This project aims to determine customer segmentation in the e-commerce sector using Python.
My purpose in addressing this problem stems from my experience in the e-commerce sector, where customers are often evaluated solely based on volume. In this project, we aim to consider not only the monthly volume but also 'recency,' 'frequency,' and 'monetary' values to provide a more comprehensive calculation.
An e-commerce company aims to segment its customers and develop marketing strategies tailored to each segment. The dataset "https://archive.ics.uci.edu/dataset/502/online+retail+ii " contains sales data from an online retail store based in the UK, covering the period from 01/12/2009 to 09/12/2011. Dataset: Online Retail II
Variables
1- InvoiceNo : Invoice number. A unique number assigned to each transaction. If it starts with "C," the transaction was canceled.
2- StockCode : Product code. A unique number assigned to each product.
3- Description : Product name.
4-Quantity: Number of products sold. Represents the quantity of each item per invoice.
5-InvoiceDate: Date and time of the invoice.
6-UnitPrice: Price of the product (in Sterling).
7-CustomerID: Unique customer identifier.
8-Country: Country name. The customer's country of residence.
This description establishes the problem context and provides details on the dataset that will be used for customer segmentation in e-commerce.
RFM Analysis is a customer segmentation technique that helps businesses understand and categorize their customers based on three key factors:
Recency (R): Measures how recently a customer has made a purchase. Customers who bought more recently are likely to engage again sooner, which indicates higher engagement or satisfaction.
Frequency (F): Measures how often a customer makes purchases. High-frequency customers demonstrate loyalty and are more likely to respond to marketing efforts or offers.
Monetary Value (M): Measures how much a customer spends, which helps identify high-value customers. Customers with higher spending are typically more valuable and can be prioritized in marketing strategies.
By analyzing these three aspects together, businesses can better tailor their marketing strategies to different customer groups, focusing on the most engaged, frequent, or valuable segments.
1- Marketing: Can develop personalized campaigns and promotions based on customer segmentation. This allows them to create marketing strategies tailored to different segments, increasing customer loyalty.
2- Sales: By understanding which customer groups generate more revenue, they can set more effective sales targets and focus efforts on high-value segments.
3- Customer Relationship Management (CRM): Segmenting customers based on past behaviors aids in developing strategies that enhance customer satisfaction and loyalty.
4- Product Management: Can analyze which products are more popular within certain segments to optimize the product range and improve inventory management.
5- Data Analytics and Business Intelligence: Plays a critical role in creating and analyzing segments, turning data into actionable insights. It supports strategic decision-making by sharing analysis results with other departments.
6- Finance: Can analyze revenue distribution across customer segments to create more accurate financial projections and prioritize investments.
Also, departments such as OPEX and Planning can leverage customer segments to ensure accurate stock management and effective planning.
** In this study, Miuul educational resources were used as a reference.