Project Overview

The RFM Analysis project represents an in-depth evaluation of customer behaviors, leveraging SQL and Tableau to derive meaningful insights. The project covers key aspects of customer segmentation, focusing on recency, frequency, and monetary value metrics to categorize customers effectively. Through this analysis, I aimed to identify customer groups for targeted marketing and to support business strategy through data-driven insights.

RFM Analysis in SQL

Using SQL, I performed RFM analysis on a provided dataset, restricting data to a single year for relevance. This involved calculating:

1. Recency: Days since the last purchase, using a set reference date.

2. Frequency: Number of distinct purchases by each customer within the timeframe.

3. Monetary Value: Total purchase value per customer.

Each metric was assigned a score based on quartile distributions, categorizing them into 1-4 scale values. This structured segmentation allowed me to group customers effectively

Customer Segmentation

Based on their RFM scores, customers were segmented into key groups:

• Champions: High scores in all RFM metrics, representing recent, frequent, and high-value buyers.

• Loyal Customers: Consistently high engagement and spending.

• Big Spenders: High monetary values but moderate frequency and recency.

• Lost Customers: Low engagement across all metrics.

• At-Risk: Reduced spending and frequency but recent purchase history, indicating potential to re-engage.

• Other Groups: Tailored categories, covering varied score combinations, allowing nuanced customer insights.

These segments provided a foundation for actionable recommendations tailored to customer value and behavior.

Tableau Dashboard

The Tableau dashboard visualizes customer distribution, highlighting each segment and key metrics, enabling quick identification of priority groups. Visuals include:

1. RFM Distribution: Showcases the spread of customers across different RFM scores.

2. Segment Performance: Analyzes contribution to revenue and engagement from each segment.

3. Insights for Targeting: Pinpoints high-value groups for loyalty programs and suggests re-engagement strategies for at-risk customers.

Insights and Recommendations

This project highlighted opportunities for enhanced customer engagement strategies:

1. Focus on Champions and Loyal Customers to strengthen retention through rewards.

2. Re-engage At-Risk Customers with targeted promotions to revitalize spending.

3. Consider Upsell Opportunities for Big Spenders by offering premium products.

Goals

1. Extract actionable insights on customer behavior through RFM analysis.

2. Segment customers based on recency, frequency, and monetary value to inform targeted marketing strategies.

3. Identify high-value customer groups for retention and loyalty initiatives.

4. Detect at-risk and lost customer segments for potential re-engagement.

5. Provide data-driven recommendations to enhance customer lifetime value.

6. Demonstrate proficiency in SQL and data visualization using Tableau.