Welcome to the project focused on analyzing and optimizing email marketing campaigns to improve user engagement. This analysis delves into the relationship between various user and campaign features and their impact on email open and click-through rates (CTR). By leveraging decision trees, conditional probabilities, and visualization techniques, we aim to help marketers enhance their strategies and boost campaign success.

The primary goal is to analyze user behavior and optimize email marketing campaigns by understanding:

• Who opens emails and clicks on links?
• Which user features drive these actions?
• How to predict and improve engagement?

The marketing team sends promotional emails, but only a small fraction opens and clicks the links. Understanding the underlying factors and predicting future behavior will allow for better-targeted campaigns, ultimately improving engagement and ROI.

Key Questions:

• Why do some users open or click emails while others do not?
• Can we predict who will click next time?
• How can we personalize campaigns to improve engagement?

This analysis is aligned with a SaaS/E-commerce business model, where:

• Email Campaigns drive marketing efforts.
• Purchases from email links generate revenue.
• User Retention and growth are critical for long-term success.

Optimizing email strategies improves customer lifetime value (CLV), ROI, and overall business performance.

• Open Rate & Click Rate Calculation:

  • open_rate = (opened_emails / total_emails) * 100
  • click_rate = (clicked_emails / total_emails) * 100

• User & Feature Insights:

  • Impact of Email Text (Short vs. Long)
  • Influence of Personalization on user engagement
  • Time & Day: Identifying the best hours and weekdays for higher engagement
  • Past Purchases: How user history affects email open and click behavior

1. Frequency of Emails Clicked: Analyzing which emails (short vs. long) perform better in terms of clicks.
2. Email Distribution by Country: Identifying engagement patterns based on user geography.
3. Email Open vs Click: Comparing the number of opened emails to the clicked ones.
4. Country-wise Email Open Rates: Tailoring marketing strategies based on country-specific data.
5. Impact of Past Purchases on Engagement: Users with a higher number of past purchases tend to engage more.

• Weekday-wise Engagement:

  • Best Day: Wednesday (2.76% click probability)
  • Worst Day: Friday (1.40% click probability)

• Hour-wise Engagement:

  • Best Hour: 11 PM (4.14% click probability)
  • Worst Hour: 9 PM (0.82% click probability)

• Email Version Impact:

  • Personalized emails saw a 2.73% click rate, nearly double that of generic emails (1.51%).

• Past Purchases:

  • 21–50 past purchases had the highest probability of click engagement (60%), while users with no purchases had a 0.05% probability.

1. Personalized Campaigns: Prioritize personalized emails for higher engagement.
2. Optimal Timing: Schedule emails for late-night or mid-morning for better open rates.
3. Target High-Purchase Users: Focus on users with a history of 20+ purchases for maximum engagement.
4. Weekday Strategy: Focus on Tuesdays & Wednesdays for higher email visibility.

• Python for analysis
• Pandas for data manipulation
• Seaborn & Matplotlib for visualizations
• Decision Trees for feature analysis and optimization

1. Clone the repository: git clone https://github.com/sAI-2025/Email-Marketing-Campaign-Optimization.git
2. Install required libraries: pip install -r requirements.txt
3. Run the notebook: jupyter notebook email_analysis.ipynb

This analysis showcases how understanding user behavior and applying data-driven techniques can significantly improve the performance of email marketing campaigns. By optimizing factors like timing, personalization, and targeting high-purchase users, marketers can achieve better results and increase engagement.