Instagram User Analytics Project

Welcome to the Instagram User Analytics project! This repository hosts a comprehensive analysis of user behavior and engagement on the Instagram platform. The insights generated through this project aim to assist various teams across our business, including marketing, product, and development, in making informed decisions to enhance user experience and drive business growth.

Project Description

The primary objective of this project is to derive meaningful insights from Instagram user data that can be utilized to launch effective marketing campaigns, make informed decisions about app features, measure the success of the app through user engagement metrics, and ultimately contribute to the overall improvement of user experience on the platform.

Tech stacks used

Approach

I have employed a structured approach to achieve my project goals:

1. Database Creation: I started by creating a MySQL database using Data Definition Language (DDL) and Data Manipulation Language (DML) SQL queries. The provided data was inserted into the database using MySQL Workbench.

2. Insight Extraction: I extracted valuable insights from the database by running SQL queries within MySQL Workbench. These insights cover a range of topics, including user demographics, engagement metrics, posting behavior, and more.

Tasks and Outputs

My analysis has encompassed several key tasks:

Task 1: Identifying Oldest Users

I've identified the five oldest users on Instagram based on their account creation dates.

Task 2: Non-Active Users

I've compiled a list of users who have never posted any photos on Instagram.

Task 3: Top Liked Users

I've identified the users with the highest number of likes on their posts, which could be potential influencers for marketing collaboration.

Task 4: Popular Hashtags

I've determined the top five most commonly used hashtags on the platform, offering insights for targeted content creation.

Task 5: User Registration Insights

I've analyzed user registration patterns to pinpoint the most popular days for user sign-ups, aiding in optimal ad campaign scheduling.

Task 6: User Posting Behavior

I've calculated the average posting frequency per user and the total number of photos on Instagram per user.

Task 7: Bot Detection

I've identified users who have liked every single photo on the site, which could potentially indicate automated or bot-like behavior.

Recommendations

Based on my insights, I recommend the following actions:

• Leverage the identified influencers with high likes for potential marketing collaborations.
• Utilize the most commonly used hashtags in marketing campaigns to increase content visibility.
• Schedule ad campaigns on days with high user registration rates for better reach.
• Focus on engaging content types and posting frequency based on user behavior.

Future Steps

To further enhance the impact of my analysis, I plan to:

• Perform sentiment analysis on user comments to gauge user sentiment.
• Explore user segmentation for targeted campaigns and feature enhancements.
• Compare our engagement metrics with competitors to drive innovation.
• Investigate user churn points and devise strategies to improve user retention.

I invite you to explore the detailed insights and outputs provided in this repository. Feel free to use these findings to inform your decision-making processes and contribute to the growth and success of my Instagram platform.

For any inquiries or suggestions, please contact Ananwita Sarkar (ananwitasarkar@gmail.com).

Disclaimer: The data and insights provided in this project are based on a specific dataset and context. It is important to consider the project's limitations and conduct further analysis as needed for comprehensive decision-making.