This project focuses on analyzing Instagram user interactions and engagement using SQL. As a data analyst working with Instagram's product team, the goal is to derive insights from user data to inform business decisions, improve the user experience, and support marketing campaigns. The analysis covers marketing metrics such as loyal user rewards, inactive user engagement, contest winner identification, hashtag popularity, and investor metrics like user engagement and bot detection.
- SQL: Used for querying and analyzing the database.
- MySQL Workbench: SQL queries and data analysis were performed using this tool.
The main tasks in this project include:
- Loyal User Reward: Identify the five oldest users on Instagram.
- Inactive User Engagement: Find users who have never posted a photo.
- Contest Winner Declaration: Determine the user with the most likes on a single photo.
- Hashtag Research: Suggest the top five most commonly used hashtags.
- Ad Campaign Launch: Identify the best day of the week to launch ads based on user registrations.
- User Engagement: Calculate the average number of posts per user.
- Bots & Fake Accounts: Detect potential bots who have liked every photo on the site.
- Database Setup: Created necessary tables and imported data using provided SQL commands.
- SQL Querying: Utilized SQL queries to extract insights such as the oldest users, inactive users, top hashtags, etc.
- Analysis: Each task involved executing a relevant SQL query, analyzing the results, and deriving actionable insights for the product and marketing teams.
- Oldest Users: Identified the most loyal users based on their registration date.
- Inactive Users: Found users who had never posted a photo, providing targets for re-engagement campaigns.
- Popular Hashtags: Suggested the top five hashtags for brands to increase reach.
- Optimal Ad Launch Day: Determined the best day of the week for Instagram user registration, guiding ad campaign timing.
This project provided key insights that will inform product features, marketing strategies, and operational improvements. By analyzing user engagement, detecting bots, and providing data-backed recommendations, the analysis will help Instagram improve both user retention and overall platform performance.
Snapshots of the SQL queries and their outputs have been included in the report.
This project helped in deepening SQL expertise while providing real-world value to Instagram’s product and marketing teams. The insights gained from the analysis can influence Instagram's future development and engagement strategies.