Customer Segmentation for E-commerce Company by Nara NguyenCustomer Segmentation for E-commerce Company by Nara Nguyen

Customer Segmentation for E-commerce Company

Nara Nguyen

Data Scientist

Data Analyst

A. Project Objective:

To segment customers of an e-commerce company into distinct groups based on their purchasing behavior, using the RFM (Recency, Frequency, Monetary) model. This segmentation helps in identifying different customer types for targeted marketing strategies.

B. Methodology:

1. Data Collection:

Gathered transaction data including purchase dates, frequency, and total spending from the e-commerce platform in last 6 months

2. Feature Engineering:

Recency: How recently a customer made a purchase.

Frequency: How often they make a successful purchase

Monetary: How much they spend on average.

3. Modeling:

Applied K-means clustering to segment customers into groups based on their RFM scores.

Based on the model algorithms to choose the effective number of clusters, and based on the business suggestions, we choose K = 4 (cluster our data into 4 groups).

4. Evaluation:

Analyzed the distinctiveness and business relevance of the clusters using metrics such as silhouette score and cluster size.

C. Results:

Identified 4 distinct customer segments (e.g., high-value loyal customers, new customers, at-risk customers).

Provided actionable insights for personalized marketing, such as targeted promotions for different segments to maximize customer retention and revenue.