page-funnel-analysis

📊 Web Funnel Analysis: Page Visit Behavior

This project presents a funnel visualization of page visits across a user journey on a website. The goal is to identify drop-off points, conversion patterns, and optimization opportunities based on web traffic flow.

---

📁 Project Overview

• Objective: Analyze page visit behavior and represent it visually as a funnel to detect where users drop off during navigation.
• Output: Interactive funnel chart (HTML) displaying the transition of users through each stage.
• Format: This is a browser-viewable .html file exported from a Jupyter Notebook using Plotly.

---

🔍 Key Insights

• Tracks the number of users reaching each page/stage.
• Highlights significant drop-offs (e.g., between homepage and cart page).
• Allows for dynamic interaction: hover to see counts, compare stages.

---

⚙️ Tools & Technologies

Tool	Purpose
Python	Data manipulation and analysis
Pandas	Reading and transforming data
Plotly	Interactive data visualization
Jupyter Notebook	Exploratory analysis & reporting
HTML Export	Sharing interactive charts outside notebooks

---

📌 File Structure

📂 Web-Funnel-Analysis │ ├── Page_Funnel_Visits.html # Final interactive funnel chart ├── funnel_analysis.ipynb # (Optional) Jupyter notebook used to generate the chart ├── data.csv # (Optional) Dataset used (anonymized) └── README.md # This documentation

---

🧠 Interpretation Guide

Each bar in the funnel chart represents the number of users at a specific stage in the navigation journey. The decreasing width shows user attrition between pages. Use this to:

• Identify pages with the highest exit rates.
• Optimize content or UX in weak-performing steps.
• Improve conversion by addressing bottlenecks.

---

📈 Sample Use Cases

• E-commerce drop-off detection
• A/B testing landing pages
• Marketing campaign performance tracking

---

🚀 How to View

To view the interactive funnel:

1. Clone or download the repository.
2. Open Page_Funnel_Visits.html in any modern browser (Chrome, Firefox, Edge).
3. Hover over the bars to see user counts and details.

---

📝 Author

Bodea Dana Ramona
Data Analyst | Python & BI Enthusiast

---

✅ Status

📌 Completed — Open to enhancements such as cohort segmentation or time-based funnels.

---

📬 Contact

If you have questions or suggestions, feel free to reach out via [LinkedIn] www.linkedin.com/in/dana-bodea-0aa3262a6 or GitHub Issues .https://amaliada.github.io/page-funnel-analysis/