Analytics como uma ferramenta para Consumer Insights

• Ano de defesa: 2017
• Autor(a) principal: Carvalho, André Silva de
• Orientador(a): Francisco, Eduardo de Rezende
• Banca de defesa: Ponchio, Mateus Canniatti, Figueiredo, Julio C. B., Kugler, José Luiz Carlos, Zambaldi, Felipe
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Escola Superior de Propaganda e Marketing
• Programa de Pós-Graduação: Programa de Mestrado Profissional em Comportamento do Consumidor
• Departamento: ESPM::Pós-Graduação Stricto Sensu
• País: Brasil
• Palavras-chave em Português: Consumer Insights; dados não estruturados; viés cognitivo; análise de redes sociais; Text Mining
• Palavras-chave em Inglês: Consumer Insights; unstructured data; cognitive bias; social network analysis.; Text Mining
• Área do conhecimento CNPq: CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO
• Link de acesso: http://tede2.espm.br/handle/tede/267

Being innovative in a more and more competitive market can be anything but trivial. There is a complex variables system to be taken into account throughout an innovation process, and hardly ever will there be enough data to support a research or decision. It is always possible to turn to human inference, or cognitive bias, when enough data is not available, or when time for decision-making is scarce. Consumer Insight technique has been used for this research purpose and aimed at lowering cognitive bias, seeking to find out what are consumers' wishes and needs so that decision-making or innovation could be supported. This paper proposes to mitigate the influence of cognitive bias, by means of data analysis techniques, in search for patterns which can identify opportunities to give both decision-making and search for innovation some support. In order to achive this purpose, unstructured data from 26.514 telephone talks had in a big financial market company between 01.12.2016 e 31.12.2016 have been used. Analysis has been carried out with the transcript from voice into text concomitantly with Text Mining and Social Network analysis. The results have led us to identify main client demands from a sales perspective, cancellation resquest, as well as the reason for inefficiency in offering new products from elements of higher centrality identified in the word association networks. It is implied that the combined use of analytical techniques applied to unstructured data may give rise to findings in which cognitive bias is lower.