Segmentation

Abstract

Segmentation identifies subgroups better served if treated separately, especially for risk-heterogeneous populations. Trade-offs occur between the resulting extra lift and the extra costs and complexities. It provides little where risk-homogeneity is enforced by strong filtering mechanisms. (1) Overview—i) drivers—operational, strategic, feedstock or interactional; ii) inhibitors—limits on the number of segments {insufficient data, costs of development, implementation, monitoring}; iii) mitigators—steps to reduce model count {interaction characteristics, alternative transformation and development methodologies}. (2) Analysis—i) learning types—supervised and unsupervised; ii) finding interactions—how to measure interactions for binary targets; iii) segment mining—comparing multiple options; iv) boundary analysis—assessing the impact for cases that switch segments. (3) Presentation—tabular and graphic means of presenting comparisons of different options, especially against having a single model. It includes performance within and across segments, drill-downs into segments and strategy curves showing differences in Accept and Bad rates.