On the length of bank-firm relationships: an empirical application to a major French bank

NIPE - WP 01/ 2016

GIS application in water resources master planning

Due to water scarcity, it is important to organize and regulate water resources utilization to satisfy the conflicting water demands and needs. This paper aims to describe a comprehensive methodology for managing the water sector of a defined urbanized region, using the robust capabilities of a Geographic Information System (GIS). The proposed methodology is based on finding alternatives to cover the gap between recent supplies and future demands. Nablus which is a main governorate located in the north of West Bank, Palestine, was selected as case study because this area is classified as arid to semi-arid area. In fact, GIS integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographic information. The resulted plan of Nablus represents an example of the proposed methodology implementation and a valid framework for the elaboration of a water master plan.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.