Customer�Segmentation

Overview and Python Implementation

Roadmap

Theoritical foundation

What is customer segmentation?

Types of customer segmentation?

Problem Statement

Specific problem statement based on e-commerce data:

https://www.kaggle.com/datasets/carrie1/ecommerce-data/code?datasetId=1985&sortBy=voteCount

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Solution Approach

Discussion on solving the problem statement

Implementation

Implementation of the solution in Python

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Customer Segmentation

What is customer segmentation?

The process of dividing customers into groups based on common characteristics so companies can cater to each group effectively and appropriately

Customer Segmentation

Why segment customers?

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1. Learning about your customers on a deeper level so you can tailor your content to their unique needs and challenges
2. Creating targeted campaigns and ads to resonate with and convert segments of customers
3. Improving your customer service and customer support efforts by understanding and preparing for challenges different groups are likely to experience
4. Increasing customer loyalty with customized content and interactions
5. Understanding who your most valuable customers are and why
6. Communicating with segments of customers via preferred channel or platform.
7. Identifying new opportunities for products, support, and service efficiently

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Customer Segmentation

Types of customer segmentation:

Problem Statement

Come-up with a framework to flag high value, medium value and low value customers.

Data Available

Attribute Information:

1. InvoiceNo: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.
2. StockCode: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.
3. Description: Product (item) name. Nominal.
4. Quantity: The quantities of each product (item) per transaction. Numeric.
5. InvoiceDate: Invoice Date and time. Numeric, the day and time when each transaction was generated.
6. UnitPrice: Unit price. Numeric, Product price per unit in sterling.
7. CustomerID: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.
8. Country: Country name. Nominal, the name of the country where each customer resides.

Dataset Link:

https://www.kaggle.com/datasets/carrie1/ecommerce-data

Solution Approach

Create metrics to gauge recency, frequency and monetary at customer level

Apply a clustering algorithm to cluster customers as low, medium and high value

High

Low

Medium

Implementation