This project aims to use Machine Learning techniques to improve customer segmentation. Instead of relying only on basic customer attributes, such as age, monthly income or marital status, we will adopt the RFM Analysis, which is based on analyzing the Recency, Frequency and Monetary Value of purchases made by customers. With these metrics in hand, we will be able to segment customers into groups with similar characteristics, allowing the Marketing or Sales area to more effectively customize the shopping experience for each segment.
Keywords: R Language, Big Data, Data Analysis, RFM Analysis, Machine Learning, Customer Segmentation, Grouping, Marketing.
RFM Analysis is a widely used strategy in the field of marketing and sales, which seeks to understand customer behavior and identify groups based on the recency of the last purchase, the frequency of purchases and the monetary amount spent. This segmentation allows the company to develop personalized strategies for each group, offering special offers, product recommendations and targeted actions. With the help of Machine Learning, we can further improve this segmentation, identifying complex patterns in the data and refining the accuracy of the analyses. This results in a greater understanding of customers and a shopping experience that is better suited to their needs, contributing to the success of the Marketing and Sales teams.
RFM analysis is based on three key metrics: recency, frequency, and monetary value. Recency measures how long ago a customer made their last purchase, identifying their recent engagement or inactivity. Frequency allows you to understand how often a customer makes purchases, indicating their level of engagement and value to the business. Monetary value evaluates the total customer spend on their purchases, helping to identify customers who generate the most revenue.
Based on these metrics, customers can be segmented into different groups such as "recently active" or "long inactive". This segmentation allows you to customize marketing and sales strategies to meet the needs of each group.
By segmenting customers, the marketing or sales team can take specific approaches for each group, such as sending special offers or product recommendations to the most engaged customers and developing strategies to reactivate inactive customers. This personalization of the shopping experience tends to improve the relationship with the company, increase satisfaction and boost sales.
The application of Machine Learning techniques in this process makes it possible to improve customer segmentation, identifying more complex patterns and relationships between attributes. Machine learning algorithms can help identify additional segments or improve segmentation accuracy based on available data.
In summary, the objective of this project is to use RFM Analysis and Machine Learning techniques to segment and group customers based on their similarity, in order to personalize the shopping experience and improve marketing and sales strategies. By better understanding customer behavior, you can optimize business performance, drive sales, and build longer-lasting customer relationships. As a result it is possible to deliver a graph and table to the marketing department. Now it is possible to identify five groups of customers by similarity.
We used real data extracted from the link: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II