Customer Segmentation for Churn Prediction and Behavioural Insights in E-commerce using K-Means Clustering: A Detailed Report

1. Introduction: The Imperative of Customer Segmentation and Churn Prediction in E-commerce

In the highly competitive e-commerce landscape, understanding customer behaviour and predicting churn is not just beneficial, it's a necessity for sustainable growth and profitability. Acquiring new customers is significantly more expensive (5 to 25 times, according to research) than retaining existing ones. Furthermore, a substantial percentage of e-commerce buyers, potentially as high as 75%, might be one-time purchasers. This underscores the critical importance of customer retention and churn management in this domain. Effective customer segmentation plays a pivotal role in achieving these goals by enabling businesses to identify distinct customer groups, understand their specific needs and behaviours, and tailor strategies to enhance engagement and minimize attrition.

This report delves into the application of K-Means clustering for customer segmentation in e-commerce, specifically focusing on its role in churn prediction and extracting actionable behavioural insights. The analysis draws upon recent research and practical implementations to provide a comprehensive overview of this methodology, its benefits, limitations, and potential enhancements. This report is tailored for the e-commerce domain and is conducted under the premise of no domain-specific constraints or regulatory limitations, as specified in the initial query.

2. Foundations of Customer Segmentation: RFM and Behavioural Metrics in E-commerce

RFM (Recency, Frequency, Monetary)

• Recency: How recently a customer made a purchase. Customers who have made purchases more recently are generally considered more engaged and valuable.
• Frequency: How often a customer makes purchases. Customers with higher purchase frequencies are typically more loyal and engaged.
• Monetary Value: How much a customer spends. Customers with higher monetary value contribute more significantly to revenue.

Beyond RFM, a richer understanding of customer behaviour can be achieved by incorporating other behavioural metrics. These metrics capture the nuanced ways customers interact with an e-commerce platform and can provide deeper insights into their engagement and potential churn risk. Examples of these additional behavioural metrics include:

• Engagement Time: Time spent browsing the website, product pages, or within the application. Longer engagement often indicates higher interest and loyalty.
• Purchase Frequency (detailed): Number of purchases within specific timeframes (weekly, monthly, quarterly). This offers a more granular view than just overall frequency within the RFM model.
• Support Ticket Volume: Number of support requests or tickets raised by a customer. High support ticket volume could indicate dissatisfaction or issues, potentially leading to churn, or conversely, high engagement but with problems. Context is crucial.
• Website/App Activity: Pages visited, features used, navigation patterns. This can reveal customer interests and preferences.
• Product Category Preferences: Categories of products frequently purchased or browsed. This helps in personalizing recommendations and offers.
• Cart Abandonment Rate: Frequency at which customers add items to their cart but do not complete the purchase. High cart abandonment can signal friction in the purchase process or lack of purchase intent.
• Email Engagement: Open rates, click-through rates on marketing emails. Indicates responsiveness to marketing efforts and overall engagement.
• Returns and Refunds: Number and value of returns or refunds initiated by a customer. High returns may signal product dissatisfaction or sizing issues, potentially impacting loyalty.
• Wishlist Activity: Items added to wishlists, frequency of wishlist usage. Indicates potential future purchase intent and product interest.

By combining RFM metrics with these broader behavioural metrics, businesses can create a comprehensive customer profile that captures both transactional behaviour and engagement patterns, providing a robust foundation for segmentation and churn prediction.