finding latent customer segments customer segmentation process helps the organization with knowing its customer base an obvious side effect of any such practice is finding out which segment of customers it might be missing this can help in identifying untapped customer segments by focused on marketing campaigns or new product development higher revenue this is the most obvious requirement of any customer segmentation project the reason being that customer segmentation can lead to higher revenue due to the combined effects of all the advantages identified in this section strategies the easy answer to the question of "how to do customer segmentation?would be "in any way you deem fitand it would be perfectly acceptable answer the reason this is the right answeris because of the original definition of customer segmentation it is just way of differentiating between the customers it can be as simple as making groups of customers based on age groups or other attributes using manual process or as complex as using sophisticated algorithm for finding out those segments in an automated fashion since our book is all about machine learningwe describe how customer segmentation can be translated into core machine learning task the detailed code for this section is available in the notebook titled customer segmentation ipynb clustering we are dealing with an unlabeledunsupervised transactional dataset from which we want to find out customer segments thusthe most obvious method to perform customer segmentation is using unsupervised machine learning methods like clustering hencethis will be the method that we use for customer segmentation in this the method is as simple as collecting as much data about the customers as possible in the form of features or attributes and then finding out the different clusters that can be obtained from that data finallywe can find traits of customer segments by analyzing the characteristics of the clusters exploratory data analysis using exploratory data analysis is another way of finding out customer segments this is usually done by analysts who have good knowledge about the domain relevant to both products and customers it can be done flexibly to include the top decision points in an analysis for examplefinding out the range of spends by customers will give rise to customer segments based on spends we can proceed likewise on important attributes of customers until we get segments of customer that have interesting characteristics clustering vs customer segmentation in our use casewe will be using clustering based model to find out interesting segments of customers before we go on to modify our data for the model there is an interesting point we would like to clarify lot of people think that clustering is equivalent to customer segmentation although it is true that clustering is one of the most suitable techniques for segmentationit is not the only technique besides thisit is just method that is "appliedto extract segments