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Analyzing user engagement data for articles on AnimeMangaToon as part of an internship selection assignment. It employs A/B testing and visualizations to enhance user retention and reduce bounce rates.

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AnimeMangaToon User Interaction Analysis Project

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.

Project Overview

  1. User Engagement Analysis for "Why is the Tower of God Show So Popular?"

    Sample data:
    {02342C6C-1DC9-4E62-9FDA-0D6E74286C37}

    Temporal analysis (views):
    480fa2cd-0878-44fa-b97d-01a201d6bd26

    Top 10 most viewed days:
    {46A5389C-8214-440B-8C51-C361FB041D76}

    And Worst:
    {C1892D1A-85F4-4695-973D-DE3A5B0A0A59}

    Bounce rate vs avg time spent:
    46f1b55b-09a0-4330-a271-9fadc1901e33

    • 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.
  2. 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.

    Sample data:
    {CFAB124F-6B6E-4EA8-BFDF-49690ADB2E1B}

    Visualization:
    c15fb65d-8cc0-4021-8c7a-e6bd83ad4c97

    Inference based on A/B strategy:

    • 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.

  3. 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).

    Segmentation Visualization:
    {6FA0126C-28E9-4EA1-B800-2B2B28666E8E}

    Visualization:
    bcc9e5c9-5247-4211-a136-aeea3c164c74

    • 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 Structure

  • Project_animetoons.ipynb: This Jupyter Notebook contains the entire project code for data simulation, visualization, and analysis.

Technologies Used

  • Python: Primary programming language.
  • Pandas: Used for data manipulation and analysis.
  • Matplotlib/Seaborn: For creating visualizations (bar charts, pie charts, etc.).

How to Run the Project

  1. Install the required libraries (if not already installed):

    pip install pandas matplotlib seaborn
  2. Open the Project_animetoons.ipynb in Jupyter Notebook:

    jupyter notebook Project_animetoons.ipynb
  3. 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.

Key Insights and Results

A/B Testing Summary:

  • 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.

Recommendations for Future Content:

  • 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.

About

Analyzing user engagement data for articles on AnimeMangaToon as part of an internship selection assignment. It employs A/B testing and visualizations to enhance user retention and reduce bounce rates.

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