Predictive Analytics: Customer Segmentation & Market Basket Analysis

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. 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.