Data Mining Techniques in CRM: Inside Customer Segmentation by Konstantinos K. Tsiptsis, Visit Amazon's Antonios

By Konstantinos K. Tsiptsis, Visit Amazon's Antonios Chorianopoulos Page, search results, Learn about Author Central, Antonios Chorianopoulos,

This can be an utilized guide for the appliance of information mining strategies within the CRM framework. It combines a technical and a enterprise point of view to hide the desires of commercial clients who're trying to find a realistic advisor on facts mining. It makes a speciality of purchaser Segmentation and provides directions for the improvement of actionable segmentation schemes. through the use of non-technical language it publications readers via the entire levels of the knowledge mining procedure.

Content:
Chapter 1 facts Mining in CRM (pages 1–15):
Chapter 2 an summary of information Mining recommendations (pages 17–64):
Chapter three info Mining suggestions for Segmentation (pages 65–132):
Chapter four The Mining info Mart (pages 133–187):
Chapter five purchaser Segmentation (pages 189–224):
Chapter 6 Segmentation purposes in Banking (pages 225–290):
Chapter 7 Segmentation purposes in Telecommunications (pages 291–332):
Chapter eight Segmentation for outlets (pages 333–348):

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Example text

They associate a particular outcome category, for instance a product, with a set of conditions, for instance a set of other products. They are typically used to identify purchase patterns and groups of products purchased together. AN OVERVIEW OF DATA MINING TECHNIQUES 51 In the e-bookstore example, by browsing through past purchases, association models can discover other popular books among the buyers of the particular book viewed. They can then generate individualized recommendations that match the indicated preference.

Things are much more complicated than in our simplified exercise. Tasks like this are impossible to handle without the help of specialized algorithms that aim to automatically uncover the underlying groups. One thing that should be made crystal clear about clustering is that it groups records according to the observed input data patterns. Thus, the data miners and marketers involved should decide in advance, according to the specific business objective, the segmentation level and the segmentation criteria – in other words, the clustering fields.

It is called hierarchical or agglomerative because it starts with a solution where each record comprises a cluster and gradually groups records up to the point where all of them fall into one supercluster. In each step it calculates the distances between all pairs of records and groups the most similar ones. A table (agglomeration schedule) or a graph (dendrogram) summarizes the grouping steps and the respective distances. The analyst should consult this information, identify the point where the algorithm starts to group disjoint cases, and then 46 DATA MINING TECHNIQUES IN CRM decide on the number of clusters to retain.

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