Media companies aggressively compete for their share of the pay-per-view television market. Such share can only be kept or improved by avoiding customer defection, or churn. The analysis of customers' data should provide insight into customers' behavior over time and help preventing churn. Data visualization can be part of this analysis. Here, a database of pay-per-view television customers is visualized using a nonlinear manifold learning model. This visualization is enhanced through, first, the reintroduction of the local nonlinear distortion using a cartogram technique and, second, the visualization of customer migrations using flow maps. Both techniques are inspired by geographical representation.
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