Publicação
Optimizing Banking Offers: A Next Best Offer Recommendation System
| Resumo: | In the rapidly evolving banking sector, personalized marketing strategies are critical for customer engagement and retention. This study explores the development and implementation of a Next Best Offer (NBO) recommendation system designed to predict the most probable products or services a customer is likely to purchase next. Leveraging a multimodel approach, the system integrates diverse predictive models, including churn and acquisition models, spanning various products within the Bank's extensive portfolio. While integrating different model types presents initial challenges, it ultimately enhances the system's robustness by enabling it to effectively handle diverse prediction scenarios. The core process involves multiplying the probability outputs from these models for each client by the natural rate of purchase and the specific characteristics of each banking product. This approach generates a score indicating the likelihood of a client purchasing a particular product. The final output of the NBO system is a prioritized list of products for each client, ranked according to their expectancy purchase. This ranking facilitates targeted marketing efforts, thereby optimizing customer engagement and product uptake. This study underscores the efficacy of a multi-model NBO system in enhancing predictive accuracy and delivering personalized banking solutions. |
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| Autores principais: | Gama, Tomás Bourdain da |
| Assunto: | Recommendation System Banking Sector Next Best Offer Customer Engagement Targeted Marketing |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | In the rapidly evolving banking sector, personalized marketing strategies are critical for customer engagement and retention. This study explores the development and implementation of a Next Best Offer (NBO) recommendation system designed to predict the most probable products or services a customer is likely to purchase next. Leveraging a multimodel approach, the system integrates diverse predictive models, including churn and acquisition models, spanning various products within the Bank's extensive portfolio. While integrating different model types presents initial challenges, it ultimately enhances the system's robustness by enabling it to effectively handle diverse prediction scenarios. The core process involves multiplying the probability outputs from these models for each client by the natural rate of purchase and the specific characteristics of each banking product. This approach generates a score indicating the likelihood of a client purchasing a particular product. The final output of the NBO system is a prioritized list of products for each client, ranked according to their expectancy purchase. This ranking facilitates targeted marketing efforts, thereby optimizing customer engagement and product uptake. This study underscores the efficacy of a multi-model NBO system in enhancing predictive accuracy and delivering personalized banking solutions. |
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