This project is an end-to-end Power BI dashboard built for Shield Insurance as part of the Codebasics Virtual Internship.
The report answers key business questions in seconds by tracking Total Revenue, Total Customers, DRG/DCG (Daily Revenue & Customer Growth), monthly trends, and customer segmentation across city, sales mode, age group, and policy ID.
Shield Insurance provides comprehensive insurance plans for individuals and businesses. This dashboard is designed to help stakeholders quickly understand:
- how revenue and customer growth is trending month-over-month,
- where customers are coming from (city + demographic segments),
- how different sales modes contribute to revenue and customer acquisition,
- which age groups prefer which policies and channels.
Note: Values are shown in Millions & Thousands across the report.
- What are the Total Revenue and Total Customers this month vs last month?
- How are DRG/DCG changing (daily growth indicators)?
- What are the monthly trends for revenue and customers?
- Which cities, age groups, and policies contribute most?
- Which sales modes (Agent/App/Direct/Website) drive revenue vs customers?
- How do age group behaviors differ by policy preference, sales mode, and expected settlements?
The Overview page highlights:
- Revenue (with Last Month and % change)
- Customers (with Last Month and % change)
- DRG — Daily Revenue Growth
- DCG — Daily Customer Growth
Example shown for Jan_23:
- Revenue: ₹141.0M (LM: 156M, %Chg: -9.79%)
- Customers: 3.9K (LM: 4K, %Chg: -2.51%)
- DRG: ₹4.5M (LM: 5.04M, %Chg: -9.79%)
- DCG: 126.4 (LM: 129.68, %Chg: -2.51%)
A business snapshot with:
- KPI cards (Revenue, Customers, DRG, DCG)
- Monthly trends (Revenue & Customers toggle)
- Customer split by Age Group
- Revenue split by City
- Detailed segmentation table (City + Age Group + Customers + Revenue)
- Filters for City, Medium, Mode, Policy ID, Month
📌 Add screenshot:
This page breaks down performance by channel:
- Total Revenue % by Sales Mode
- Total Customers % by Sales Mode
- Monthly trend lines for each mode (Agent, App, Direct, Website)
- Filters for Month, Policy ID, Mode, Medium, City
📌 Add screenshot:
This page focuses on customer demographics and behavior:
- Trends by age group across months
- Age Group vs Policy Preference (policy-wise distribution)
- Age Group vs Sales Mode
- Customers by Age Group
- Age Group vs Expected Settlements distribution
- Filters for City, Medium, Mode, Policy ID, Month
📌 Add screenshot:
- City-level contribution to revenue and customers (e.g., top city segments visible in Revenue Split table)
- Age-group contribution and customer distribution
- Clear separation of “summary → deep-dive views” via dedicated pages for:
- Sales Mode insights
- Age Group insights
- Imported the dataset into Power BI
- Cleaned and structured the data in Power Query
- Built a clean data model to support cross-filtering across:
- City, Age Group, Policy ID, Mode, Medium, Month
- Created DAX measures for:
- Total Revenue, Total Customers
- Last Month comparisons (LM)
- % Change vs LM
- DRG / DCG indicators
- Designed a 3-page interactive report:
- Overview (KPIs + trends + segmentation)
- Sales Mode analysis
- Age Group analysis
- Power BI (dashboard design + storytelling)
- Power Query (data cleaning & transformation)
- DAX (KPIs, LM comparisons, DRG/DCG, % change)
- Data modeling (filter flow + segmentation-ready structure)
- Business reporting (trend + segmentation + drill-ready layouts)
This dashboard helps:
- Leadership monitor revenue and customer growth quickly (with LM and % change)
- Sales teams understand which channels drive revenue vs customer acquisition
- Marketing teams target the right segments by city and age group
- Operations track performance trends monthly and react faster to dips
- Stakeholders explore performance interactively using slicers (city, mode, policy, month)