{"id":1385,"date":"2014-01-12T10:03:54","date_gmt":"2014-01-12T04:33:54","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=1385"},"modified":"2015-09-07T21:56:34","modified_gmt":"2015-09-07T16:26:34","slug":"customer-segmentation-outliers-telecom-case-study-part-3","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-outliers-telecom-case-study-part-3\/","title":{"rendered":"Cluster Analysis and Outliers \u2013 Telecom Case Study Example (Part 3)"},"content":{"rendered":"
\"Groucho<\/a>

Groucho – by Roopam<\/p><\/div>\n

\n

Outliers<\/span><\/h2>\n

“I refuse to join any club that would have me as a member.” – <\/i><\/b>Groucho Marx<\/b><\/p>\n

This witty statement came from (according to me) one of the funniest men in the history of American cinema \u2013 Julius Henry Marx better known as Groucho Marx. Groucho was certainly a very unusual man and might be considered to be an outlier. Today we are going to discuss the impact of outliers on cluster analysis and life in general. An outlier is an observation that is distant \/ different from the others. I know statisticians get nightmares about outliers. A single outlier can create havoc in any analysis, hence the general tendency is to ignore them from the analysis or beat them back to normal (read data transformation to form normal distribution). At times the above techniques to deal with the outliers are necessary for the sake of analysis. However ignoring outliers altogether is something analysts \/ scientists \/ society can take at their own peril. The reason is outliers could be pointing towards a new emerging trend in the system. Today\u2019s outlier may very well be tomorrow\u2019s normal. Also, let’s face it outliers are so much more fun!<\/p>\n

I have discussed a few outliers in the articles on YOU CANalytics such as Columbus<\/a>, Turning<\/a>,\u00a0Euler<\/a>,\u00a0Sherlock Holmes<\/a>,\u00a0Leonardo da Vinci<\/a>, Batman<\/a>, and of course Groucho Marx. These men* have changed the course of human history in their own way. I hope to introduce more such outliers in the future articles. Interestingly, one of the striking feature about the human outliers is the treatment they receive from the society, similar to statistical outliers of getting ignored or beaten-up to convert to normal.<\/p>\n

* I just noticed so far I have not introduced a\u00a0<\/span>woman\u00a0<\/span>outlier in my articles, will do it soon.<\/span><\/p>\n

Telecom Case Study Example and Outliers<\/span><\/h2>\n

\"Cluster<\/a>In the last few articles, we have been working on a case study example from the telecom sector where you are playing the role of\u00a0the head of customer insights and marketing (Read Part 1<\/a> and Part 2<\/a>). In those articles, you started with some fundamentals of cluster analysis. The business case was to create customer segments to understand your customer base better and enhance your company\u2019s marketing campaigns. In the first part of this case study, we have created the clusters on our dataset with the following two variable – average international and local call duration. We chose a couple of random seeds and produced the adjustment clusters. These clusters were produced after recursive iteration where Euclidean distance plays a crucial role.<\/p>\n

\"outlier<\/a><\/p>\n

Now, let us add one more customer \/data point to the above dataset with average local duration equal to 20 minutes and international duration equal to 10 minutes. This customer is clearly an outlier as can be seen in the adjustment plot. Now, let us try to perform cluster analysis on this modified dataset with the same original random seeds that we have used in the first article. The iterative results are shown in the animation below. There are total 4 iterations and notice how the cluster centroids are moving with each iteration. Additionally, also keep an eye for how cluster allegiance for each data point is changing with iterations. The color of data point represents cluster\u00a0allegiance.<\/p>\n

\"output_0OCngU\"<\/a><\/p>\n

Clearly, the presence of an outlier has changed the entire result of our analysis. You must have noticed that the initial choice of two clusters or two random seeds was not that good with the addition of an outlier. In this case, the outlier became a single cluster and the remaining data points are formed into another cluster. If we had 3 cluster centroid-seeds in the beginning we would have seen a more reasonable cluster results. This has put forth an important question about the choice of number of cluster at the beginning of the analysis. Though one would like a simple answer to the question, trust me there isn’t one. There are a few analytical techniques that could serve well in this but at the end of the day the analyst needs to make a prudent choice based on her domain experience.\u00a0We will discuss these analytical techniques in some other article.<\/p>\n

Sign-off Note<\/span><\/h4>\n

I believe there is an outlier hidden somewhere in all of us. Outliers can make the world a better place to live. There is a need to not let these outliers be beaten into normal because of the life\u2019s pressure we all go through. Let me finish this article with another classic statement from Groucho Marx who didn\u2019t lose his witty self even when he was on his death bed.\u00a0 A nurse came and mentioned to frail Groucho that she wanted to measure if he had a temperature. Goucho retorted in his quick wit tone \u201cDon\u2019t be silly \u2013 everybody has a temperature\u201d. Yes, this is similar to saying everybody has an outlier.<\/p>\n","protected":false},"excerpt":{"rendered":"

