Website Data Analysis – Extended Introduction
- Project Purpose & Context This analysis aims to uncover actionable insights from web traffic data to inform marketing strategy and enhance user engagement. It’s designed for website owners, digital marketers, and data analysts who want to understand:
Who is visiting the site and when
How different traffic sources perform in terms of engagement
What patterns or trends emerge over time that could guide optimization efforts
By interpreting user behavior and traffic channel effectiveness, this project helps stakeholders make data-driven decisions to boost website performance and conversion.
- Dataset Overview Source: Exported from a web analytics platform (e.g., Google Analytics)
Key dimensions:
session_date, user_id, traffic_channel (Direct, Organic Search, Referral, Paid Social, etc.)
Key metrics:
sessions, users, avg_session_duration, pages_per_session, bounce_rate
- Analytical Objectives Temporal Trends
Track how sessions and active users change over time (daily, weekly, monthly).
Identify peak traffic periods and assess seasonality or anomalies.
Channel Comparison
Rank traffic channels by volume, engagement, and retention metrics.
Evaluate which channels are delivering the most valuable users.
Engagement Analysis
Compare average session durations and pages per session across channels.
Highlight channels with stronger stickiness or lower bounce rates.
Correlation & Patterns
Investigate relationships between variables (e.g., session duration and pages per session).
Surface insights such as “traffic from paid social has higher bounce rate” or “organic search brings longer sessions”.
- Tools & Workflow Data processing using Pandas and NumPy
Visualization via Matplotlib and Seaborn:
Line charts for temporal trends
Bar plots for channel performance
Scatter plots and heatmaps for correlation analysis
Data cleaning steps include handling missing values, date formatting, and channel grouping.
- Expected Deliverables Visual report: Charts illustrating key findings and channel performance summaries
Insights & recommendations:
Identifying best-performing channels
Proposing optimization strategies (e.g., invest more in high-retention channels, improve low-performing ones)