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The model predicts churn three months ahead. • The generated churn propensities can then be used for better targeting of an outbound retention campaign. The churn model results can be combined and cross-examined with the present or potential value of the customers so that the retention activities are prioritized accordingly. • All input data fields that were used for the model training are required, obviously with refreshed information, in order to update the churn propensities. • Two months have been reserved to allow for scoring and preparing the campaign.

To illustrate their basic concepts and usage we will present the results of a hypothetical churn model that was built on a dichotomous output field which flagged churners. The first step in the creation of such charts and tables is to select the target category of interest, also referred to as the hit category. Records/customers are then ordered according to their hit propensities and binned into groups of equal size, named quantiles. In our hypothetical example, the target is the category of churners and the hit propensity is the churn propensity; in other words, the estimated likelihood of belonging to the group of churners.

The reasoning behind this approach is that, from that point on, the model classifies worse than randomness. This approach usually yields large targeting lists. In practice analysts and marketers should take into consideration the particular business situation, objectives, and resources and possibly consider as a classification threshold the point of lift maximization. If possible, they should also incorporate in the gains chart cost (per offer) and revenue (per acceptance) information and select the cut-point that best serves their specific business needs and maximizes the expected ROI and profit.

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