This project aims to analyze a Kaggle Netflix user base dataset to discover key trends in subscription type, revenues, and user retention while using Power BI to build an interactive dashboard that highlights the insights of the given data.
Data exploration and cleansing
By identifying missing values and different data types, I was able to ensure that each column has the correct data type and numeric values.
Calculating the revenue and user counts by subscription type
Q: Which subscription type contributes the most to the revenue, and what is each type's average revenue per user?
A: The output shows the revenue and user count for each subscription type, allowing you to identify which plans generate the most revenue.
Calculating the Churn Rate for each subscription type
Q: What is the churn rate for each subscription plan, and how does it differ from region to region?
A: The churn rate per plan helps identify the high-risk plans that might need targeted retention efforts.
The geographic revenue contribution by country and subscription type
Q: Which countries generate the most revenue and are specific subscription types more popular in certain regions?
A: It supports regional marketing strategies by highlighting profitable countries and preferred subscription plans.
Analyzing the device preference by subscription type
Q: How does device usage vary across different subscriptions, and what revenue is generated by each device?
A: Useful for developing targeted marketing or improving device-specific features to enhance user experience.
Calculating the average subscription duration plan
Q: What is the average duration of subscription per plan, and does any particular plan have a significantly shorter subscription length?
A: .
Tracking monthly new subscribers and churned accounts
Q: What are the monthly trends in new subscriptions and chum, and is there a seasonal pattern?
A: