Dataplor is a location intelligence platform that utilizes machine learning, advanced image recognition, and human validation to provide real-time, accurate point of interest data across over 250 million locations globally. This enables businesses to make informed decisions regarding site selection, competitive analysis, and targeted marketing, ultimately reducing the risk of lost revenue due to outdated or inaccurate location data.
Funding
$19.8M 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.
Founders
Product
Problem
Many businesses struggle with outdated or inaccurate point-of-interest (POI) data, leading to flawed decision-making in areas like site selection, competitive analysis, and targeted marketing. This can result in lost revenue and missed opportunities due to a lack of reliable location intelligence.
Solution
Dataplor offers a location intelligence platform that provides real-time, accurate POI data for over 250 million locations worldwide. The platform aggregates data from hundreds of sources and employs machine learning, advanced image recognition, and human validation to ensure data quality. This comprehensive approach enables businesses to make informed, data-driven decisions, optimize their strategies, and mitigate risks associated with inaccurate location information. Dataplor's dynamically updated data and extensive global coverage provide a significant advantage over traditional geospatial analytics.
Target Audience
Dataplor's primary customers include consumer packaged goods (CPG) companies, mapping services, third-party logistics providers, retailers, quick-service restaurants (QSRs), and fintech companies that require accurate and up-to-date location data for strategic decision-making.
Features
- Global coverage spanning over 200 countries and territories with a consistent data schema.
- Daily data updates to ensure near real-time accuracy and avoid outdated information.
- Proprietary quality control process combining automated systems and human review.
- Advanced image recognition and machine learning algorithms for data extraction and validation.
- Integration with existing systems to enrich POI databases with accurate location attributes.
- Qualitative KPIs on a worldwide scale for advanced insights.