This project delivers an interactive Power BI dashboard designed to analyze insurance data, focusing on customer behavior, claim patterns, and sentiment analysis. The dashboard provides a comprehensive overview of key performance indicators (KPIs), enabling data-driven decision-making for improved customer satisfaction and operational efficiency. By leveraging a variety of visualizations and interactive elements, this project transforms raw data into actionable insights.
- Interactive Slicers:
- Policy Type: Filter data by different insurance policy types.
- Customer ID: Focus on specific customer data.
- Claim Number: Isolate individual claim details.
- Key Performance Indicator (KPI) Cards:
- Premium Amount: Total premium collected.
- Coverage Amount: Total coverage provided.
- Claim Amount: Total amount paid out in claims.
- Other relevant metrics, including multi-row cards for detailed breakdowns.
- Visualizations:
- Ribbon Chart: Claim count categorized by claim status over time.
- Bar Chart: Premium amount distribution across different policy types.
- Line Chart: Average claim amount trends across different age groups.
- Donut Chart: Proportion of active versus inactive policies.
- Matrix Visual: Providing detailed cross-tabular analysis of various data points.
- Drill-Through Functionality: Enables in-depth exploration of specific data points, allowing users to move from high-level summaries to granular details.
- Sentiment Analysis: Analysis of customer feedback performed to categorize and summarize sentiment indicators derived from text data.
- Clear Text Boxes: Providing relevant context and explanations throughout the dashboard.
The data utilized in this project is an anonymized dataset comprising:
- Customer policy information (policy type, demographics).
- Claims data (claim amount, claim date, claim status).
- Customer feedback (text reviews).
- Power BI Desktop: For data modeling, visualization, and dashboard creation.
- SQL: Used for data extraction, transformation
- Slicers: Enabled flexible filtering by Policy Type, Customer ID, and Claim Number, allowing for granular analysis.
- Cards: Provided immediate insight into key financial metrics like Premium Amount, Coverage Amount, and Claim Amount.
- Ribbon Chart: Visualized the evolution of claim status over time.
- Bar Chart: Compared premium amounts for each type of policy, showing which policy types generate the most revenue.
- Line Chart: Identified age groups with the highest average claim amounts.
- Donut Chart: Showed the current ratio of active to inactive policies.
- Sentiment Analysis: Analyzed customer sentiment regarding claims processing, highlighting areas for improvement.
- Discovered correlations between customer demographics and policy preferences.
- Detailed the distribution of customer policies.
- Dashboard Overview:
- Claim Analysis:
- Sentiment Analysis:
