Back

Telecom Customer Churn

Introduction

Telecom Customer Churn is the shift of customers from one telecom service provider to its competition.

This could be due to service dissatisfaction, costly subscription or better alternatives.

In this project, analysis of the telecom churn data helps to determine the factors that lead to customer churning and proffer solution to mitigate the churn rate

Explore Dashboard

Project Objectives

  • To use Python to explore and analyze the telecom customer churn data and extract insight into the underlying causes of churn
  • To build an interactive dashboard in Power BI that shows top level KPI’s and customers profile based on churn-rate analysis

Project Links

Github

Power BI

.

Tools used

  • Microsoft Power BI
  • Python

Skills Utilized

  • Data Cleaning
  • Feature Engineering
  • Data Analysis
  • Data Visualization
  • Dashboard & Storytelling

Insights

Dashboard 1

KPI Analysis

  • The telecom had a total of 7,043 customers out of which 1,869 customers were churned, resulting in a 26.5% churn rate
  • 454 customers joined and the telecom had a retention rate of 67%
  • A total revenue of $21,371,131 was generated out of which $3,684,459 was loss due to churn which amount to 17% revenue churn.
  • 13% of churned customers were high-valued customers

Dashboard 1

Churn Analysis

  • 88.5% of the churned customers subscribed to a month-to-month contract.
  • 53% of the churned customers left in less than a year, of which 20% were churned in one month.
  • 56% had no offer of any type maybe because there were no better offer type available.
  • 62% paid more than the average monthly charges. This could be one of the key driver for churn.
  • The top three reasons for churn were; competitors had better device, better offer, and support persons had a bad attitude

Dashboard 2

Customer Profile Analysis

  • 88.5% of the churned customers subscribed to a month-to-month contract.
  • 53% of the churned customers left in less than a year, of which 20% were churned in one month.
  • 56% had no offer of any type maybe because there were no better offer type available.
  • 62% paid more than the average monthly charges. This could be one of the key driver for churn.
  • The top three reasons for churn were; competitors had better device, better offer, and support persons had a bad attitude

Recommendation

  • The monthly contract should be upgraded to a six months contract, this may lead to low churn rate.
  • A reduction in monthly charges may lower the churn rates as 56% of churn customers payed above average rate.
  • Upgrading to a better device and providing better offer types than the competitions can increased rentention.
  • Implementing a loyalty reward program can help retain high valued customers
  • Re-training of the support staff on customer management will help resolve customers problems and in-turn increase retention rate