RFM Sales Analysis

RFM (Recency, Frequency, Monetary) sales analysis is a marketing technique used to evaluate and segment customers based on their purchasing behavior.

This method helps businesses identify their most valuable customers and tailor marketing strategies accordingly.

📍 Objectives

The goals of this project are to perform RFM analysis and Segmentation analysis on the given dataset and build an interactive Sales Analytics dashboard using Tableau.

📍 Tools Used

The dataset 🖥️ used in this project is obtained from kaggle.

• Tools : Microsoft SQL Server, Tableau
• Keywords: RFM Analysis, Segmentation Analysis, Data Visualization, Data Interpretation, Dasboard
• Concepts: Common Table Expression, Window Functions, GROUP BY clause, Aggregate Functions, Various charts in Tableau

📍 Results

The key results from the analysis are presented as follows:

• Preliminary Analysis Results:

  1. The company generated total revenue of $10,032,629.
  2. There were 92 unique customers.
  3. Classic Cars and Vintage Cars are the highest-selling products.
  4. According to order status, 92% of products were shipped, and 2% were canceled.
  5. Yearly Sales Change:
     1. Sales increased by 34.32% from 2003 to 2004.
     2. Sales dropped by 62.08% from 2004 to 2005.
  6. The top 3 revenue-generating countries are the USA, Spain, and France.
  7. The 4th quarter has the highest product sales, with 38.62%.

• RFM Analysis Results:
   From customer segmentation, it was concluded that 15% of customers were loyal, 18% were potential churners, and 22% of customers were lost.

📍 Description

1. Preliminary Analysis
   Conducted a comprehensive analysis to understand key sales metrics:

• Total Sales & Orders:
    Analyzed total sales and order count across countries.
• Order Frequency & Unique Customers:
    Identified the most frequent ordering country and unique customer count.
• Product & Deal Size Analysis:
   Evaluated sales performance of products and various deal sizes.
• Order Status Distribution:
    Assessed distribution of order statuses.
• Top Performance Metrics:
• Best selling year and month.
• Year-over-year revenue changes.
• Highest selling products in peak months and by city/country.
• Top revenue-generating countries and cities.
• Sales trends by territories, quarters, and yearly product performance.
• Yearly sales differences for specific products.

2. RFM Analysis
   Utilized RFM (Recency, Frequency, Monetary) analysis to segment and understand customer behavior:

• Best Customers Identification:
   Identified top customers based on purchase patterns.
• Customer Segmentation:
   Grouped customers into segments such as best, loyal, and at-risk customers.
• Segment Distribution:
   Calculated the percentage of customers in each segment.
• Product Combinations:
   Analyzed frequently bought together products for bundling opportunities.

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