An AI-powered marketing intelligence system designed to analyze landing pages, detect behavioral friction, evaluate trust signals, and generate actionable optimization insights.
This repository is a sanitized portfolio version of a larger real-world AI marketing system. It demonstrates the architecture, analysis pipeline, and example outputs without exposing proprietary algorithms, private data, or production infrastructure.
Modern marketing optimization often relies on superficial UI checklists or A/B testing without understanding how users actually make decisions.
This system introduces a behavioral intelligence layer that analyzes:
- user hesitation signals
- trust perception indicators
- CTA clarity
- visual friction
- decision confidence
The goal is to help marketers understand why conversions fail before running experiments.
Identifies signals indicating hesitation, confusion, or trust issues during the decision process.
Analyzes credibility elements such as structure, messaging clarity, and visual cues.
Provides interpretable insights about why users hesitate to convert.
Suggests prioritized improvements to reduce friction and increase conversion probability.
Includes confidence scoring and methodological transparency to prevent over-interpretation of AI outputs.
Below are example outputs generated by the system.
The system generates structured diagnostic outputs.
Example:
{
"hesitation_detected": true,
"hesitation_type": "cta_unclear",
"primary_blocker": "insufficient_evidence",
"friction_score": 5.8,
"confidence": 0.20,
"recommended_fix": "Add clear value proposition above the form"
}
This structured output allows the system to be integrated into:
marketing dashboards
CRO pipelines
AI decision support systems
automated optimization workflows
System Architecture
The project is organized into modular components:
app/
lead_scoring_demo.py
architecture/
system_architecture.md
docs/
system-overview.md
use-cases.md
examples/
sample-analysis-output.json
screenshots/
UI demonstration images
The architecture separates:
signal extraction
behavioral inference
decision diagnostics
recommendation generation
This modular approach allows integration with different marketing platforms.
Technology Stack
The production system uses a combination of:
Python
Behavioral data modeling
Decision analysis frameworks
AI-assisted interpretation layers
This demo focuses on the decision intelligence logic rather than the full production stack.
Use Cases
Conversion Rate Optimization (CRO)
Detect hidden friction before running experiments.
Landing Page Evaluation
Analyze trust signals and decision clarity.
Marketing Diagnostics
Understand behavioral causes behind low conversion rates.
AI Decision Support
Provide interpretable insights to marketing teams.
Automated Marketing Intelligence
Integrate behavioral analysis into marketing analytics pipelines.
Important Note
This repository intentionally excludes:
proprietary AI models
production datasets
API keys
internal infrastructure
commercial optimization algorithms
It is designed as a portfolio demonstration of the system's architecture and capabilities.
About the Author
Nima Saraeian
AI Behavioral Marketing Strategist
Decision Intelligence Systems Builder
Focused on combining:
artificial intelligence
behavioral psychology
marketing analytics
decision science
to build intelligent marketing systems.
License
MIT License
This repository is intended for demonstration, learning, and portfolio purposes.