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 finished guide for the appliance of knowledge mining ideas in advertising and buyer dating administration. It combines a technical and a enterprise viewpoint, bridging the distance among info mining and its use in marketing. It courses readers via all of the stages of the knowledge mining strategy, providing a pretty good info mining method, facts mining top practices and proposals for using the knowledge mining effects for potent advertising. It solutions the an important query of 'what info to exploit' through presenting mining facts marts and entire lists of KPIs for all significant industries.Data mining algorithms are offered in an easy and entire approach for the company clients in addition to real-world program examples from all significant industries.The publication is principally addressed to dealers, enterprise analysts and information mining practitioners who're trying to find a how-to consultant on info mining. It provides the authors' wisdom and event from the "data mining trenches", revealing the secrets and techniques for info mining good fortune.

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4 Model tiles 1 2 3 4 5 6 7 8 9 10 The gains, response, and lift table. 0 The concentration of the actual churners is also expected to decrease. 2%). 8 times higher than randomness, though. ’’ Gain % is defined as the percentage of the total target population that belongs in the quantiles. 1% of all actual churners, yielding a gain % of the same value. A random list containing 10% of the customers would normally capture about 10% of all observed churners. 1%) of all observed churners. Once again we come to the lift concept.

Similarly, records far apart from the majority of the cluster members might also indicate anomalous cases that require special attention. The clustering techniques are further explained and presented in detail in the next chapter. AN OVERVIEW OF DATA MINING TECHNIQUES 47 REDUCING THE DIMENSIONALITY OF DATA WITH DATA REDUCTION TECHNIQUES As their name implies, data reduction techniques aim at effectively reducing the data’s dimensions and removing redundant information. They do so by replacing the initial set of fields with a core set of compound measures which simplify subsequent modeling while retaining most of the information of the original attributes.

Clustering solutions are based on analyzing similarities among records. 11 43 Graphical representation of clustering. records with similar input data patterns, hence similar behavioral profiles, to the same cluster. Nowadays, various clustering algorithms are available, which differ in their approach for assessing the similarity of records and in the criteria they use to determine the final number of clusters. The whole clustering ‘‘revolution’’ started with a simple and intuitive distance measure, still used by some clustering algorithms today, called the Euclidean distance.

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