Customer Segmentation using Clustering
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The document provides an overview of clustering techniques, specifically hierarchical and k-means clustering, and their applications in predicting successful music production. It outlines a business case for increasing the success rate of music hits using clustering algorithms to analyze data. Practical exercises are included to demonstrate clustering with movie and credit card profile data.
Customer Segmentation using Clustering
- 1. 0 Confidential. © Stream Intelligence Ltd. All rights reserved. Introduction to Clustering
- 2. 1 Confidential. © Stream Intelligence Ltd. All rights reserved. Agenda 1 Introduction: Business Case 2 Clustering 3 Hierarchical Clustering 4 K-means Clustering
- 3. 2 Confidential. © Stream Intelligence Ltd. All rights reserved. Business Case 1
- 4. 3 Confidential. © Stream Intelligence Ltd. All rights reserved. Business Case – Predicting Successful Music Production Cluster music A Cluster music B Cluster music C Cluster music D • Target is to appear at Billboard’s weekly to 40 • Cost per single could up to 300K USD • Music Intelligence Solution using clustering to predict if a music will be accepted by market • Increase success rate from 1 out of 10 to 8 out of 10
- 5. 4 Confidential. © Stream Intelligence Ltd. All rights reserved. Clustering 2
- 6. 5 Confidential. © Stream Intelligence Ltd. All rights reserved. Statistical Learning Categorization Statistical Learning Unsupervised Learning Supervised Learning Clustering Predictive Model
- 7. 6 Confidential. © Stream Intelligence Ltd. All rights reserved. Clustering • Process of grouping a set of physical or abstract objects into clusters (example: customer, product etc.) • A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters • Similarity is calculated based distance between point • Common distance measure is Euclidian distance
- 8. 7 Confidential. © Stream Intelligence Ltd. All rights reserved. Hierarchycal Clustering 2
- 9. 8 Confidential. © Stream Intelligence Ltd. All rights reserved. Hierarchical Clustering • Start with each data point in its own cluster
- 10. 9 Confidential. © Stream Intelligence Ltd. All rights reserved. Hierarchical Clustering • Combine two nearest clusters (Euclidian, Centroid)
- 11. 10 Confidential. © Stream Intelligence Ltd. All rights reserved. Lets Practice • The data for this exercise was downloaded from www.movielens.org • Open “clustering_movie.R” • The movies in the dataset are categorized as belonging to different gender: a. Action b. Comedy c. Sci-Fi d. etc.
- 12. 11 Confidential. © Stream Intelligence Ltd. All rights reserved. Dendogram Heights represent the distance between point/cluster
- 13. 12 Confidential. © Stream Intelligence Ltd. All rights reserved. Finding Meaningful Cluster • How to see which cluster have the most action movies? use this command: tapply(movies$Action, clusterGroups, mean) • Exercise: Can you find the characteristic of each cluster? Hint: - Add the cluster as one of the variable in the data - Load dplyr library - Use aggregate and summarise function
- 14. 13 Confidential. © Stream Intelligence Ltd. All rights reserved. Common scenario Tips: - Normalize the data Movie Action Romance Rating Revenue (in USD) A 1 1 5 200 B 0 1 4 150 C 0 0 3 50 D 1 1 4 120
- 15. 14 Confidential. © Stream Intelligence Ltd. All rights reserved. K-means Clustering 2
- 16. 15 Confidential. © Stream Intelligence Ltd. All rights reserved. K-Means Clustering 1. Group data into K-clusters by: a. Determining the k centroid b. Group the data points to the nearest centroid 2. Algorithm works by iterating between two stages until the data points converge Objective : High Level Description
- 18. 17 Iteration = 0 1. Start with random positions of centroids. K-Means Illustrations
- 19. 18 Iteration = 1 1. Start with random positions of centroids. 2. Assign each data point to closest centroid K-Means Illustrations
- 20. 19 Iteration = 1 1. Start with random positions of centroids. 2. Assign each data point to closest centroid 3. Move centroids to center of assigned points (recalculating C) K-Means Illustrations
- 21. 20 Iteration = 3 1. Start with random positions of centroids. 2. Assign each data point to closest centroid 3. Move centroids to center of assigned points 4. Iterate till minimal cost K-Means Illustrations
- 22. 21 Iteration = 3 1. Start with random positions of centroids. 2. Assign each data point to closest centroid 3. Move centroids to center of assigned points 4. Iterate till minimal cost What potentially can go wrong?
- 23. 22 Optimum Number of Cluster Illustrations TSS = Total Sum of Square Error K = Number of cluster Optimum Number of Cluster
- 24. 23 Confidential. © Stream Intelligence Ltd. All rights reserved. Lets Practice • We will use the credit card profile data (cc-profile.csv) • Open “segmenting_customer.R” Exercise: • What is the optimum number of cluster? • Please provide the characteristics of segment. Do you think it is meaningful?