Ana Pereira

Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.

Abstract
Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, Firefly Algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the Firefly Algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.

Abstract
Over the past two decades, Digital Transformation (DT) has been focused on improving businesses, industries, and the general public through significant breakthroughs. This paper examines the significant developments brought forth by DT and how they impact organizations. This analysis explores the impact of Virtual Reality (VR) and the Metaverse on global businesses, taking inspiration from successful case studies such as Netflix, Amazon, and Meta. This study emphasizes the potential of virtual reality and the Metaverse in facilitating remote meetings, training employees, engaging with consumers, and gathering data. Case studies and strategic recommendations are offered for overcoming barriers to the adoption of these digital technologies. The study finishes by addressing the future trajectory of DT and emphasizing the significance of devoting time, commitment, and resources to effectively utilize the range of potential offered by VR and the Metaverse. It highlights the importance for organizations to comprehend and handle this ever-changing environment to remain at the forefront of the digital frontier.

Abstract
This paper focuses on the importance of Business Intelligence (BI) tools in the business context and the urgent need for more effective implementation of time series forecasting models in these resources. It shows the utility and applicability of Sage Enterprise Intelligence (SEI), an integrated BI tool in Enterprise Resource Planning (ERP) Sage, by illustrating how it enhances data analysis and decision-making processes. Additionally, a study will show the application of time series forecasting models: Seasonal AutoRegressive Integrated Moving Average (SARIMA) and additive Holt-Winters to the sales value of a fuel sector company. The research was conducted through a case study in which sales data were collected from 2016 to 2023. The results indicate that neither of the two models exceeded the sales figures reflecting the company's market position. In this case study, both models performed well, with the residuals verifying the assumptions. However, the additive Holt-Winters model had lower errors, which is why it was selected for the final step: forecasting 12 months.