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Additional resources for Data Mining Techniques in CRM: Inside Customer Segmentation
To present the basic concepts of clustering, let us consider the hypothetical case of a mobile telephony network operator that wants to segment its customers according to their voice and SMS usage. The available demographic data are not used as clustering inputs in this case since the objective concerns the grouping of customers according only to behavioral criteria. 6. 10, these customers are positioned in a twodimensional space according to their voice usage, along the X-axis, and their SMS usage, along the Y-axis.
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. The top 10% model tile identiﬁes about four times more target customers than a random list of the same size. ’’ The lift or index assesses the improvement in predictive ability due to the model.
The following modeling techniques are included in the class of classiﬁcation models: • Decision trees: Decision trees operate by recursively splitting the initial population. For each split they automatically select the most signiﬁcant predictor, the predictor that yields the best separation with respect to the target ﬁeld. Through successive partitions, their goal is to produce ‘‘pure’’ sub-segments, with homogeneous behavior in terms of the output. They are perhaps the most popular classiﬁcation technique.