Seeking consumer panel, digital behavior, and psychographic datasets from top research and segmentation sources
Discover high-value data requests and learn how to monetize your datasets through the Tiki network.
Seeking AppLovin mobile ad spend data with 2-5 years of history and weekly updates, tracking in-app advertising trends and budget allocations.
Seeking B2B SaaS transaction data with 2-5 years of history and weekly updates, tracking enterprise software spend exceeding $1 million per month.
B2B transaction data with 2-5 years of history and weekly updates, tracking corporate spending globally.
Retail transaction data from Asian markets with CPI-aligned insights, revealing historical spending trends, inflation, and consumer behavior across key economies like China, Japan, Korea, and Vietnam.
Sourcing POS terminal data combined with paper receipt insights, including terminal IDs, offering a deep dive into market trends, industry usage, and technology adoption.
Seeking commercial leased vehicle counts for service companies, including company names and fleet sizes, with 10 years of historical data for insights into economic trends and market dynamics.
Global credit card spending data (excluding the EU), offering insights into consumer trends, regional variations, and economic activity to support financial, retail, and travel industry decisions.
Hotel data covering demand trends, pricing insights, customer sentiment, and market performance to support smarter decisions across finance, travel, and hospitality sectors.
Comprehensive airline data covering demand patterns, operational performance, market trends, and macroeconomic indicators to support smarter decisions across finance, travel, and economic sectors.
Detailed data from paper receipts, including terminal IDs and metadata, offering insights into payment infrastructure, consumer behavior, and hardware trends across retail environments.
Detailed full-service restaurant menu price data from the US, offering insights into inflation, consumer spending, and economic behavior.
Looking for foot-traffic data from manufacturing facilities in Asian countries such as Japan, China, Korea, and Vietnam. The dataset should offer broad insights into facility activity and workforce engagement.
Seeking brand-level ad spend data for publicly traded companies, including ad spend, campaign performance metrics, and demographics.
Looking for large-coverage Brazilian data across multiple sectors, including consumer transactions, receipts, foot traffic, job boards, supply chain, ad spend, and real estate.
Sourcing any sonsumer spending data from China, covering retail and economic activity. The dataset should provide insights into consumer behavior and economic strength cues for assessing market trends.
Seeking data relevant to Nvidia, such as sales, production volumes, and market share for GPUs and AI hardware. The dataset should provide insights into Nvidia’s performance across different product lines and its competitive positioning.
Seeking semiconductor manufacturing data including production volumes, supply chain efficiency, and pricing trends. The dataset should provide insights into elements such as manufacturing performance and semiconductor component pricing.
Healthcare claims data including patient volumes, treatment types, and prescription trends. The dataset should provide deidentified claims data with regional insights into healthcare utilization and costs.
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.
Consumer transaction and e-receipt data from Japan, with a focus on item-level purchase details and spending behavior across various categories.
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.
Data on logistics, shipment, heavy machinery, and trucking spending within the industrials sector. The dataset should cover spending amounts, shipment volumes, and equipment investments.
