📊 Shield Insurance – Business Performance Analysis (Power BI)

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📌 Table of Contents

• Project Overview
• Tasks Performed
• Key Insights
• Data Model
• Dashboards Preview
• Tools & Technologies
• Skills Demonstrated
• Conclusion
• Explore More
• Connect With Me

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🧠 Project Overview

This project is a Power BI case study developed for Shield Insurance, focused on analyzing overall business performance, sales channels, and customer demographics.

The objective was to design an end-to-end BI solution that provides clear insights into:

• Revenue growth and customer trends
• Sales performance by mode (offline/online)
• Policy contribution and profitability
• Demographic-based performance and claim settlement patterns

This project empowers business stakeholders to make data-driven decisions for optimizing sales strategies and improving customer retention.

👉 Live Dashboard: Shield Insurance Power BI Dashboard

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🧩 Tasks Performed

🔹 1. Data Preparation (Power Query)

• Cleaned and transformed raw data from Excel sources.
• Handled missing values, standardized naming conventions, and ensured data consistency.
• Added custom columns for Month-Year, Policy Mapping, and Premium Grouping.

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🔹 2. Data Modeling

• Designed a Star Schema Model with two Fact tables and three Dimension tables for efficient analytics.
• Fact Tables:
  • fact_premiums → contains premium transaction data (policy_id, customer_code, sales_mode, revenue, and date).
  • fact_settlements → includes claim settlement details (settlement %, age, and age group).
• Dimension Tables:
  • dim_customer → holds demographic and customer-level data (customer_code, city, DOB, Age, AgeGroup).
  • dim_policies → stores policy details (policy_id, coverage, base premium).
  • dim_date → a calendar table for time intelligence functions.
• Created Age and AgeGroup DAX columns inside fact_settlements to establish a relationship with dim_customer.
• Ensured accurate one-to-many relationships for seamless cross-filtering.

📊 Data Model Preview:

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🔹 3. DAX Measures & Calculated Columns

• Built DAX measures for KPIs including:
  • Total Revenue, Average Premium, Customer Count, Settlement %, and MoM Growth.
• Created DAX calculated columns:
  • Age and AgeGroup in fact_settlements to relate to dim_customer.
• Developed Dynamic Titles using DAX to make visuals context-aware based on user selections.
• Optimized measure dependencies for report performance.

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🔹 4. Dashboard Design

• Created three interactive dashboards:
  • Dashboard 1 – General Overview: Company KPIs, revenue & customer growth summary.
  • Dashboard 2 – Sales Mode Analysis: Revenue and customer contribution across different sales modes.
  • Dashboard 3 – Age Group Analysis: Demographic impact on revenue, premium payments, and claim settlements.
• Implemented Bookmarks and Selection Pane for visual transitions between revenue and customer trends.
• Added Home Page, Support Page, and Instruction Manual for easy navigation and user guidance.

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💡 Key Insights

✔️ The 30–39 age group generated the highest revenue (₹335M+).
✔️ Offline Agents contributed 55%+ of total revenue, though online channels show strong growth potential.
✔️ March 2023 was the highest-performing month, driven by renewal campaigns.
✔️ Delhi NCR and Mumbai are the most profitable regions.
✔️ POL4321HEL is the most popular plan across all demographics.
✔️ Seniors (60+) have higher settlement ratios, while young customers (18–29) drive long-term business stability.

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🖼️ Dashboards Preview

Dashboard 1 – General Overview

Dashboard 2 – Sales Mode Analysis

Dashboard 3 – Age Group Analysis

👉 Try Live Interactive Dashboard

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🧰 Tools & Technologies

• Power BI Desktop – Data Modeling, DAX, and Visualization
• Power Query – ETL and data transformation
• Excel – Source and validation data
• Power BI Service – Publishing and sharing dashboards

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🧠 Skills Demonstrated

• Data Cleaning & Transformation (Power Query)
• Star Schema Data Modeling
• DAX (Measures, Columns, and Dynamic Titles)
• Storytelling & Dashboard Design
• UX-focused Power BI Navigation (Bookmarks & Buttons)

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🏁 Conclusion

This project demonstrates how Power BI can transform raw insurance data into actionable insights by:

• Connecting customer demographics to business KPIs.
• Identifying high-value sales modes and customer segments.
• Enabling data-driven decisions to balance profitability and customer satisfaction.

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🔗 Explore More

💼 Check out my full analytics portfolio here:
👉 https://codebasics.io/portfolio/Mohammad-Navaman-Jamadar

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🤝 Connect With Me

📧 Email: noumanjamadar123@gmail.com
🔗 LinkedIn: www.linkedin.com/in/mohammad-navaman-jamadar

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