Customer Segmentation using Clustering

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Confidential. © Stream Intelligence Ltd. All rights reserved.

Introduction to Clustering

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Agenda

1 Introduction: Business Case

2 Clustering

3 Hierarchical Clustering

4 K-means Clustering

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Business Case

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Business Case – Predicting Successful Music Production

Clustermusic A

Cluster music B

Clustermusic C

Clustermusic 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

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Clustering

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Statistical Learning Categorization

Statistical Learning

Unsupervised Learning

Supervised Learning

Clustering Predictive Model

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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

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Hierarchycal Clustering

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Hierarchical Clustering

• Start with each data point in its own cluster

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Hierarchical Clustering

• Combine two nearest clusters (Euclidian, Centroid)

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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. Actionb. Comedyc. Sci-Fid. etc.

http://www.movielens.org/

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Dendogram

Heights represent the distance between point/cluster

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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

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Common scenario

Tips:- Normalize the data

Movie Action Romance Rating Revenue (in USD)

A 1 1 5 200B 0 1 4 150C 0 0 3 50D 1 1 4 120

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K-means Clustering

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K-Means Clustering

1. Group data into K-clusters by:a. Determining the k centroidb. 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

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Suppose k=3

K-Means Illustrations

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Iteration = 0

1. Start with random positions of centroids.


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Iteration = 1

1. Start with random positions of centroids.2. Assign each data point to closest centroid


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Iteration = 1

1. Start with random positions of centroids.2. Assign each data point to closest centroid3. Move centroids to center of assigned

points (recalculating C)


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Iteration = 3


points4. Iterate till minimal cost


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Iteration = 3


points4. Iterate till minimal cost

What potentially can go wrong?

23Optimum Number of Cluster Illustrations

TSS = Total Sum of Square ErrorK = Number of cluster

Optimum Number of Cluster

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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?

Customer Segmentation using Clustering

Data & Analytics

Transcript of Customer Segmentation using Clustering