RFM Customer Segmentation with K Means. This article is part of a bigger project for development of a basic marketing analytics stack. The idea is to build a stack of reusable and replicable templates for ML based marketing analysis.
This article was written in R Markdown. You can find the code [here_](https://github.com/DG-creative-lab/Customer_segmentation_with_K_Means_Clustering/blob/master/RFM_customer_segmentation_markdown_article.Rmd). The article was rendered and posted directly from R Markdown to Medium thanks to the [great work_](https://yutani.rbind.io/post/2017-10-26-post-to-medium/)_ of Hiroaki Yutani._
This article is part of a bigger project for development of a basic marketing analytics stack. The idea is to build a stack of reusable and replicable templates for ML based marketing analysis. Template here is used in the sense of process logic, reusable code snippets and generalised business case scenarios for which the model is applicable.
For every analytical model in the stack an interactive application in Shiny is suggested. The idea of the app is to serve as a reusable framework for model presentation. This will allow for non-technical users to get visual understanding of the data and play with different scenarios directly on the app. In addition, the application will allow for automation of the analysis. This means that when the application is integrated with the data source, every change in the data will automatically change the output figures from the analysis. This will allow for instantaneous marketing action like grasping time sensitive opportunities for up-sale promotions.
Customer segmentation helps you understand how, why, and when your product or service is purchased/used. These insights are crucial for efficient allocation of marketing resources.
Recency, frequency and monetary (RFM) customer segmentation gives you a base model to analyse customers according to their transactional behaviour- how recent was their last transaction, how often they purchase and how much are they spending.
RFM customer segmentation therefore is an effective way to prevent customer churn, identify and use up-sale and cross-sale opportunities.
K means is unsupervised algorithm and probably the most used algorithm for clustering. One of the major advantages of K-Means is that it can handle larger data sets compared to for example the hierarchical cluster approaches.
However, K-Means comes with some disadvantages as well. It is sensitive to outliers. The model is based on measuring distances between the data sets. The closer are the data points, the more similar they are. Therefore the data used with K-Means needs to be scaled before performing the algorithm.
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