RFM Analysis to Understand Customer Patterns, Engagement and Retention in E-Commerce

Abstract

Recency, Frequency and Monetary (RFM) analysis is a popular marketing technique that seeks to classify customers according to the frequency and value of their transactions. This article provides an introduction to the RFM analysis process and how it is used to gather crucial information for developing targeted marketing strategies. By using the RFM analysis on our catalogue and analysing sales data, we gain significant insights into the performance of particular products. According to the demand, the Recency dimension reveals which items are selling swiftly or slowly. Frequency dimension shows how well-liked particular goods are. The financial component, sheds information on viability and worth of any offering. RFM analysis gives marketers an opportunity to pinpoint various consumer categories with varied degree of loyalty, engagement and profitability. RFM analysis enables marketers to group customers into categories, like high-value, at-risk or dormant, using a combination of statistical methodologies and data mining. With a deep understanding of catalogue and sales data, we can make better choices on the products for customers. By emphasising top-selling items on the home-page, we may captivate customers and instantly pique their curiosity. In order to increase consumer involvement, we might simultaneously prepare to promote slower moving products with targeted incentives. The messaging, promotions and customer interactions can be modified for specific preferences of each group. High value customers can receive exclusive incentives to strengthen loyalty, and at risk customers receive re-engagement initiatives. By using RFM framework, businesses may develop targeted marketing strategies, enhance client interaction and boost marketing return-on-investment.

Keywords: RFM Model, Data Science, E-Commerce, Inventory, Product Marketing, Machine Learning

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