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,

A whole and complete guide for the applying of knowledge mining thoughts in advertising and client courting administration. It combines a technical and a company viewpoint, bridging the distance among info mining and its use in marketing. It publications readers via all of the stages of the knowledge mining strategy, featuring an excellent information mining method, information mining top practices and proposals for using the knowledge mining effects for potent advertising and marketing. It solutions the the most important query of 'what info to take advantage of' via featuring mining info marts and entire lists of KPIs for all significant industries.Data mining algorithms are offered in an easy and finished approach for the enterprise clients besides real-world software examples from all significant industries.The publication is especially addressed to agents, company analysts and information mining practitioners who're searching for a how-to advisor on facts mining. It provides the authors' wisdom and event from the "data mining trenches", revealing the secrets and techniques for information mining good fortune.

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Kohonen network/Self-Organizing Map (SOM): Kohonen networks are based on neural networks and typically produce a two-dimensional grid or map of the clusters, hence the name self-organizing maps. Kohonen networks usually take a longer time to train than the K-means and TwoStep algorithms, but they provide a different view on clustering that is worth trying. Apart from segmentation, clustering techniques can also be used for other purposes, for example, as a preparatory step for optimizing the results of predictive models.

They incorporate specific criteria to identify inadequate predictors: for instance, predictors with an extensive percentage of missing (null) values, continuous predictors which are constant or have little variation, categorical predictors with too many categories or with almost all records falling in a single category. • Rank predictors according to their predictive power. The influence of each predictor on the target field is assessed and an importance measure is calculated. Predictors are then sorted accordingly.

This feature enables marketers to tailor the size of their campaigns according to their resources and marketing objectives. They can expand or reduce their target lists on the basis 22 DATA MINING TECHNIQUES IN CRM of their particular objectives, always targeting those customers with the relatively higher probabilities. The purpose of all classification models is to provide insight and help in the refinement and optimization of marketing applications. The first step after model training is to browse the generated results, which may come in different forms according to the model used: rules, equations, graphs.

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