https://github.com/robinmillford/instagram-user-insights-analyzing-user-behavior

In this project, I delved into the dynamics of a popular photo-sharing website using SQL queries
https://github.com/robinmillford/instagram-user-insights-analyzing-user-behavior

data-analysis data-visualization instragram powerbi sql

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In this project, I delved into the dynamics of a popular photo-sharing website using SQL queries

• Host: GitHub
• URL: https://github.com/robinmillford/instagram-user-insights-analyzing-user-behavior
• Owner: RobinMillford
• Created: 2023-10-04T17:16:39.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-04T17:21:47.000Z (about 1 year ago)
• Last Synced: 2023-10-05T05:41:49.657Z (about 1 year ago)
• Topics: data-analysis, data-visualization, instragram, powerbi, sql
• Homepage:
• Size: 4.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

        
# Insta-Insights: Analyzing User Behavior on Instagram

In this project, I delved into the dynamics of a popular photo-sharing website using SQL queries. Here are some key highlights of my analysis:

**User Registration Insights:** By identifying the day of the week when most users registered, I provided valuable information for optimizing ad campaign scheduling.

**Engagement Strategies:** I discovered inactive users who had never posted a photo, suggesting an email campaign to re-engage them. To boost engagement, I organized a contest to determine the user with the most likes on a single photo.

**User Activity Metrics:** Calculating the average number of posts per user, I assessed user engagement levels. Ranking users by their post counts revealed top contributors. Additionally, I determined the total number of users who had shared at least one post.

**Hashtag Trends:** Analyzing hashtag usage shed light on popular trends, with the top five most commonly used hashtags unveiled.

**Addressing Bot Activity:** I identified users who had liked every single photo and pinpointed those who had never commented on a photo. This insight is valuable for identifying potential bot accounts and assessing user engagement.

**User Engagement Ratio:** I computed the percentage of users who had either never commented on a photo or had commented on every photo, providing insights into different user engagement categories.

**Data Visualization:** I also utilized Power BI for data visualization, enhancing the project's comprehensibility.

Dashboard 1 - ![Alt Text](https://github.com/RobinMillford/Instagram-User-Insights-Analyzing-User-Behavior/blob/main/Page%201.png)

Dashboard 2 - ![Alt Text](https://github.com/RobinMillford/Instagram-User-Insights-Analyzing-User-Behavior/blob/main/Page%202.png)

Through these SQL queries and visualizations, I gained in-depth insights into user behavior on the platform. These findings serve as a foundation for optimizing engagement strategies, addressing bot-related concerns, and enhancing the overall user experience on the photo-sharing website.