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.

Cálculo dos níveis ótimos de rentabilidade para o desempenho energético de habitações residenciais unifamiliares objeto de reabilitação

Dissertação de mestrado em Construção e Reabilitação Sustentáveis

Rentabilidade económica de medidas de melhoria do desempenho energético e de produção - utilização de energias renováveis

Dissertação de mestrado integrado em Engenharia Civil

Otimização de custos, consumos energéticos e emissões de gases com efeito de estufa na reabilitação de edifícios residenciais de baixa densidade

Dissertação de mestrado integrado em Engenharia Civil

O impacto da gestão de fundo de maneio na rendibilidade das empresas durante a crise financeira: evidência europeia

Dissertação de mestrado em Finanças

A carta do presidente e o relato voluntário do capital intelectual: análise das empresas do segmento do novo mercado da BM&FBOVESPA

Dissertação de mestrado em Contabilidade

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.