Predictive Analytics: Customer Segmentation & Market Basket Analysis

Two central goals of marketing are reducing customer acquisition costs and increasing customer lifetime value. Customer segmentation is an important step towards both of these goals – by learning more about present and prospective customers marketing practitioners can focus on tailoring strategies to acquire and retain these different types of customers more effectively. Explore the regency frequency and monetary value (RFM) framework of customer interactions by performing K-means clustering and using the silhouette score to pick the optimal number of clusters. Next switch to two alternative clustering techniques known as agglomerative clustering and DBScan. Finally perform market basket analysis also known as affinity analysis to predict what items that customers will purchase together such as bread and jam. Use the a priori algorithm for computing frequent itemsets and the calculation and implications of metrics such as support confidence lift and conviction.