As part of my data analytics portfolio, I developed an end-to-end Customer Retention & Sales Analytics Dashboard using Microsoft Power BI. This project demonstrates my ability to transform raw transactional data into meaningful business insights that support customer retention strategies, customer engagement initiatives, and revenue growth.
The dashboard was designed for Sales and Customer Success teams to monitor customer purchasing behavior, identify high-value customers, track order trends, and detect inactive customers who may be at risk of churn. By leveraging data visualization and business intelligence techniques, the solution enables stakeholders to make informed decisions that improve customer loyalty and maximize sales performance.
The dataset contains customer and order transaction records, including key variables such as: Customer ID, Customer Name, Order ID, Order Date, Product Information, Quantity Purchased, Sales Revenue, Customer Activity Status, Purchase Frequency, Customer Lifetime Value Metrics
- Power Query – Data cleaning and transformation
- Power BI – Data modeling, DAX calculations, and dashboard development
- Removed duplicate records and handled missing values.
- Standardized data formats and ensured consistency across customer and transaction records.
- Created relationships between relevant tables to support accurate reporting.
Developed calculated fields to enhance business analysis, including:
- Customer Segments
- Purchase Frequency Categories
- Customer Activity Status
- Revenue Contribution Levels
- Retention Classification Metrics
Created dynamic measures to support KPI tracking, including:
- Total Revenue
- Total Orders
- Average Order Value
- Customer Count
- Active Customers
- Inactive Customers
- Customer Retention Rate
- Customer Lifetime Value (CLV)
- Revenue per Customer
The objective of this analysis was to answer critical business questions, including:
- Which customers generate the highest revenue?
- How frequently do customers make purchases?
- What percentage of customers are active versus inactive?
- Which customer segments contribute most to overall revenue?
- How do purchasing patterns change over time?
- Which customers may require retention efforts?
- How can customer engagement be improved to increase sales?
The dashboard was designed with a focus on usability, business storytelling, and actionable insights through:
- KPI Cards for customer and sales performance
- Customer Segmentation Analysis
- Revenue Trends and Sales Performance Monitoring
- Active vs Inactive Customer Analysis
- Customer Purchase Frequency Analysis
- High-Value Customer Identification
- Interactive Filters and Drill-Through Features
- A small group of high-value customers contributes a significant percentage of total revenue.
- Several customers have become inactive, indicating opportunities for retention campaigns.
- Repeat customers generate substantially more revenue than one-time purchasers.
- Customer engagement levels vary across segments, highlighting opportunities for personalized marketing strategies.
- Revenue trends reveal periods of increased customer activity that can inform future sales initiatives.
- Develop targeted retention campaigns for inactive customers.
- Introduce loyalty and reward programs to encourage repeat purchases.
- Focus marketing efforts on high-value customer segments.
- Implement personalized communication strategies based on purchasing behavior.
- Monitor customer activity regularly to identify churn risks early.
- Create cross-selling and upselling initiatives for engaged customers.
This project demonstrates my ability to:
- Transform raw customer transaction data into actionable business intelligence.
- Design interactive dashboards that support strategic decision-making.
- Apply DAX and data modeling techniques to solve business problems.
- Use data storytelling to communicate insights effectively.
- Support customer retention and revenue optimization through analytics.
This Customer Retention & Sales Analytics Dashboard showcases my skills in data cleaning, data modeling, visualization, and business intelligence using Power BI. The project highlights how data analytics can be leveraged to understand customer behavior, improve customer engagement, strengthen retention strategies, and drive sustainable revenue growth.