Explorium is a data science and artificial intelligence platform that helps organizations enrich internal data with relevant external signals to build more accurate and impactful predictive models. By combining internal enterprise data with curated third-party sources, Explorium enables better risk modeling, lead scoring, customer segmentation, and forecasting across multiple industries.
Explorium’s platform automates the discovery, connection, and use of external data, removing the traditional barriers to using third-party datasets. With its AI-powered feature engineering, data discovery engine, and ML model automation, Explorium turns data enrichment into a competitive advantage.
Features
Explorium offers a robust set of features to streamline the enrichment, modeling, and deployment process for predictive analytics:
External Data Enrichment
Access hundreds of curated external data sources—covering business signals, demographics, firmographics, web activity, and more.AI-Based Feature Generation
Automatically generates and ranks new predictive features based on enriched data to improve model accuracy.Explorium Data Catalog
A centralized interface to discover, explore, and assess the relevance of external data assets for your business use case.Smart Match Engine
Connects internal records to external datasets with high accuracy using proprietary identity resolution techniques.AutoML and Model Training
Build, train, and compare machine learning models using enriched feature sets with built-in performance monitoring.Model Deployment and Monitoring
Deploy models to production, monitor performance, and retrain automatically as new data becomes available.APIs and Integration Support
Easily integrate enriched data or model outputs into CRMs, data warehouses, BI tools, or custom applications.
How It Works
Connect Internal Data
Users upload or connect CRM, transaction, or behavioral data to the Explorium platform.Discover Relevant External Data
Explorium’s data catalog and AI engine identify external data signals that align with business objectives (e.g., credit risk, churn prediction).Enrich and Engineer Features
The system enriches the data with hundreds of external attributes and generates predictive features automatically.Build and Evaluate Models
Users can build ML models using Explorium’s AutoML tools or export enriched features for use in their existing data science stack.Deploy and Integrate
Models or enriched datasets are deployed to downstream systems or decision engines via API or batch integration.
Use Cases
Explorium is used by data teams, analysts, and decision-makers across industries, including fintech, retail, marketing, and insurance:
Risk Scoring
Enrich borrower or customer profiles with external financial and behavioral data to improve credit risk modeling.Lead Scoring and Sales Prioritization
Score leads using firmographic and intent data for more accurate pipeline forecasting and sales targeting.Customer Churn Prediction
Add external context such as market activity or economic indicators to anticipate churn more effectively.Marketing Segmentation and Targeting
Use web, social, and geographic signals to create more refined customer segments and ad audiences.Fraud Detection
Combine transactional data with third-party behavioral data to detect anomalies and fraudulent patterns.Demand Forecasting
Enhance forecasts with real-world signals such as weather patterns, mobility trends, and local events.
Pricing
Explorium follows an enterprise pricing model based on:
Volume of enriched data
Number of data sources accessed
Frequency of enrichment (batch or real-time)
Model deployment and usage requirements
Support and integration scope
There is no public pricing listed on the website. Interested organizations can request a demo to receive a customized quote.
Strengths
Bridges the gap between internal and external data sources
Significantly improves model accuracy with relevant third-party signals
Offers automated feature engineering and data science workflows
Reduces the time and effort required to source, clean, and connect external data
Built-in tools for model training, deployment, and monitoring
Secure and enterprise-compliant infrastructure
Drawbacks
Geared toward data science and technical teams; may require ML expertise to fully leverage
Primarily targeted at enterprise-level organizations
No self-service pricing or free trial publicly available
Data privacy and compliance must be carefully managed due to use of third-party data
Comparison with Other Tools
Explorium stands out from traditional data marketplaces and AutoML tools by offering a full-stack solution:
Compared to data providers like Dun & Bradstreet or Clearbit, Explorium combines multiple data sources and connects them intelligently with internal data.
Compared to AutoML tools like DataRobot or H2O.ai, Explorium focuses heavily on data enrichment and feature generation—not just model training.
Unlike basic enrichment APIs, Explorium provides a platform approach that supports everything from data discovery to predictive deployment.
Customer Reviews and Testimonials
Explorium is used by enterprises across fintech, marketing, retail, and e-commerce. While detailed case studies are available on the website, common benefits cited by users include:
Up to 30% improvement in model accuracy after data enrichment
Reduced data acquisition time from weeks to hours
Higher ROI on marketing and sales campaigns through better targeting
Increased agility and experimentation for data science teams
To view industry-specific success stories, visit Explorium’s customer page.
Conclusion
Explorium delivers a transformative approach to predictive modeling by bridging internal enterprise data with the world of external information. With its automated enrichment, AI-based feature engineering, and scalable deployment tools, Explorium empowers organizations to make smarter decisions, faster.
For data science teams and business leaders seeking to gain a competitive edge through enhanced data intelligence, Explorium offers a unified and powerful platform to unlock new value from both internal and external data.
