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http://hdl.handle.net/1843/ESBF-9GMN7J
Tipo: | Tese de Doutorado |
Título: | Revenue optimization and customer targeting in daily-deals sites |
Autor(es): | Anisio Mendes Lacerda |
primer Tutor: | Nivio Ziviani |
primer Co-tutor: | Adriano Alonso Veloso |
primer miembro del tribunal : | Berthier Ribeiro de Araujo Neto |
Segundo miembro del tribunal: | Leandro Balby Marinho |
Tercer miembro del tribunal: | Ricardo Baeza-yates |
Cuarto miembro del tribunal: | Wagner Meira Junior |
Resumen: | Daily-deals sites (DDSs), such as Groupon and Peixe Urbano, attract millions of customers in the hunt for offers at significantly reduced prices. The challenge of DDSs is to find the best match between deals and customers while generating as much revenue as possible. One important objective of a DDS is to improve the aggregated value customers give to emails, which should not be seen as spam. This thesis solves three different problems in order to guarantee revenue maximization and customer satisfaction. First, a method for predicting the number of coupons a deal is going to sell is proposed. Second, we present an email prioritization approach. Third, we introduce a new strategy for deals recommendation via email. All three methods improved the results of state-of-the-art algorithms for the tasks being addressed, with gains in precision varying from 7% to 21%, while reducing the number of emails sent in 40% without affecting the number of customers clicking the deals in emails. |
Abstract: | Daily-deals sites (DDSs), such as Groupon and Peixe Urbano, attract millions of customers in the hunt for offers at significantly reduced prices. The challenge of DDSs is to find the best match between deals and customers while generating as much revenue as possible. One important objective of a DDS is to improve the aggregated value customers give to emails, which should not be seen as spam. This thesis solves three different problems in order to guarantee revenue maximization and customer satisfaction. First, a method for predicting the number of coupons a deal is going to sell is proposed. Second, we present an email prioritization approach. Third, we introduce a new strategy for deals recommendation via email. All three methods improved the results of state-of-the-art algorithms for the tasks being addressed, with gains in precision varying from 7% to 21%, while reducing the number of emails sent in 40% without affecting the number of customers clicking the deals in emails. |
Asunto: | Sistema de recomendação Computação Sistemas de recuperação de informação |
Idioma: | Português |
Editor: | Universidade Federal de Minas Gerais |
Sigla da Institución: | UFMG |
Tipo de acceso: | Acesso Aberto |
URI: | http://hdl.handle.net/1843/ESBF-9GMN7J |
Fecha del documento: | 20-dic-2013 |
Aparece en las colecciones: | Teses de Doutorado |
archivos asociados a este elemento:
archivo | Descripción | Tamaño | Formato | |
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an_siomendeslacerda.pdf | 1.77 MB | Adobe PDF | Visualizar/Abrir |
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