The Challenge: Everything Affects Everything
Your business metrics are influenced by countless overlapping factors. When sales go up 20%, traditional analytics can show you correlations—but was it really your new marketing campaign, or was it the market growing, a competitor stumbling, seasonal trends, or all of the above?
The problem is that these factors don't operate in isolation. They overlap, interact, and influence each other in complex hierarchies. Your product's performance is affected by market-wide trends, category-specific shifts, regional variations, and product-specific factors—all at the same time.
The Traditional Approach Falls Short
Traditional Analytics: "Sales are up 20%. The market grew 10%, so we'll attribute 10% to market growth and 10% to our efforts."
The Problem: Your product is part of the market. You might be the one driving market growth! This naive attribution often assigns credit to the wrong factors.
The FactorPrism Solution: Mathematical Decomposition
FactorPrism uses advanced mathematical techniques borrowed from compressed sensing and signal processing to solve this attribution problem properly. Here's how it works:
1. Hierarchical Modeling
We model your business as a hierarchy of influences. For example:
- Market Level: Overall industry trends
- Category Level: Your product category's performance
- Regional Level: Geographic variations
- Product Level: Your specific product's factors
Each level can influence the levels below it, creating a complex web of potential explanations for any change.
2. The Simplest Explanation Principle
When multiple explanations exist for the same outcome, FactorPrism finds the simplest one—the explanation requiring the fewest active factors. This is based on Occam's Razor: the simplest explanation is usually correct.
Example: Two Products Double in Sales
Possible Explanation 1: Both products independently happened to double (unlikely coincidence)
Possible Explanation 2: The overall market doubled, lifting both products (simpler, more likely)
FactorPrism: Automatically identifies Explanation 2 as more plausible
3. Compressed Sensing Technology
FactorPrism applies breakthrough techniques from compressed sensing—the same mathematics that lets MRI machines reconstruct images from limited data. Just as compressed sensing can recover a complete picture from sparse measurements, we recover the complete story of your business from complex, overlapping signals:
- Separates mixed signals into individual components
- Reconstructs the true drivers from noisy, incomplete data
- Finds the minimal set of factors that explain everything
- Handles thousands of potential influences simultaneously
Why This Matters: Accuracy in Attribution
Our approach is 2-3x more accurate than traditional methods at isolating true causal impacts. This means:
- Better Decisions: Know which initiatives actually work
- Resource Efficiency: Stop investing in factors that don't matter
- Competitive Advantage: Understand drivers your competitors miss
- Risk Mitigation: Identify problems at their true source
Real-World Application
Case Study: Retail Chain Mystery
A retail chain saw certain stores outperforming by 30%. Traditional analysis showed correlations with everything—demographics, store size, local competition, weather patterns.
FactorPrism found the hidden driver: Stores near colleges performed better, but only during specific weather patterns that drove students indoors. The mathematical decomposition revealed this compound effect that simple correlation analysis missed entirely.
Result: Targeted marketing during weather events near college campuses, increasing revenue by $5M annually.
The Bottom Line
FactorPrism doesn't just show you what happened—it mathematically isolates why it happened, finding the simplest and most accurate explanation among millions of possibilities. It's the difference between guessing and knowing.
Ready to See the Science in Action?
Let us show you what's really driving your business metrics.
Request a Technical Demo