A machine learning approach to assess price sensitivity with application to automobile loan segmentation

• Published in: Applied soft computing 2019-03, Vol.76, p.390-399
• Main Author: Arevalillo, Jorge M.
• Format: Article
• Language: English
• Subjects: Conditional inference trees; Machine learning; Model based recursive partitioning; Price sensitivity; Random forests
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.12.012

Price sensitivity is an outstanding business issue in companies and organizations that aim to undertake optimal managerial decisions for increasing sales and / or revenue. Hence, price sensitivity assessment has become an in fashion problem that has attracted the attention of a wide variety of actors and business units within the organizations. In this paper we propose a machine learning approach to assess price sensitivity for an automobile loan portfolio in order to get a segmentation revealing the existence of groups with differential price sensitivity, defined by their differential purchase responses against changes in the loan interest rate. The proposed method combines the power of conditional inference trees, random forests and model based recursive partitioning algorithms to implement a process for price group finding, variable selection and price sensitivity segmentation in order uncover such differential groups and characterize them by asset and product characteristics and by customer attributes as well. The resulting segmentation will define high sensitivity groups, where interest rate reductions can be recommended in order to increase sales, as well as nearly insensitive groups for which a price strategy that increases the interest rate is expected to have slight impact on loan disbursements. •A machine learning approach is proposed to assess price sensitivity (PS).•The approach combines Random Forests, Classification Trees and MOB algorithms.•As a result, it provides a segmentation that defines groups with differential PS.•When applied to automobile loans we get useful insights for pricing management.