This project revolves around leveraging three years' worth of transactional data collected by a manufacturer of automobile components. The primary objective is to harness this valuable historical data to generate personalized marketing plans tailored to different client categories.

The manufacturer faces the challenge of optimizing its marketing strategies and resources to cater to various client segments effectively. To address this, I have implemented RFM (Recency, Frequency, Monetary) analysis, a powerful technique in customer segmentation and targeting.

RFM analysis involves assessing the following aspects for each customer:

• Recency: How recently did the customer make a purchase?
• Frequency: How often does the customer make purchases?
• Monetary: How much does the customer spend?

By evaluating these parameters, we can categorize customers into different segments based on their buying behavior. This information is invaluable for crafting personalized marketing strategies for each group, such as tailored promotions, recommendations, or communication channels.

• Utilized three years' worth of transactional data for insights.
• Implemented RFM analysis using KNIME to segment customers effectively.
• Developed personalized marketing plans for each customer category.
• Optimized marketing strategies and resource allocation.
• Enhanced customer engagement and satisfaction.

To explore the project and its findings, refer to the provided Business Report in this repository. You can follow the steps outlined to replicate the RFM analysis and personalized marketing plans for your datasets or adapt the approach to other industries.