📢 Day 3: Using RFM Scoring to Classify Customer Risk
📊 Before diving into machine learning or deep learning models, I started with a simpler yet powerful approach: RFM (Recency, Frequency, Monetary) scoring to classify customers into high-risk or low-risk groups.
Why RFM? In the context of Ethiopian BNPL services, where traditional credit histories are scarce, RFM provides a practical starting point by analyzing customer behavior:
1️⃣ Recency: How recently did the customer make a purchase?
2️⃣ Frequency: How often do they shop?
3️⃣ Monetary: How much do they spend?
This method helped me:
✅ Identify behavioral patterns to differentiate reliable customers from risky ones.
✅ Create a foundation for more advanced models
✅ Address data scarcity by leveraging transactional and engagement data.
RFM scoring is simple and interpretable, making it easier to communicate results to stakeholders early on.Next, I’ll integrate these insights into machine learning models to refine predictions and enhance scalability. 🚀
📊 Before diving into machine learning or deep learning models, I started with a simpler yet powerful approach: RFM (Recency, Frequency, Monetary) scoring to classify customers into high-risk or low-risk groups.
Why RFM? In the context of Ethiopian BNPL services, where traditional credit histories are scarce, RFM provides a practical starting point by analyzing customer behavior:
1️⃣ Recency: How recently did the customer make a purchase?
2️⃣ Frequency: How often do they shop?
3️⃣ Monetary: How much do they spend?
This method helped me:
✅ Identify behavioral patterns to differentiate reliable customers from risky ones.
✅ Create a foundation for more advanced models
✅ Address data scarcity by leveraging transactional and engagement data.
RFM scoring is simple and interpretable, making it easier to communicate results to stakeholders early on.Next, I’ll integrate these insights into machine learning models to refine predictions and enhance scalability. 🚀