.S. dollars. This growth trajectory suggests that the digital landscape will continue to play a crucial role in consumer discovery and purchasing habits. Payment preferences among young consumers Traditional payment methods remain popular among Zoomers. A survey across North America, Europe, and Latin America found that debit and credit cards are still preferred by Gen Z for online shopping and travel bookings. However, younger consumers are showing a growing demand for diverse payment options, indicating potential shifts in the e-commerce landscape as digital wallets gain prominence, particularly in Asia-Pacific markets.

Treasury plans to sell up to $10 billion of securities per month, subject to market conditions. This is in addition to principal paydowns (currently ranging between $2 and $4 billion per month). If the sales proceeded at the full $10 billion per month, the portfolio would be unwound in whole over approximately one year, depending on future rates of prepayments. If market conditions change and Treasury slows asset sales, it is possible that the unwind will take a longer period of time. Shows range of prices MBS securities were purchased for.

Access high-fidelity consumer data powered by our proprietary modeling technology that provides the most comprehensive consumer intelligence, accurate targeting, first-party data enrichment, and personalization at scale. Our deterministic dataset, anchored in the purchasing habits of over 140 million U.S. consumers, delivers superior targeting performance with proven 70% increase in ROAS.

Core Data Assets Transactional Data Foundation: Real purchasing behavior from over 140 million U.S. consumers with 8.5 billion behavioral signals across 250 million adults. Seven years of daily credit card and debit card purchase data aggregated from all major credit cards sourced from more than 300 national banks, capturing $2+ trillion in annual discretionary spending.

Consumer Demographics & Lifestyle: Comprehensive profiles including age, income, household composition, geographic distribution, education, employment, and lifestyle indicators. Our proprietary taxonomy organizes consumer spending across 8,000+ brands and 2,500+ merchants, from major retailers to emerging direct-to-consumer brands.

Behavioral Segmentation: 150+ custom consumer communities including demographic groups (Gen Z, Millennials, Gen X), lifestyle segments (Health & Fitness Enthusiasts, Tech Early Adopters, Luxury Shoppers), and behavioral categories (Deal Seekers, Brand Loyalists, Premium Service Users, Streaming Subscribers). Purchase Intelligence: Deep insights into consumer spending patterns across entertainment, fitness, fashion, technology, travel, dining, and retail categories. Our models identify cross-category purchasing behaviors, seasonal trends, and brand switching patterns to optimize targeting strategies. Advanced Modeling Technology

Our proprietary consumer intelligence engine combines deterministic transaction-based data with Smart Audience Engineering that transforms first-party signals from anonymized website traffic, behavioral indicators, and CRM enrichment into precision-modeled segments. Unlike traditional data providers who sell static lists, our AI-powered predictive modeling continuously learns and optimizes for unprecedented precision and superior conversion outcomes.

Performance Advantages: Audiences built on user-level transactional data deliver 70% increase in ROAS compared to traditional targeting methods. Weekly-optimized audiences with performance narratives eliminate wasted ad spend by 20-30%, while our deterministic AI models analyze hundreds of attributes and conversion-validated signals to identify prospects with genuine purchase intent, not just lookalike behaviors.

25487 Active Global View buyers list and Global View importers directory compiled from actual Global import shipments of View.

United States CSI: Home Values: Next 5 Yrs: Don't Know data was reported at 1.000 % in May 2018. This stayed constant from the previous number of 1.000 % for Apr 2018. United States CSI: Home Values: Next 5 Yrs: Don't Know data is updated monthly, averaging 1.000 % from Mar 2007 (Median) to May 2018, with 135 observations. The data reached an all-time high of 4.000 % in Jun 2014 and a record low of 0.000 % in Mar 2017. United States CSI: Home Values: Next 5 Yrs: Don't Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: What about the outlook for prices of homes like yours in your community over the next 5 years or so? Do you expect them to increase, remain about the same, or decrease?By about what percent per year do you expect prices of homes like yours in your community to go (up/down), on average, over the next 5years or so?

United States CSI: Home Buying Conditions: Bad Time: Can't Afford data was reported at 7.000 % in May 2018. This records an increase from the previous number of 5.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data is updated monthly, averaging 8.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 19.000 % in Aug 2011 and a record low of 1.000 % in Jul 1999. United States CSI: Home Buying Conditions: Bad Time: Can't Afford data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

The San Francisco Controller's Office maintains a database of payments made to vendors from fiscal year 2007 forward. This data is presented on the Vendor Payments report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format, which represents summary data by purchase order. We have removed sensitive information from this data – this is intended to show payments made to entities providing goods and services to the City and County and to protect individuals. For example, we have removed payments to employees (reimbursements, garnishments) and jury members, revenue refunds, payments for judgments and claims, witnesses, relocation and rehousing, and a variety of human services payments. New data is added on a weekly basis. Supplier payments represent payments to City contractors and vendors that provide goods and/or services to the City. Certain other non-supplier payee payments, which are made to parties other than traditional City contractors and vendors, are also included in this dataset, These include payments made for tax and fee refunds, rebates, settlements, etc.

