Customer churn analysis - 10/2023

Utilised Power Query, DAX, and visualisation tools within Power BI to analyse a fictional telecom company's dataset, examining customer churn patterns and facilitating data-driven insights for retention strategies.

Power BI Power Query DAX Data Analysis Data visualisation

Final report

Overview

Purpose

Investigate the underlying reasons for customer churn for a fictional telecom company through analysing its dataset.

Steps

  • Data check
  • Explore data
  • Analyse and visualise data
  • Dashboarding
  • Communicate insights

Tasks details

Data check

  • Create 2 measures to check if the count of customer ids is equal to the count of unique customer ids and remove duplicate rows if exist.

Explore data

  • Calculate churn: Number of Churned Customers, Churn Rate
  • Investigate churn reasons
  • Investigate churn categories
  • Investigate churn by state

Investigate churn patterns

  • Analyse demographics: age groups, age bins, gender
  • Inspect multiple fields: contract category, extra services, usage of those services
  • Investigate topics related to customers

Collate information and create a report

  • Organise the visualisations created in the previous steps into new pages:
    • Overview
    • Age Groups, Payment and contract
    • Extra charges
    • Other Insights

Publish

The final report and dashboard is published to Power BI Service and shared as at the top of this page. Below are the links to the report on Power BI Service, and its static PDF version 👇