#segmentation

Perform RFM (Recency, Frequency, Monetary) customer segmentation. Process: 1. Calculate RFM scores from transaction data. 2. Normalize features using StandardScaler. 3. Determine optimal K using elbow method and silhouette score. 4. Apply K-means clustering (4-6 segments). 5. Profile each segment with descriptive statistics and business labels (Champions, At-Risk, Lost). Visualize clusters using PCA 2D projection.

Create accurate user personas based on real customer data. Research methods: 1. User interviews (15-20 per segment): understand goals, frustrations, workflows. 2. Analytics analysis: usage patterns, feature adoption, churn triggers. 3. Support ticket analysis: common issues and requests. 4. Sales team insights: objections, competitive losses. Persona components: 1. Demographics: age, role, company size, location. 2. Goals: what they're trying to achieve (primary and secondary). 3. Pain points: current frustrations and blockers. 4. Behaviors: how they discover and evaluate solutions. 5. Quote: memorable statement capturing their mindset. Example: 'Sarah, Marketing Manager at 500-person SaaS company. Goal: prove marketing ROI to executives. Pain: too many tools, data scattered. Quote: I spend more time making reports than analyzing them.' Validation: test personas against new customer data quarterly. Use in product decisions: WWSD (What Would Sarah Do?).