Customer Segmentation Analytics Masterclass 2024
Tobias Mayer
Prepare customer data in Python, apply supervised regression and unsupervised k-means/PCA, evaluate clusters and translate segments into targeted marketing actions
Marketing focus
Prepare customer data, apply hierarchical/k-means/PCA segmentation, analyze purchase data by segment and model purchase incidence, marketing mix and price response
365 Careers , Iliya Valchanov · Udemy
Prepare customer data, apply hierarchical/k-means/PCA segmentation, analyze purchase data by segment and model purchase incidence, marketing mix and price response
Selected published requirements: “Basic Python programming”. See the provider page for the full requirements and current tool terms
Practice: The outline lists quiz questions about price elasticity
Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
Related tasks, tools and formats. Check the differences before choosing.
Tobias Mayer
Prepare customer data in Python, apply supervised regression and unsupervised k-means/PCA, evaluate clusters and translate segments into targeted marketing actions
Packt Publishing
Prepare and visualize marketing data in Python, create hierarchical and k-means customer segments, predict customer value and classify customer choice
O.P. Jindal Global University
Python marketing workflows cover k-means, hierarchical and DBSCAN segmentation, PCA, t-SNE and autoencoders, anomaly detection, association mining, semi-supervised methods, and recommender systems