Features
Relationship Detection
Discover hidden relationships in your data with our advanced AI algorithms. Automatically identify correlations and dependencies that might otherwise go unnoticed in complex datasets.
Correlation Matrix
| Features | Age | Income | Purchase Freq. | Avg. Order | Products Viewed | Satisfaction | Login Freq. |
|---|---|---|---|---|---|---|---|
| Age | 1.00 | 0.72 | 0.23 | 0.42 | -0.31 | 0.05 | -0.25 |
| Income | 0.72 | 1.00 | 0.81 | 0.86 | 0.12 | 0.37 | 0.09 |
| Purchase Freq. | 0.23 | 0.81 | 1.00 | 0.58 | 0.76 | 0.65 | 0.83 |
| Avg. Order | 0.42 | 0.86 | 0.58 | 1.00 | 0.29 | 0.36 | 0.14 |
| Products Viewed | -0.31 | 0.12 | 0.76 | 0.29 | 1.00 | 0.45 | 0.69 |
Relationship Insights
Strongest Correlations
Income and Average Order Value (0.86) - Higher income customers tend to place larger orders.
Purchase Frequency and Login Frequency (0.83) - More frequent logins correlate with more purchases.
Income and Purchase Frequency (0.81) - Higher income customers tend to make purchases more often.
Surprising Relationships
Age and Products Viewed (-0.31) - Younger customers tend to browse more products before purchase.
Products Viewed and Purchase Frequency (0.76) - Higher product browsing correlates with purchase frequency.
Potential Causal Relationships
Increased Login Frequency may directly lead to higher Purchase Frequency - Consider email campaigns to encourage logins.
High Satisfaction appears to drive Purchase Frequency - Focus on customer experience improvements.
AI Recommendations
Consider targeted marketing to younger demographics with more product options to increase engagement.
Create income-based pricing tiers or premium options for higher-income customers.
Implement login incentives to potentially increase purchase frequency across all customer segments.
How Relationship Detection Works
1. Data Connection
Connect your datasets from multiple sources. Our system supports various data formats including CSV, SQL databases, and API endpoints.
Connected 3 datasets with 24 features
2. Feature Analysis
Our AI automatically analyzes data features, detecting types, distributions, and initial patterns to identify candidates for relationship analysis.
Analyzed 18 numerical & 6 categorical features
3. Relationship Detection
Advanced algorithms identify correlations, dependencies, and potential causal relationships between different data features and across datasets.
Detected 42 relationships across datasets
4. Insight Generation
Translate statistical relationships into actionable business insights and recommendations you can implement to improve performance.
Generated 12 actionable recommendations
Key Features
Cross-Dataset Relationship Detection
Discover relationships not just within datasets, but across different data sources. Our system can connect patterns between customer data, sales metrics, and product information even when they're stored in separate systems.
- Correlate data across different database systems
- Match entities across disparate data sources
- Identify complex multi-dimensional relationships
Advanced Correlation Analysis
Go beyond simple Pearson correlations. Our system employs multiple statistical methods to detect non-linear relationships, time-delayed correlations, and complex patterns that traditional analytics would miss.
- Non-linear relationship detection using advanced algorithms
- Time-lagged correlations for trend forecasting
- Multivariate analysis that detects conditional dependencies
Causation Inference Technology
Move beyond correlation to causation. Our advanced AI algorithms use causal inference techniques to suggest which relationships might be causal, helping you make more impactful business decisions.
- Bayesian network modeling for causal relationships
- A/B testing recommendations based on detected relationships
- Counterfactual analysis for decision impact prediction
"The relationship detection feature revealed connections in our data we'd never have found otherwise. We discovered that weather patterns were affecting our online sales in surprising ways, allowing us to adjust our marketing strategy accordingly."
Marcus Johnson
Head of Data Science, RetailNova Inc.
campaign efficiency