- 🚀 Project Overview
- 🎯 Objective
- 🧠 Skills Demonstrated
- 🛠️ Tools & Technologies
- 🧹 Data Cleaning Summary
- 📊 Key DAX Measures
- 📈 Dashboard Walkthrough
- 🔍 Insights & Recommendations
- 💡 What Should Change?
- 📁 Project Files
- 🙏 Acknowledgment
- 🔗 Let’s Connect!
This project simulates a real-world case study for PhoneNow, a telecom provider, focused on customer churn analysis and retention strategy using Power BI. The goal is to visualize patterns, identify high-risk customers, and support proactive retention decisions.
This project was completed as part of a professional upskilling challenge provided by PwC Switzerland to help learners develop strong, real-world analytics capabilities. 🙌
Customers are the heart of telecom businesses, and losing them can be costly. This dashboard was designed to:
- Detect early signals of churn
- Identify at-risk segments like short-tenure users or senior citizens
- Reveal service and contract types most correlated with churn
- Empower decision-makers with actionable visual insights
✅ Power BI Dashboard Design
✅ Power Query – Data Cleaning
✅ DAX – KPI and Measure Calculation
✅ Analytical Storytelling
✅ Business-Driven Insights
- Power BI Desktop
- Power Query (ETL)
- DAX (Data Analysis Expressions)
- Excel (for raw dataset)
- PDF (for brief/project brief understanding)
Using Power Query, I:
- Corrected data types and handled blanks
- Removed duplicates
- Created calculated columns:
ChurnFlag,RiskCategory,TenureGroup - Formatted data for Power BI visuals
TotalCustomers = DISTINCTCOUNT('01 Churn-Dataset'[CustomerID])
ChurnRate = DIVIDE(SUM('01 Churn-Dataset'[Churn Flag]), COUNT('01 Churn-Dataset'[CustomerID])) * 100
RevenueLoss = SUMX(
FILTER('01 Churn-Dataset', '01 Churn-Dataset'[Churn Flag] = 1),
'01 Churn-Dataset'[MonthlyCharges]
)
RetentionRate = 100 - [ChurnRate]
HighRiskCustomers =
CALCULATE(
COUNTROWS('01 Churn-Dataset'),
'01 Churn-Dataset'[RiskCategory] = "High Risk"
)
5. [🧹 Data Cleaning Summary](#-data-cleaning-summary)
6. [📊 Key DAX Measures](#-key-dax-measures)
7. [📈 Dashboard Walkthrough](#-dashboard-walkthrough)
- [Page 1 – Executive Summary](#-page-1--executive-summary)
- [Page 2 – Demographics & Behavior](#-page-2--demographics--behavior)
- [Page 3 – High-Risk Customer Focus](#-page-3--high-risk-customer-focus)
8. [🔍 Insights & Recommendations](#-insights--recommendations)
9. [💡 What Should Change?](#-what-should-change)
10. [📁 Project Files](#-project-files)
11. [🙏 Acknowledgment](#-acknowledgment)
12. [🔗 Let’s Connect!](#-lets-connect)
---
## 🚀 Project Overview
This project simulates a real-world case study for **PhoneNow**, a telecom provider, focused on **customer churn analysis** and **retention strategy** using **Power BI**. The goal is to visualize patterns, identify high-risk customers, and support proactive retention decisions.
