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Extra info for Data Mining Techniques in CRM: Inside Customer Segmentation
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.