Success.ai’s B2B Intent Data – Know Your Customer & Accelerate Growth

Success.ai’s B2B intent data offers businesses the power to stay ahead of the competition by tracking real-time purchase intent signals from companies actively searching for products and services like yours. By leveraging behavioral insights from trusted and ethically sourced partners, Success.ai provides businesses with the information needed to prioritize high-value leads, engage the right prospects, and ultimately, accelerate sales growth.

Intent data helps you understand where potential customers are in their buying journey, based on their interactions with online content, search patterns, and behaviors. This actionable insight ensures your outreach efforts are timely and relevant. With Success.ai’s B2B intent database, you no longer waste time on cold leads but instead focus on prospects who are in-market and ready to convert.

API Features:

• Continuous Updates: Our APIs provide continuous updates, ensuring that you receive the most current and actionable intent signals available.
• Scalable Access: Capable of handling large volumes of API calls, our system supports high demand and delivers data seamlessly to your platforms.
• Customizable Filters: Tailor your data retrieval by selecting specific intent topics, industries, or stages in the buyer's journey, making your targeting efforts more precise and effective.

Benefits of Success.ai’s Buyer Intent Data:

• Enhanced Targeting: Pinpoint potential customers who are in the decision-making phase, drastically reducing the sales cycle and increasing conversion rates.
• Competitive Advantage: Stay ahead of the competition by engaging prospects at the right time with the right message, leveraging our extensive coverage of intent topics.
• Best Price Guarantee: We ensure that our intent data solutions are not only comprehensive but also competitively priced to fit your budget.

Unlock Targeted Outreach for Maximum Impact

Success.ai’s purchase intent data allows you to create hyper-personalized campaigns that are tailored to the exact interests of your prospects. By tracking their online behaviors, we help you discover which companies are searching for solutions like yours. With this b2b marketing data, you can refine your strategies, engage prospects at the perfect moment, and significantly boost your chances of conversion.

Whether you're looking to generate b2b email data lists for targeted marketing campaigns, identify purchase-ready companies in emerging markets, or refine your b2b contact data, Success.ai’s B2B intent data ensures that every interaction with your leads is meaningful and results-driven.

Key Benefits:

Real-Time Intent Signals: Stay ahead of the curve with continuously updated intent data that tracks buyer behaviors across the web. Identify which companies are actively searching for your products and services. Actionable Insights: Our B2B intent database provides detailed insights into which companies are researching your solutions, enabling you to create hyper-personalized outreach strategies. Ethically Sourced & Compliant: We work with trusted partners and ensure all intent data is collected ethically and in compliance with global privacy regulations. Fill Your Pipeline with High-Value Prospects: With Success.ai’s purchase intent data, you gain access to companies that are ready to buy, helping you fill your pipeline with leads that are primed for conversion. Tailored Solutions for Sales, Marketing, and Beyond

Success.ai’s B2B intent data is tailored to fit various business functions. Here’s how different teams can benefit:

Sales Prospecting & Lead Generation: Prioritize high-potential leads by focusing on companies actively searching for your solutions. Success.ai provides the actionable data needed to build accurate B2B email lists for direct sales outreach.

Account-Based Marketing (ABM): Refine your ABM strategy by identifying key accounts that are showing interest in specific products or services. With purchase intent data, you can tailor your marketing campaigns to align perfectly with each target's interests.

Recruitment and Talent Sourcing: Identify companies that are actively expanding or seeking specific technologies, enabling your team to target talent acquisition efforts more effectively.

Investment Research & Due Diligence: Use intent signals to identify companies on the rise, whether they are exploring new technologies or undergoing significant growth, providing you with a clearer picture of investment opportunities.

Competitor Intelligence: Keep an eye on the behavior of key competitors by tracking their employee movements, expansion into new markets, and technological adoptions.

Real-Time Purchase Intent Signals to Stay Ahead of Your Competition

Success.ai’s B2B intent data is updated continuously, giving you real-time insights into your prospects’ online behaviors. By focusing on the most r...

United States CSI: Home Buying Conditions: Bad Time: Prices are High data was reported at 22.000 % in May 2018. This records an increase from the previous number of 18.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Prices are High data is updated monthly, averaging 10.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 34.000 % in Aug 1978 and a record low of 2.000 % in Sep 2012. United States CSI: Home Buying Conditions: Bad Time: Prices are High data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

3870 Active Global Barcode Scanner buyers list and Global Barcode Scanner importers directory compiled from actual Global import shipments of Barcode Scanner.