This project was completed as part of a professional upskilling challenge provided by **PwC Switzerland** to help learners develop strong, real-world analytics capabilities. 🙌
---
## 🎯 Objective
Customers are the heart of telecom businesses, and losing them can be costly. This dashboard was designed to:
- Detect early signals of churn
- Identify at-risk segments like short-tenure users or senior citizens
- Reveal service and contract types most correlated with churn
- Empower decision-makers with actionable visual insights
---
## 🧠 Skills Demonstrated
✅ Power BI Dashboard Design
✅ Power Query – Data Cleaning
✅ DAX – KPI and Measure Calculation
✅ Analytical Storytelling
✅ Business-Driven Insights
---
## 🛠️ Tools & Technologies
- **Power BI Desktop**
- **Power Query (ETL)**
- **DAX (Data Analysis Expressions)**
- **Excel** (for raw dataset)
- **PDF** (for brief/project brief understanding)
---
## 🧹 Data Cleaning Summary
Using Power Query, I:
- Corrected data types and handled blanks
- Removed duplicates
- Created calculated columns: `ChurnFlag`, `RiskCategory`, `TenureGroup`
- Formatted data for Power BI visuals
---
## 📊 Key DAX Measures
```DAX
TotalCustomers = DISTINCTCOUNT('01 Churn-Dataset'[CustomerID])
ChurnRate = DIVIDE(SUM('01 Churn-Dataset'[Churn Flag]), COUNT('01 Churn-Dataset'[CustomerID])) * 100
RevenueLoss = SUMX(
FILTER('01 Churn-Dataset', '01 Churn-Dataset'[Churn Flag] = 1),
'01 Churn-Dataset'[MonthlyCharges]
)
RetentionRate = 100 - [ChurnRate]
HighRiskCustomers =
CALCULATE(
COUNTROWS('01 Churn-Dataset'),
'01 Churn-Dataset'[RiskCategory] = "High Risk"
)
)
## 🚀 Project Overview
This project simulates a real-world case study for **PhoneNow**, a telecom provider, focused on **customer churn analysis** and **retention strategy** using **Power BI**. The goal is to visualize patterns, identify high-risk customers, and support proactive retention decisions.
This project was completed as part of a professional upskilling challenge provided by **PwC Switzerland** to help learners develop strong, real-world analytics capabilities. 🙌
---
## 🎯 Objective
Customers are the heart of telecom businesses, and losing them can be costly. This dashboard was designed to:
- Detect early signals of churn
- Identify at-risk segments like short-tenure users or senior citizens
- Reveal service and contract types most correlated with churn
- Empower decision-makers with actionable visual insights
---
## 🧠 Skills Demonstrated
✅ Power BI Dashboard Design
✅ Power Query – Data Cleaning
✅ DAX – KPI and Measure Calculation
✅ Analytical Storytelling
✅ Business-Driven Insights
---
## 🛠️ Tools & Technologies
- **Power BI Desktop**
- **Power Query (ETL)**
- **DAX (Data Analysis Expressions)**
- **Excel** (for raw dataset)
- **PDF** (for brief/project brief understanding)
---
## 🧹 Data Cleaning Summary
Using Power Query, I:
- Corrected data types and handled blanks
- Removed duplicates
- Created calculated columns: `ChurnFlag`, `RiskCategory`, `TenureGroup`
- Formatted data for Power BI visuals
---
## 📊 Key DAX Measures
```DAX
TotalCustomers = DISTINCTCOUNT('01 Churn-Dataset'[CustomerID])
ChurnRate = DIVIDE(SUM('01 Churn-Dataset'[Churn Flag]), COUNT('01 Churn-Dataset'[CustomerID])) * 100
RevenueLoss = SUMX(
FILTER('01 Churn-Dataset', '01 Churn-Dataset'[Churn Flag] = 1),
'01 Churn-Dataset'[MonthlyCharges]
)
RetentionRate = 100 - [ChurnRate]
HighRiskCustomers =
CALCULATE(
COUNTROWS('01 Churn-Dataset'),
'01 Churn-Dataset'[RiskCategory] = "High Risk"
)
- KPI cards for Total Customers, Churn Rate, Monthly Revenue Loss
- Churn by Contract, Internet Services, Payment Methods
- Monthly trend line
- Churn patterns by Senior Citizen, Partner, Dependent
- Internet and support services usage
- Dynamic table of high-risk profiles
- Filters to slice by service and customer demographics
- Month-to-month customers churn more than others — consider loyalty incentives
- Customers without tech support are highly likely to leave
- Short-tenure (≤ 6 months) customers are most at risk — target them early
- Senior citizens show different patterns — tailor outreach accordingly
- Shift from reactive retention to predictive retention using insights like "RiskCategory"
- Integrate churn scores into CRM tools
- Enhance customer support and bundled services
📊 Customer Call Analysis.pbix– Interactive Power BI report📄 02 Churn-Dataset.xlsx– Dataset used📃 PhoneNow inputs.pdf– Project brief
Big thanks to PwC Switzerland for designing this realistic telecom analytics task to boost practical learning. It sharpened my Power BI, data storytelling, and business decision-making skills. 💼📊
📧 *jmercy306@gmail.com *
🔗 LinkedIn
⭐ If you found this helpful, give it a star!
---
Would you like me to save this into a `README.md` file for direct upload to your GitHub repo? Also, feel free to drop your LinkedIn/portfolio link and email if you'd like me to plug those in too!