benefits of segmentation: Evidence from a South African bank and other studies

Abstract

We applied different modelling techniques to six data sets from different disciplines in the industry, on which predictive models can be developed, to demonstrate the benefit of segmentation in linear predictive modelling. We compared the model performance achieved on the data sets to the performance of popular non-linear modelling techniques, by first segmenting the data (using unsupervised, semi-supervised, as well as supervised methods) and then fitting a linear modelling technique. A total of eight modelling techniques was compared. We show that there is no one single modelling technique that always outperforms on the data sets. Specifically considering the direct marketing data set from a local South African bank, it is observed that gradient boosting performed the best. Depending on the characteristics of the data set, one technique may outperform another. We also show that segmenting the data benefits the performance of the linear modelling technique in the predictive modelling context on all data sets considered. Specifically, of the three segmentation methods considered, the semi-supervised segmentation appears the most promising.

empirical investigation of alternative semi-supervised segmentation methodologies

“…The benefit of segmentation was also clearly illustrated in the six data sets used in previous work, 12 although the impact of the transformation methodology is not known. In this study, we have also clearly highlighted the danger of using an absolute Gini coefficient to evaluate the performance of any predictive model.…”

“…Note that for a comparison of supervised and unsupervised techniques, please refer to other research studies. [11][12][13] Also, while our focus was to compare different semi-supervised segmentation techniques, we have also included an unsegmented logistic regression in each table as a further baseline. Table 2 summarises the performance of the modelling techniques when applied to the direct marketing data set (as measured by the Gini coefficient calculated on the validation set).…”

“…The data sets are the same as those used in the previous study. 12 A short summary of the data used is given in Table 1. Details on the data sets can be found in Breed 11 .…”

“…Their combined use considers both target and explanatory variables, and is called semi-supervised segmentation (SSS). [11][12][13] It has many similarities with semi-supervised clustering 14 , supervised clustering 15 and semi-supervised semantic segmentation 16 (more used in image processing). For more detail on the differences and similarities see Breed 11 .…”

Predicting take-up of home loan offers using tree-based ensemble models: A South African case study

“…12 In the process of determining how well a predictive modelling technique performs, the lift of the model is considered, where lift is defined as the ability of a model to distinguish between the two outcomes of the target variable (in this paper, take-up vs non-take-up). There are several ways to measure model lift 16 ; in this paper, the Gini coefficient was chosen, similar to measures applied by Breed and Verster 17 . The Gini coefficient quantifies the ability of the model to differentiate between the two outcomes of the target variable.…”