Fee schedule design when debt collection is price sensitive

Abstract

When payment for a good (or service) is not required to be done immediately after its delivery, collection in full may not be possible. This is critical for post-paid billing systems such as mobile phone, water, electricity, and other utility service providers. In order to maximize their revenue, firms of these characteristics must not focus solely on increasing sales; instead, more importantly, they should focus on increasing collection. Thus, pricing should consider not only the good’s demand elasticity, but also market’s payment capacity—or its willingness to pay. We propose a linear programming model that can be used to maximize a firm’s revenue collection over a one-period decision horizon. Our model works by segmenting customers based on their consumption level and assumes that customer’s willingness to pay is similar within each segment. Prices are, then, found for each customers’ segment. A case study is provided and its solution analyzed to develop further insights about the model. Keywords— Pricing, willingness to pay, collected revenue Digital Object Identifier (DOI): http://dx.doi.org/10.18687/LACCEI2015.1.1.244 ISBN: 13 978-0-9822896-8-6 ISSN: 2414-6668 Fee Schedule Design when Debt Collection is Price Sensitive Andres G. Abad, Ph.D.1, and Cinthia C. Perez, Ph.D.1 1Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Ecuador, agabad@espol.edu.ec, ccperez@espol.edu.ec Abstract– When payment for a good (or service) is not required to be done inmediately after its delivery, collection in full may not be possible. This is critical for post-paid billing systems such as mobile phone, water, electricity, and other utility service providers. In order to maximize their revenue, firms of these characteristics must not focus solely on increasing sales; instead, more importantly, they should focus on increasing collection. Thus, pricing should consider not only the good’s demand elasticity, but also market’s payment capacity—or its willingness to pay. We propose a linear programming model that can be used to maximize a firm’s revenue collection over a one-period decision horizon. Our model works by segmenting customers based on their consumption level and assumes that customer’s willingness to pay is similar within each segment. Prices are, then, found for each customers’ segment. A case study is provided and its solution analyzed to develop further insights about the model. Keywords– Pricing, willingness to pay, collected revenue. When payment for a good (or service) is not required to be done inmediately after its delivery, collection in full may not be possible. This is critical for post-paid billing systems such as mobile phone, water, electricity, and other utility service providers. In order to maximize their revenue, firms of these characteristics must not focus solely on increasing sales; instead, more importantly, they should focus on increasing collection. Thus, pricing should consider not only the good’s demand elasticity, but also market’s payment capacity—or its willingness to pay. We propose a linear programming model that can be used to maximize a firm’s revenue collection over a one-period decision horizon. Our model works by segmenting customers based on their consumption level and assumes that customer’s willingness to pay is similar within each segment. Prices are, then, found for each customers’ segment. A case study is provided and its solution analyzed to develop further insights about the model. Keywords– Pricing, willingness to pay, collected revenue.