https://github.com/samruddhi3012/rfm-analysis
Hi there! In this project I have performed Sales Analysis (RFM Analysis) using SQL and Tableau.
https://github.com/samruddhi3012/rfm-analysis
data-analysis data-visualization mssqlserver rfm-analysis segmentation tableau
Last synced: 12 months ago
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Hi there! In this project I have performed Sales Analysis (RFM Analysis) using SQL and Tableau.
- Host: GitHub
- URL: https://github.com/samruddhi3012/rfm-analysis
- Owner: samruddhi3012
- Created: 2024-07-24T14:24:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-26T17:46:46.000Z (about 1 year ago)
- Last Synced: 2025-06-02T22:47:31.512Z (about 1 year ago)
- Topics: data-analysis, data-visualization, mssqlserver, rfm-analysis, segmentation, tableau
- Homepage:
- Size: 39.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# RFM 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.
## :round_pushpin: Objectives
To identify high-value customers and at-risk churners through data-driven segmentation, enabling targeted marketing and inventory decisions.
## :round_pushpin: Tools and Technologies
* Data Source: [Dataset](https://www.kaggle.com/datasets/kyanyoga/sample-sales-data)
* Tools: Microsoft SQL Server, Tableau
* Keywords: **RFM Analysis**, **Segmentation Analysis**, **Data Visualization**, Data Interpretation, Data Manipulation, Tableau Dashboard
* Concepts: Common Table Expression, Window Functions, GROUP BY clause, Aggregate Functions, Various charts in Tableau
## :round_pushpin: Results
The key results from the analysis are presented as follows:
* _**Exploratory Analysis Results**_:
1. The company generated total revenue of **$10,032,629**.
4. There were **92** unique customers.
5. **Classic Cars** and **Vintage Cars** are the highest-selling products.
6. According to order status, **92%** of products were shipped, and 2% were canceled.
8. Yearly Sales Change:
1. Sales increased by **34.32%** from 2003 to 2004.
2. Sales dropped by **62.08%** from 2004 to 2005.
10. The top 3 revenue-generating countries are the USA, Spain, and France.
11. 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**.
## :round_pushpin: 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.
### _Thank you for visiting my repository!_