Outliers “I refuse to join any club that would have me as a member.” – Groucho Marx This witty statement came from (according to me) one of the funniest men in the history of American cinema \u2013 Julius Henry Marx better known as Groucho Marx. Groucho was certainly a very unusual man and might be<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":1386,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[1,58],"tags":[7,71,6,10],"jetpack_publicize_connections":[],"yoast_head":"\nCluster Analysis and Outliers: Telecom Case Study Example<\/title>\n<meta name=\"description\" content=\"This is a case study example to illustrate significance of outliers in customer segments through cluster analysis.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-outliers-telecom-case-study-part-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cluster Analysis and Outliers: Telecom Case Study Example\" \/>\n<meta property=\"og:description\" content=\"This is a case study example to illustrate significance of outliers in customer segments through cluster analysis.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-outliers-telecom-case-study-part-3\/\" \/>\n<meta property=\"og:site_name\" content=\"YOU CANalytics |\" \/>\n<meta property=\"article:author\" content=\"roopam\" \/>\n<meta property=\"article:published_time\" content=\"2014-01-12T04:33:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2015-09-07T16:26:34+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/01\/photo.jpg?fit=768%2C1024&ssl=1\" \/>\n\t<meta property=\"og:image:width\" content=\"768\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Roopam Upadhyay\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Organization\",\"@id\":\"https:\/\/ucanalytics.com\/blogs\/#organization\",\"name\":\"YOU CANalytics\",\"url\":\"https:\/\/ucanalytics.com\/blogs\/\",\"sameAs\":[],\"logo\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/ucanalytics.com\/blogs\/#logo\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2015\/11\/YOU-CANalytics-Logo.jpg?fit=607%2C120\",\"contentUrl\":\"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2015\/11\/YOU-CANalytics-Logo.jpg?fit=607%2C120\",\"width\":607,\"height\":120,\"caption\":\"YOU CANalytics\"},\"image\":{\"@id\":\"https:\/\/ucanalytics.com\/blogs\/#logo\"}},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/ucanalytics.com\/blogs\/#website\",\"url\":\"https:\/\/ucanalytics.com\/blogs\/\",\"name\":\"YOU CANalytics |\",\"description\":\"Explore the Power of Data Science\",\"publisher\":{\"@id\":\"https:\/\/ucanalytics.com\/blogs\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/ucanalytics.com\/blogs\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-outliers-telecom-case-study-part-3\/#primaryimage\",\"inLanguage\":\"en-US\",\"url\":\"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/01\/photo.jpg?fit=768%2C1024&ssl=1\",\"contentUrl\":\"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/01\/photo.jpg?fit=768%2C1024&ssl=1\",\"width\":768,\"height\":1024,\"caption\":\"Groucho - 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These are some of your observations\u2026","rel":"","context":"In "Analytics Labs"","block_context":{"text":"Analytics Labs","link":"https:\/\/ucanalytics.com\/blogs\/category\/analytics-labs\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2016\/08\/Regression-analysis.jpg?fit=448%2C528&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":695,"url":"https:\/\/ucanalytics.com\/blogs\/data-visualization-case-study-banking-part-2\/","url_meta":{"origin":1385,"position":2},"title":"Data Visualization – Banking Case Study Example (Part 2)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Sherlock Holmes & Data Visualization As a kid, a friend of mine used to own a Sherlock Holmes toy kit \u2013 the source of envy for all the other friends. The kit had a Sherlock Holmes cap, a pipe, a watch and a magnifying glass. The magnifying glass was the\u2026","rel":"","context":"In "Banking Risk Case Study Example"","block_context":{"text":"Banking Risk Case Study Example","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/banking-risk-case-study-example\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Sherlock-Holmes-Copy.jpg?fit=459%2C458&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1116,"url":"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-cluster-analysis-telecom-case-study-example\/","url_meta":{"origin":1385,"position":3},"title":"Customer Segmentation & Cluster Analysis – Telecom Case Study Example (Part 1)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Galaxies and Cluster Analysis I live in Mumbai (Bombay), the financial capital of India and one of the largest cities in the world. One of the problems of living in a large city is that you rarely see stars in the night sky. The limited sky one can see through\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"The Night Sky - by Roopam","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/11\/sky-1.jpg?fit=768%2C1024&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/11\/sky-1.jpg?fit=768%2C1024&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/11\/sky-1.jpg?fit=768%2C1024&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/11\/sky-1.jpg?fit=768%2C1024&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":1259,"url":"https:\/\/ucanalytics.com\/blogs\/customer-segmentation-cluster-analysis-telecom-case-study-part-2\/","url_meta":{"origin":1385,"position":4},"title":"Customer Segmentation & Cluster Analysis \u2013 Telecom Case Study Example(Part 2)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"In one of\u00a0the previous articles, we have started with a case study example from the telecom sector. 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