Feature selection strategies for improving data-driven decision support in bank telemarketing

The usage of data mining techniques to unveil previously undiscovered knowledge has been applied in past years to a wide number of domains, including banking and marketing. Raw data is the basic ingredient for successfully detecting interesting patterns. A key aspect of raw data manipulation is feature engineering and it is related with the correct characterization or selection of relevant features (or variables) that conceal relations with the target goal. This study is particularly focused on feature engineering, aiming at the unfolding features that best characterize the problem of selling long-term bank deposits through telemarketing campaigns. For the experimental setup, a case-study from a Portuguese bank, ranging the 2008-2013 year period and encompassing the recent global financial crisis, was addressed. To assess the relevance of such problem, a novel literature analysis using text mining and the latent Dirichlet allocation algorithm was conducted, confirming the existence of a research gap for bank telemarketing. Starting from a dataset containing typical telemarketing contacts and client information, research followed three different and complementary strategies: first, by enriching the dataset with social and economic context features; then, by including customer lifetime value related features; finally, by applying a divide and conquer strategy for splitting the problem in smaller fractions, leading to optimized sub-problems. Each of the three approaches improved previous results in terms of model metrics related to prediction performance. The relevance of the proposed features was evaluated, confirming the obtained models as credible and valuable for telemarketing campaign managers.A utilização de técnicas de data mining para a descoberta de conhecimento tem sido aplicada nos últimos anos a uma grande variedade de domínios, incluindo banca e marketing. Os dados no seu estado primitivo constituem o ingrediente básico para a deteção de padrões de informação. Um aspeto chave da manipulação de dados em bruto consiste na "engenharia de atributos", que compreende uma correta definição e seleção de atributos relevantes (ou variáveis) que se relacionem com o alvo da descoberta de conhecimento. Este trabalho foca-se numa abordagem de "engenharia de atributos" para definir as variáveis que melhor caraterizam o problema de vender depósitos bancários a prazo através de campanhas de telemarketing. Sendo um estudo empírico, foi utilizado um caso de estudo de um banco português, abrangendo o período 2008-2013, que inclui os efeitos da crise financeira internacional. Para aferir da importância deste problema, foi realizada uma inovadora análise da literatura recorrendo a text mining e ao algoritmo latent Dirichlet allocation, confirmando a existência de uma lacuna nesta matéria. Utilizando como base um conjunto de dados de contactos de telemarketing e informação sobre os clientes, três estratégias diferentes e complementares foram propostas: primeiro, os dados foram enriquecidos com atributos socioeconómicos; posteriormente, foram adicionadas características associadas ao valor do cliente ao longo do seu tempo de vida; finalmente, o problema foi dividido em problemas mais específicos, permitindo abordagens otimizadas a cada subproblema. Cada abordagem melhorou as métricas associadas à capacidade preditiva do modelo. Adicionalmente, a relevância dos atributos foi avaliada, confirmando os modelos obtidos como credíveis e valiosos para gestores de campanhas de telemarketing

Using data mining for bank direct marketing: an application of the CRISP-DM methodology

The increasingly vast number of marketing campaigns over time has reduced its effect on the general public. Furthermore, economical pressures and competition has led marketing managers to invest on directed campaigns with a strict and rigorous selection of contacts. Such direct campaigns can be enhanced through the use of Business Intelligence (BI) and Data Mining (DM) techniques. This paper describes an implementation of a DM project based on the CRISP-DM methodology. Real-world data were collected from a Portuguese marketing campaign related with bank deposit subscription. The business goal is to find a model that can explain success of a contact, i.e. if the client subscribes the deposit. Such model can increase campaign efficiency by identifying the main characteristics that affect success, helping in a better management of the available resources (e.g. human effort, phone calls, time) and selection of a high quality and affordable set of potential buying customers

Enhancing bank direct marketing through data mining

The financial crisiscreated pressure on banksdue to credit restriction, increasing competition for deposits retention and demanding efficiency improvements of direct marketing campaigns. Our research conducted a data mining project on direct marketing campaigns for depositssubscriptionsby using recent data of a Portuguese retail bank. We used the Support Vector Machine (SVM) data mining technique for modeling and evaluated it through a sensitive analysis. The findings revealed previously unknown valuable knowledge, such as the best months for campaigns to occur, and optimal call duration. Such knowledge can be used to improve campaign efficiency

Unveiling island tourism in cape verde through online reviews

Oliveira, C., Rita, P., & Moro, S. (2021). Unveiling island tourism in cape verde through online reviews. Sustainability (Switzerland), 13(15), 1-14. [8167]. https://doi.org/10.3390/su13158167This paper is focused on research addressing a large quantity of data extracted from online reviews written by tourists visiting islands. These were extracted from Trip Advisor regarding island tourist destinations since there is a gap in the scientific literature using this approach on island tourism. The Islands of the Sun, Boa Vista and Sal, of Cape Verde, a Small Island Developing State (SIDS), were the targets of this investigation. After applying text mining to a large dataset, results are discussed, including from the perspectives of hotels, restaurants, and tourist attractions. For example, the beach is the main tourist attraction in both islands, but whereas in Boa Vista, tours on quad bikes constitute a major tourist activity, its equivalent in Sal is actually diving. The location of hotels near the beach is a big plus for tourists who also emphasize their human interaction with staff members in both hotels and restaurants.publishersversionpublishe

