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This project leverages Python (Pandas, Prophet, Matplotlib) to track segment-wise performance, calculate kebele-level market penetration rates, and deliver a 12-month forecast. Provides actionable geographic and segment-specific insights for resource allocation and and strategic planning.

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Telecom Subscriber Growth & Penetration Analysis

Project Overview

This project simulates a comprehensive data analytics pipeline for a telecom provider operating in the Wolaita Sodo district, Ethiopia. The goal is to track, analyze, and forecast subscriber growth across different kebeles (localities) and key customer segments (Individual Users, Businesses, Telebirr Adopters).

The analysis transforms raw acquisition and churn data into actionable business intelligence to guide strategic investment and marketing focus.

Business Problem

The company needs a data-driven solution to understand the spatial and behavioral dynamics of its subscriber base. The analysis aims to answer critical questions:

  1. Market Saturation: Which kebeles are nearing saturation, and where is the greatest opportunity for new subscriber acquisition?
  2. Growth Drivers: Which customer segments and geographic areas are driving the highest Net Growth and MoM/YoY rates?
  3. Forward Planning: What is the reliable 12-month forecast for total subscriber growth to inform budget and infrastructure decisions?

Technical Stack & Methodology

This project demonstrates expertise across the entire data science lifecycle, from simulation to visualization.

Category Tools & Libraries Skills Demonstrated
Data Simulation Python, pandas, numpy, random Generating realistic time-series data with trend, seasonality, and geographic bias.
Data Wrangling Python, pandas Feature Engineering: Calculating Cumulative Subscribers, Market Penetration Rate, and Month-over-Month (MoM) Growth.
Time Series & Forecast Python, Prophet (Meta) Applying a robust model to decompose trends and forecast the next 12 months of cumulative subscribers.
Visualization Python, matplotlib, seaborn Creating a multi-panel, high-fidelity Dashboard for executive reporting and geographic comparison.

Key Findings & Deliverables

1. Geographic Penetration Map

Deliverable: A comparison of Market Penetration Rate across 15 kebeles.

  • Insight: Urban areas like Fana Womba show > 80% penetration, indicating saturation, while Periphery kebeles like Humbo-Aba offer high untapped potential.
  • Action: Recommend shifting acquisition resources from saturated Urban centers to high-potential Periphery kebeles where the Penetration Rate is lowest.

2. Segment Performance & Trend

Deliverable: Time series analysis of segment growth.

  • Insight: The Telebirr Adopter segment saw an 20% spike in growth in Year 2, confirming the success of the recent digital push."
  • Action: Continue investment in digital financial services infrastructure to maintain the momentum of the fastest-growing segment.

3. 12-Month Forecast

Deliverable: Predicted cumulative subscriber count for the next year.

  • Insight: The model forecasts a total subscriber count of 3500 by December 2025, providing a clear target for capacity planning.
  • Action: Use the predicted figures and confidence intervals for budgeting and network capacity expansion.

Developed by: Aklilu Abera

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This project leverages Python (Pandas, Prophet, Matplotlib) to track segment-wise performance, calculate kebele-level market penetration rates, and deliver a 12-month forecast. Provides actionable geographic and segment-specific insights for resource allocation and and strategic planning.

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