Website-Data-Analysis

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🛠️ Tools Used

• Python, Pandas, NumPy
• Seaborn, Matplotlib
• Google Colab

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🚀 Objectives

• Analyze how users arrive at the website (Channel Group breakdown).
• Understand engagement behavior (sessions, bounce, engagement time).
• Discover hourly traffic trends.
• Compare engaged vs non-engaged sessions by channel.
• Recommend data-driven strategies for improving engagement.

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🧹 Data Cleaning

• Set the correct headers from the first row.
• Converted Date-Hour from string (e.g. 2024041723) to datetime.
• Coerced numeric columns to proper data types.
• Derived Hour feature for temporal analysis.

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📊 Analysis & Visualizations

📈 1. Traffic Trends Over Time

• Line chart showing Sessions and Users by timestamp.
• Identifies peak traffic windows.

📊 2. Top Channels by User Volume

• Bar chart grouped by Channel Group.
• Reveals which marketing channels drive the most users.

⏱️ 3. Average Engagement Time

• Engagement time visualized per channel.
• Helps identify channels that attract higher-quality traffic.

🔄 4. Engaged vs Non-Engaged Sessions

• Grouped bar chart showing split by channel.
• Highlights where traffic is bouncing or interacting.

⏰ 5. Hourly Heatmap

• Heatmap of sessions by Hour and Channel Group.
• Provides insights for optimal content/campaign scheduling.

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🔍 Key Insights

• Organic Search and Email show higher engagement rates.
• Some channels (e.g., Display) drive traffic but with low engagement.
• Email traffic spikes in the morning; Social Media in the evening.
• Engagement rate should guide channel optimization, not just traffic volume.

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💡 Recommendations

• Increase focus on high-engagement channels.
• Improve targeting and landing pages for low-engagement channels.
• Schedule campaigns based on peak hourly traffic windows.
• Prioritize engagement rate over raw traffic for success metrics.

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