Analysis of online reviews in the airlines sector

Rita, P., Moro, S., & Cavalcanti, G. (2022). The impact of COVID-19 on tourism: Analysis of online reviews in the airlines sector. Journal of Air Transport Management, 104(September), [102277]. https://doi.org/10.1016/j.jairtraman.2022.102277 -------- Funding Information: Paulo Rita was supported through the FCT (Fundação para a Ciência e a Tecnologia) under UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.This research aimed to understand how airline companies are addressing the crisis generated by the Covid-19 pandemic and handling issues like cancellations and customer (dis)satisfaction. Research on online reviews from the most popular tourism website, TripAdvisor, was conducted through the collection of review posts from the leading 10 worldwide airline groups by number of passengers. These reviews were extracted from the sector's most impacted period during the pandemic – from the date where the first travel restrictions were imposed until the date where they began to be lifted again (from March to May 2020), which consequently led to a greater number of posted and shared reviews. A total of 885 reviews were collected and analysed with the help of the Python-based sentiment analysis tool VADER. Results showed a very negative trend, which was mainly caused by issues related to refund policies and process, confirming the reported pandemic impact on this sector. Low-cost airlines revealed a lower customer satisfaction rate when compared to traditional ones, while most of the posts were related to Loyalty/Competitiveness, which affected brands' overall equity. This study enables to better understand, from the customers' perspective, how airlines were able to deal with the severe impact of the COVID-19 pandemic. Through such knowledge and subsequent critical discussion, we unveil the critical issues that have led to unsatisfied customers, helping to build up the body of knowledge on airlines’ recovery after the pandemic.publishersversionpublishe

What drives job satisfaction in IT companies?

Moro, S., Ramos, R. F., & Rita, P. (2021). What drives job satisfaction in IT companies? International Journal Of Productivity And Performance Management, 70(2), 391-407. [Advanced online publication on 19 March 2020]. https://doi.org/10.1108/IJPPM-03-2019-0124Purpose: Strategic goal achievement in every sector of a company relies fundamentally on the firm's employees. This study aims to disclose the factors that spur employees of major Information Technology (IT) companies in the United States (US). Design/methodology/approach: In this paper, 15,000 reviews from the top 15 United States IT companies were collected from the social media platform Glassdoor to uncover the factors that satisfy IT employees. To learn the most meaningful features that influence the scores, positive and negative remarks, as well as advice to the management team, were analyzed through a support vector machine. Findings: Results highlight a positive attitude of coworkers, contributing to a positive environment and job satisfaction. However, unsatisfied IT employees reveal that work exhaustion is the main reason for their job dissatisfaction. Practical implications: IT human resource departments can use these valuable insights to align their strategies in accordance with their employees' desires and expectations in order to thrive. Originality/value: The study highlights the relevance of IT companies to understand the reasons behind their employees' satisfaction. Up until now, little is known concerning the variants of job satisfaction among IT employees, enriching the understanding in this particular professional area.authorsversionpublishe

A data mining approach for bank telemarketing using the rminer package and R tool

Due to the global financial crisis, credit on international markets became more restricted for banks, turning attention to internal clients and their deposits to gather funds. This driver led to a demand for knowledge about client’s behavior towards deposits and especially their response to telemarketing campaigns. This work describes a data mining approach to extract valuable knowledge from recent Portuguese bank telemarketing campaign data. Such approach was guided by the CRISP- -DM methodology and the data analysis was conducted using the rminer package and R tool. Three classification models were tested (i.e., Decision Trees, Naïve Bayes and Support Vector Machines) and compared using two relevant criteria: ROC and Lift curve analysis. Overall, the Support Vector Machine obtained the best results and a sensitive analysis was applied to extract useful knowledge from this model, such as the best months for contacts and the influence of the last campaign result and having or not a mortgage credit on a successful deposit subscription

A text mining and topic modelling perspective of ethnic marketing research

This study presents an enhanced automated approach based on literature analysis and synthesis for establishing the dimensions of the ethnic marketing literature, covering a set of 239 journal articles published by nine major publishers. The approach reported is enhanced by two novel procedures to address previously identified limitations, namely: definition of a relevant dictionary based on both a sufficient lexicon extracted from a definition of the core theme and a conditional dictionary, with related but non-core terms; and a visually appealing pictorial representation to summarize the discovered topics. The application of the method to ethnic marketing indicates that ethnic marketing research is characterized by high conceptual heterogeneity, although a clear definition of "ethnic marketing" is imperative for research development. Overall, the paper advances an approach with considerable scalability advantages when compared with extant approaches, an important issue to consider when textual sources become big data.- (undefined

A cross-cultural case study of consumers' communications about a new technological product

Using a case-study based approach, this research contributes to the standardisation versus adaptation debate in global marketing. It analyses the influence of the local culture dimension reflected in consumers’ comments in the Facebook platform regarding a new global technological product. Galaxy S8/S8+, launched worldwide in 2017. Consumers’ comments about this new smartphone were gathered and analysed for three cultural distinct English-speaking countries: Australia, India, and South Africa. The analysis’ procedure consisted of a text mining and topic modelling approach, including sentiment classification analysis, to discern and understand consumers’ responses to global brand communications. The findings indicate that cultural aspects still play a key role in consumers’ reactions to the product in each country, justifying the continued need for marketing strategies that conflate pursuing economies of scale with accounting for the cultural sensitivities of demand at country level. Evidence of consumers attitudes’ and behaviours’ homogenisation across countries is still limited