BEHAVIORAL INTELLIGENCE SYSTEM
Transform your behavioral data into actionable intelligence using rigorous mathematical models. Unlike black-box analytics, every calculation is transparent, defensible, and grounded in first principles.
Deploy advanced Markov-Shapley algorithms combined with Kelley's Covariation Model to extract behavioral intelligence from digital footprints.
Ignores 90% of the customer journey. A user sees 5 touchpoints but only the last gets credit.
Can't explain why. When the CFO asks "how did you calculate this?" you have no answer.
Your personal data travels to third-party servers. Privacy policies change. Data gets leaked.
Upload your data export to begin analysis
DEPLOY TARGET FILE
DROP FILE OR CLICK TO SELECT FROM SYSTEM
SUPPORTED DATA SOURCES
Google Takeout • Facebook Export • Apple Archives • Browser History
Accepts: JSON, CSV, ZIP, TXT • Max 50MB • All processing happens locally in your browser
Combines Markov chain removal effects (causality) with Shapley value allocation (fairness). The α parameter lets you tune the balance.
Tags behaviors as dispositional (personality-driven) or situational (context-driven) using Consistency, Distinctiveness, and Consensus.
Generates three reports: Executive Summary (insights), Technical Analysis (methodology), and Risk Assessment (limitations).
This is a Personal Epistemic Instrument—designed for self-reflection, not surveillance. We analyze what you did, never what you felt.
Experience the power of Markov-Shapley hybrid attribution with real-time visualization and advanced uncertainty quantification. Explore channel performance and journey patterns.
Personal Epistemic Instrument v1.0.0
A thinking instrument that transforms your behavioral data into actionable insights using rigorous mathematical models. Unlike black-box analytics, every calculation is transparent, defensible, and grounded in first principles.
Control the balance between causality and fairness in attribution
Hybrid = α·Markov + (1-α)·ShapleyEach node is a Markov state. Drag to rotate. Connections show transition probabilities between touchpoints.
Center: All journeys → Inner ring: Channels → Outer ring: Individual journeys
Each line = one touchpoint. See how journeys traverse: Channel → Position → Touchpoints → Duration → Value → Conversion
Animated particles show transition probability flow. Wider streams = more frequent transitions.
Final credit allocation with 95% confidence intervals from Bootstrap + Dirichlet UQ
| Channel | Hybrid Share | Attributed Value | 95% CI | Confidence |
|---|
This is a Personal Epistemic Instrument—designed for reflection, not surveillance. It helps you understand your own behavioral patterns.
"Layer 4 (Psychographic Inference) is architecturally prohibited. We analyze what you did, never what you felt."
Last-click ignores all earlier touchpoints. A user might see 5 ads before converting—last-click gives 100% credit to the final one. Our hybrid model credits the entire journey fairly.
Every calculation is derivable from axioms. Shapley values satisfy Efficiency, Symmetry, Dummy, and Additivity. Markov chains are provably row-stochastic. No black boxes.
We resample your data 10,000 times (Bootstrap) and add Bayesian smoothing (Dirichlet). The 95% CI shows the range where the true value likely falls.
Yes, within the model's assumptions. Check the CI width—narrow = high confidence. Compare against holdout tests. This is a scientific instrument, not an oracle.
CLASSIFIED - PRIVACY-FIRST - LOCAL PROCESSING - SCHEMA-VALIDATED
2024 ATTRIBUTION MATRIX - ALL SYSTEMS OPERATIONAL