GOT DATA?

Buying data on the number of customers at US independent pizzerias, based on unique credit card transactions. Also interested in similar data for chains like Domino's or Papa John's, per location. Partial datasets are acceptable.

Seeking point-of-sale data for retail outlets in the US and globally, focusing on food & beverage, with optional segments like electronics and apparel. Include weekly product-retailer metrics on sales, pricing, and distribution for both brick & mortar and eCommerce.

Consumer transaction and e-receipt data from Japan, with a focus on item-level purchase details and spending behavior across various categories.

Seeking non-credit card data providing insights into customer KPIs for Planet Fitness and the broader fitness industry. Specifically interested in data on new customers, returning customers, and cancellations.

Seeking market and industry conditions data affecting surety bond underwriting, including economic environment and industry-specific trends. This data helps in understanding external factors that could impact the principal’s performance.

Data on logistics, shipment, heavy machinery, and trucking spending within the industrials sector. The dataset should cover spending amounts, shipment volumes, and equipment investments.

Buying US physical receipts data, with a focus on grocery receipts. The dataset should include itemized details and can be OCR-scanned from physical receipts, providing insights into consumer spending.

Buying consumer data on alcohol purchases for ceremonies and parties in the USA, including price, consumer address, and type of alcohol purchased. Require at least three years of historical data with monthly updates.

Buying US-specific product transaction data from major retailers, eCommerce platforms like Shopify, and other relevant sales channels. The data will be used to analyze video ad conversion rates.

Seeking data on spending for non-consumables, including durable goods and apparel in the USA. The focus is on product receipts and transactions, primarily from physical receipts.

Transaction data on Starbucks' energy drinks, including both new products and cold energy drink category. The focus is on understanding sales performance and consumer adoption trends.

Buying data on sportsbook market share among millennials, including newer platforms like DraftKings and FanDuel, and legacy sportsbooks like William Hill and BetMGM. The focus is on millennial engagement and preferences in the sports betting market.

Seeking UPC code data for specific jeans sold by retailers such as Target, Walmart, and Amazon. The request includes the number of jeans sold per specific UPC code and the price paid by consumers.

Sourcing data on net churn for Netflix and other streaming services such as Hulu, Amazon Prime Video, Disney+, and HBO Max. The focus is on net additions versus losses to assess subscriber growth and retention trends across these platforms.

Buying legal and regulatory compliance data on surety principals, including past legal issues, lawsuits, regulatory compliance, and dispute history. This data is key for assessing the legal risks associated with underwriting the bond.

Sourcing data on alcohol purchases surrounding major sporting events (e.g., Super Bowl, Kentucky Derby), including price, store address, type, brand, and quantity. Require at least three years of historical data with monthly updates.

Sourcing E-receipt, SKU-level data extracted from email or account linking. 1M US consumer coverage required for major retailers (e.g. Amazon, Walmart, Costco, Apple, Target, Home Depot).

Sourcing lawsuit data on small to medium-sized businesses in the USA, focused on premise liability cases, both open and settled. This data should clarify legal challenges for risk assessment and mitigation. Used to help offer insurance to otherwise denied businesses.

Looking for complete P&C claims data across various fact patterns, with a focus on deidentified bodily harm data. The dataset should offer broad coverage and detailed insights into claims patterns, aiding in the automation and analysis of claims processing.

Sourcing business history and experience data on surety principals, including their track record with similar projects, years in business, and reputation within the industry. This data helps evaluate the principal’s capability and reliability.

Tear down data for semiconductor components, providing visibility into pricing, volume, and supply chain details. The dataset should cover various semi components, including costs and manufacturing details.

Sourcing data on the volume of milk sales (in gallons) in NYC, including the price paid per gallon across various retail channels. The data should reflect both average and specific pricing trends over time.

Seeking financial health data on surety principals, including credit history, financial statements, net worth, and liquidity ratios. This data is essential for assessing the financial stability and reliability of principals seeking surety bonds.

Seeking project-specific data for surety bond underwriting, focusing on the type and scope of projects, contract terms, and details on subcontractor and supplier relationships. This data is crucial for assessing the complexity and risks associated with specific projects.

Looking to purchase financial details such as loans, creditworthiness, and overall financial health for small-to-medium sized business owners. This data is crucial for understanding the financial stability and growth potential of these businesses.

Advertising spend data, primarily from Google, with insights into spending trends, ad performance, and budget allocation across different industries.

B2B spend data in the software sector, including expenditures on SaaS products, enterprise solutions, and business tools. The dataset should provide insights into spending patterns by business size and industry type.

Seeking real-world data on small to medium-sized business success, including foot traffic, revenue, and employee turnover. The dataset should provide insights into operational dynamics for comprehensive performance analysis.

Luxury spending data, with insights into consumer purchases of high-end goods, including spending amounts, product categories, and consumer demographics.

Sourcing performance data for rug brands, with a focus on NuLoom and its competitors at major B2B segment retailers such as Amazon, Walmart, Target, and Wayfair. The data should include sales volumes, market share, and trends across these retail platforms.

Device activation data, especially for iOS devices, providing insights into activation trends, consumer usage, and market share within the telecom sector.

Seeking Comprehensive contact and profile data for students, faculty, staff, and alumni of all universities in the country. The data should include names, emails, titles, organizations, LinkedIn profiles, wealth scores, home addresses, and additional information like political donations from public records. Specific interest in schools: Notre Dame, Catholic U of America

Data on consumer transactions and e-receipts from international markets, including the UK, EU, and ASIAPAC. The dataset should provide insights into spending patterns, product categories, and frequency of purchases in these regions.

Buying construction-specific project data, including the type, size, and scope of construction projects, contract terms, and relationships with subcontractors and suppliers. This data should capture the complexities and risks unique to construction projects.

Buying data on consumer spending patterns for individuals who have taken Ozempic or GLP-1 drugs, comparing their spend on beverages, snacks, and restaurants before and after starting the medication. The focus is on identifying shifts in spending within these sub-sectors.