AtliQo 5G Impact Analysis

Overview

This project analyzes the impact of the 5G launch on AtliQo's key performance indicators (KPIs) by comparing metrics before and after the launch. The analysis focuses on revenue, average revenue per user (ARPU), active users, and churn rates, providing insights for informed decision-making.

Table of Contents

• Key Outcomes
• Data Sources
• Methodology
• Visualizations

Key Outcomes

• Data Acquisition and Preparation:

  • Loaded and explored multiple datasets from various CSV files.
  • Merged datasets to create a comprehensive view of AtliQo's performance.

• Comparative Analysis:

  • Segmented data into pre-5G and post-5G periods.
  • Aggregated metrics to create a comparison report.

• Key Performance Indicators:

  • Revenue Change: Analyzed revenue growth trends post-5G launch.
  • ARPU Analysis: Investigated changes in ARPU.
  • Active User Trends: Compared the number of active users before and after the launch.
  • Churn Rate Examination: Evaluated the increase in unsubscribed users.

• Insights and Recommendations:

  • Identified factors leading to the decline in active users and revenue post-5G.
  • Suggested optimizing internet plans based on user feedback.

Data Sources

The datasets used in this analysis include:

• dim_cities.csv: Contains information about cities.
• dim_date.csv: Contains information about the dates.
• dim_plan.csv: Contains details about the various internet plans.
• fact_atliqo_metrics.csv: Contains data about revenue, active users and churn users data.
• fact_market_share.csv: Contains market share data.
• fact_plan_revenue.csv: Contains different plans information.

Methodology

1. Data Loading: Used Python's pandas library to load datasets from CSV files.
2. Data Merging: Combined datasets from different dimensions to enrich the analysis.
3. Segmentation: Filtered data into pre-5G and post-5G segments for comparison, Used Machine Learning to interpret the Revenue growth and user engagement.
4. Aggregation: Calculated mean values for key metrics (revenue, ARPU, active users) using groupby().
5. Visualization: Created visualizations using Matplotlib and Seaborn to illustrate findings.

Visualizations

The following visualizations were created to support the analysis:

• Revenue Comparison: Bar charts comparing revenue changes before and after the 5G launch.
• ARPU Analysis: pie charts showing ARPU trends over time.
• Active Users Change: Bar charts visualizing changes in active user counts.
• Churn Rate Analysis: bar illustrating the changes in unsubscribed users.