This startup provides real-time, in-store data on pricing, promotions, and product availability to retailers and brands. Their data-as-a-service platform uses natural language processing and machine learning to transform disparate data into organized, actionable insights, eliminating the need for manual store visits.
Funding
$16M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
NMFounders
Product
Problem
CPG brands and retailers often struggle to maintain a comprehensive understanding of real-time market conditions across numerous stores and online channels. Traditional methods of gathering competitive intelligence, such as manual store visits and syndicated data, are often time-consuming, expensive, and provide stale or incomplete insights. This lack of timely and granular data hinders effective decision-making related to pricing, promotions, assortment, and new product introductions.
Solution
Datasembly provides a data-as-a-service platform that delivers real-time, hyper-local product data, enabling brands and retailers to make informed decisions with speed and precision. The platform aggregates and analyzes over 12 billion product data points weekly from 150,000+ stores and 200+ retailers, encompassing pricing, promotions, and assortment information. Using machine learning and natural language processing, Datasembly transforms disparate, publicly available data into structured, actionable insights. This allows users to monitor competitor actions, optimize pricing strategies, track product availability, and identify new product trends with unparalleled granularity and timeliness.
Target Audience
The primary target audience includes CPG brands and retailers seeking to optimize pricing, promotions, assortment, and new product strategies through real-time, hyper-local market intelligence.
Features
- Real-time data collection from over 150,000 brick and mortar stores and online channels
- Comprehensive coverage of pricing, promotions, and product assortment
- Machine learning and natural language processing for data enrichment and product matching
- Store-level pricing detail for granular competitive analysis
- Customizable dashboards and alerts for tracking key products and retailers
- Interactive maps, charts, and graphs for data visualization
- Data export capabilities for integration with existing systems
- Competitive product matching, even for private label products