This project analyzes an Instagram-like relational database to derive actionable business and investor insights using SQL. The analysis focuses on understanding user engagement, retention, content performance, hashtag usage, and identifying potential bot or fake accounts.
The project demonstrates the ability to translate real-world business questions into structured SQL queries and generate decision-oriented insights.
- Identify loyal users for reward and retention strategies
- Detect inactive users to support re-engagement campaigns
- Analyze content engagement to determine high-performing posts
- Identify trending hashtags to maximize marketing reach
- Determine optimal user registration days for ad campaign launches
- Detect abnormal behavior indicating potential bot or fake accounts
The analysis is based on an Instagram-like relational database consisting of the following tables:
users– User profile and registration detailsphotos– User-posted contentlikes– User engagement with photostags– Hashtag metadataphoto_tags– Mapping between photos and hashtags
- SQL – Core analysis and querying language
- MySQL Workbench 8.0 – Database management and query execution
- Identified the five oldest users on the platform to support loyalty and reward programs.
- Detected users who have never posted any photos, highlighting opportunities for targeted re-engagement.
- Identified the user whose single photo received the highest number of likes, demonstrating the impact of high-performing content.
- Determined the top five most-used hashtags to support marketing and brand visibility strategies.
- Identified the most effective day of the week for user registrations to optimize ad campaign timing.
- Calculated the average number of posts per user to assess overall platform engagement.
- Identified users who liked every photo on the platform, indicating abnormal or automated behavior.
- A small group of users demonstrates long-term retention, indicating strong platform loyalty.
- A significant number of users remain inactive, presenting opportunities for engagement campaigns.
- Highly engaging content drives disproportionate user interaction.
- Certain hashtags dominate platform usage and can be leveraged to maximize reach.
- User registrations peak on specific days, useful for marketing optimization.
- Abnormal liking patterns may indicate bot or fake accounts that skew engagement metrics.
This project strengthened practical SQL analytics skills, including joins, subqueries, aggregations, and business-driven analysis. It demonstrates the ability to work with relational datasets and generate actionable insights to support marketing and investment decision-making.
This is a self-initiated SQL analytics project developed for portfolio and learning purposes.