Segmentation of Potential and Churn-Risk Customer Using Recency, Frequency, and Monetary Analysis on Superstore Data

Project Background :

This project involves analyzing Superstore data to segment customers into potential and churn-risk categories using Recency, Frequency, and Monetary (RFM) metrics. The goal is to enhance marketing strategies and customer retention by understanding customer behavior and value. By examining purchasing patterns and calculating metrics such as Customer Lifetime Value (CLV) and Average Order Value (AOV), the project aims to provide actionable insights for more targeted, effective marketing efforts and increase profit

Key Questions :

1. How can we effectively segment customers to tailor marketing strategies and improve engagement?
2. What is the total value of customers over their lifetime, and how should this influence investment in customer acquisition and retention?
3. What is the average transaction value, and how can pricing and promotions be optimized to increase it?
4. How can we identify and prioritize potential customers versus those at risk of churning?
5. What behavioral patterns can be used to develop more effective marketing, retention and increase profit strategies?

Digital Asset

Dataset

Context :

With growing demands and cut-throat competitions in the market, a Superstore Giant is seeking your knowledge in understanding what works best for them. They would like to understand which products, regions, categories and customer segments they should target or avoid. You can even take this a step further and try and build a Regression model to predict Sales or Profit. Go crazy with the dataset, but also make sure to provide some business insights to improve

Google Collab

Process Of Exploratory Data Analysis :

1. Data Cleaning
2. Data Transformation
3. Data Reduction
4. Data Validation

Google Spreadsheet

Process Of Data Processing and Analysis :

1. Data splitting (Pivot)
2. RFM (Recency, Frequency and Monetary) Segmentation into Business Matrics Analysis

Business Matrics :

1. Segmentation using RFM (Recency: How recent the last purchase was, Frequency: How often purchases are made and Monetary: Total amount of money spent)
2. (CLV) Customer Lifetime Value
3. (AOV) Average Order Value
4. Segmentation Potensial Customer
5. Segmentation Churn-Risk
6. Profilling Demographics
7. Behavior of Potensial Customer and Potensial Churn Customer

Tableau Public

Process of Exploratory Data Visualization :

1. Visualization
2. Dashboard

Deck

Slide Of Presentation :

1. Project background
2. Overview
3. Data Understanding
4. Data Analysis
5. Visualization and Dashboard
6. Recommendations and Actionable insight