#SQL #RFM Calculate Customer segmentation based on RFM and Purchase Category

One of the significant challenges I managed to solve in the last 24 months was related to customer segmentation in my previous role. We previously used a generalized Recency, Frequency, Monetary (RFM) model for our entire customer base. This was limiting because each customer demonstrated different behaviours depending on the product category, making generic segmentation ineffective. I observed that customers who purchased electronic products were different from those who bought Fast-Moving Consumer Goods (FMCG). This sparked the idea that we should not lump our customers into a one-size-fits-all model. Instead, we needed to acknowledge these differences and account for them in our marketing strategy. To address this challenge, I developed an 'RFM per category' model. This involved conducting separate RFM analyses for each product category – thereby creating a more nuanced picture of our customers per category. By doing this, we could understand and work with our customers' specific behaviours within each category rather than treating all customers as the same. After implementing the 'RFM per category' model, I also developed a data dashboard for it. This tool allowed us to visualize our data, making it easier to identify our high-value customers for each category. The result of the 'RFM per category' project was a significant improvement in our marketing strategies. It allowed us to create targeted campaigns for each category's high-value customers, leading to more effective marketing and better returns on our marketing investment. This project was a prime example of how a nuanced understanding of data can lead to real-world solutions. It also demonstrated my ability to innovate and adapt traditional methodologies to address specific business needs. Author Mohammed Torbati