Use este identificador para citar ou linkar para este item:
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 |
Primeiro Orientador: | Nivio Ziviani |
Primeiro Coorientador: | Adriano Alonso Veloso |
Primeiro membro da banca : | Berthier Ribeiro de Araujo Neto |
Segundo membro da banca: | Leandro Balby Marinho |
Terceiro membro da banca: | Ricardo Baeza-yates |
Quarto membro da banca: | Wagner Meira Junior |
Resumo: | 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. |
Assunto: | Sistema de recomendação Computação Sistemas de recuperação de informação |
Idioma: | Português |
Editor: | Universidade Federal de Minas Gerais |
Sigla da Instituição: | UFMG |
Tipo de Acesso: | Acesso Aberto |
URI: | http://hdl.handle.net/1843/ESBF-9GMN7J |
Data do documento: | 20-Dez-2013 |
Aparece nas coleções: | Teses de Doutorado |
Arquivos associados a este item:
Arquivo | Descrição | Tamanho | Formato | |
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an_siomendeslacerda.pdf | 1.77 MB | Adobe PDF | Visualizar/Abrir |
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