{"id":1532,"date":"2014-02-25T21:14:50","date_gmt":"2014-02-25T15:44:50","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=1532"},"modified":"2015-09-07T21:57:26","modified_gmt":"2015-09-07T16:27:26","slug":"customer-segmentation","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/customer-segmentation\/","title":{"rendered":"Telecom Case (Part 4) – Customer Segmentation and Application"},"content":{"rendered":"
\n

Telecom Case Study \u2013 Customer Segmentation<\/b><\/span><\/h2>\n
\"Customer<\/a>

Customer Segmentation – by Roopam<\/p><\/div>\n

For the last few articles we have been working on a telecom case study to create customer segments (Part 1<\/a>, Part 2<\/a> and Part 3<\/a>). In this case, you are the head of customer insights and marketing at a telecom company, ConnectFast Inc. Recall, in the first part, you have created cluster centroids through iterative calculation of Euclidean distances. \u00a0Remember, the objective of iterative calculations was to adjust the centroids to place them at the center of the cluster members (see Part 1<\/a>). Have a look at the animation below (you have seen this data in part 1 of this series); with each iteration Standard Sum of Error (SSE) is reducing. For the time being, don\u2019t worry about the calculation of SSE but try to understand its purpose. In the animation, we started with 29 as the original value of SSE for the original random seeds and converged to stabilized SSE of 7 for the final iteration (further iterations won\u2019t change SSE or positions of cluster centroids). This is absolutely the objective to iteratively reduce SSE till it gets stabilized \u2013 and voila! You have found your cluster centroids \/ black holes (see Part 1<\/a>). As discussed in the previous article most machine-learning algorithms try to iteratively converge to an optimal solution. For cluster analysis the idea is to minimize SSE iteratively. I hope you have noticed, this is somewhat similar to Archimedes\u2019 method of converging to the value of \u03c0 (discussed in the previous article<\/a>).<\/p>\n

 <\/p>\n

\"output_3DSNJW\"<\/a><\/p>\n

Customer Segmentation<\/b><\/span><\/b><\/h2>\n

Coming back to the case study, you are at the final stages of customer segmentation exercise to form clusters based on customers\u2019 services usage behavior. As a telecom company ConnectFast offers several services on top of their existing cellphone plan (with prepaid and postpaid billing), some of them are listed below\"Customer<\/a><\/p>\n