This project analyzes user engagement data for various articles on AnimeMangaToon using A/B testing and various data visualizations. The objective is to gain insights into user behavior and suggest improvements to increase user retention and reduce bounce rates.
Note: All data has been generated randomly using Numpy's Random module.
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User Engagement Analysis for "Why is the Tower of God Show So Popular?"
Bounce rate vs avg time spent:
- Two strategies to increase the average time spent on the page are suggested based on the analysis:
- Enhance internal linking: Adding relevant internal links to keep users engaged with related content.
- Improve content readability: Break the article into sections with subheadings, images, and infographics.
- Two strategies to increase the average time spent on the page are suggested based on the analysis:
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A/B Testing for "Refund High School Chapter 22-30: The New Arc of Mook"
- A/B testing strategy is proposed with content changes:
- Testing different headlines (e.g., engaging question vs. statement) and visuals (character close-ups vs. action shots).
- Metrics like Click-Through Rate (CTR) and Bounce Rate are tracked to measure user interaction.
- Version B performs better on CTR, meaning headlines/thumbnails are catchier in Version B compared to Version A.
- Version B also performs better in Bounce Rate, while Version A suffers from a higher bounce rate, indicating articles in Version A fail to capture the user's attention.
Solution: Version B is the better choice overall and should be the page to go live.
- A/B testing strategy is proposed with content changes:
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User Segmentation Case Study for "11 Best Solo Leveling Arcs in the Manhwa to Read Now"
- Users are segmented based on demographics (e.g., age group) and behavior (e.g., returning vs. new visitors).
- Suggestions are made to tailor content to each segment:
- Users b/w 18-35 are more likely to return to the webpage or explore one.
- Need to tailor suitable experiences for the mature audience to increase return rate (35+)
- Example: A more professional text font and accessible UI.
Project_animetoons.ipynb
: This Jupyter Notebook contains the entire project code for data simulation, visualization, and analysis.
- Python: Primary programming language.
- Pandas: Used for data manipulation and analysis.
- Matplotlib/Seaborn: For creating visualizations (bar charts, pie charts, etc.).
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Install the required libraries (if not already installed):
pip install pandas matplotlib seaborn
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Open the
Project_animetoons.ipynb
in Jupyter Notebook:jupyter notebook Project_animetoons.ipynb
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Run each cell in the notebook to:
- Simulate data for page views, bounce rates, and user behavior.
- Create visualizations for A/B testing and user interaction analysis.
- Analyze and interpret the results to improve content strategy.
- Version B (with new headline and updated visuals) performed better with a higher CTR and a lower bounce rate than Version A.
- Headline Variations: Engaging, curiosity-driven headlines led to more interaction.
- Visual Elements: High-quality visuals (e.g., character close-ups) kept users more engaged.
- Internal Links: Adding more internal links to related content can increase user retention.
- Better Visuals: Use updated, high-quality visuals to reduce bounce rate and improve engagement.
- User Segmentation: Tailor content based on user demographics and behavior to maximize engagement.