Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies’ websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company’s brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual “customers”.
Purpose
– The integrated purpose of the libraries’ communication plan in general is to create and accomplish scientific events aiming, first of all, at covering the extensive demand for the scientific conferences. Their primary objective is to raise the prestigious brand name of their organisation, which constitutes the organizing authority. At the same time, this authority, except for its non-profit charitable profile, aims to financial gains by attracting participants for its sustainability. Furthermore, these academic events have contributed to the utmost dissemination of the library’s brand name to an expanding mass of people to the extent of attracting new visitors (Broady-Preston and Lobo, 2011). One of the qualitative academic events, among others, is the creation of academic-nature events, whose following-up is blocked by a multitude of financial barriers according to the new visitors’ viewpoint. Considering the economic crisis, the purpose of this paper is the creation of interesting, in the science of library, online events, just like the online conferences (Broady-Preston and Swain, 2012).
Design/methodology/approach
– This paper highlights the advantages of the dynamic modelling of systems aimed at developing a successful online conference. In this research, the authors have used the science of design and the research methodology for testing the concept of modeling.
Findings
– This paper examines the interface among several dimensions for the development of dynamic models. The validity and usefulness of those models in the process of decision-making has been confirmed by the usage of dynamic models in various sectors.
Originality/value
– This paper applies the system and the concepts of dynamic modelling, which are pioneering elements as to their nature and evolution.
In recent years, the energy market has seen an increase in small and medium enterprises (SMEs) participating in the sector and providing relevant services to customers. The energy sector SMEs need to acknowledge whether reengineering their marketing strategy by modeling customers’ website behavior could enhance their digital marketing efficiency. Web Analytics refers to the extracted data of customers’ behavior from firms’ websites, a subclass of big data (big masses of uncategorized data information). This study aims to provide insights regarding the impact that energy SMEs’ web analytics has on their digital marketing efficiency as a marketing reengineering process. The paper’s methodology begins with the retrieval of behavioral website data from SMEs in the energy sector, followed by regression and correlation analyses and the development of simulation models with Fuzzy Cognitive Mapping (FCM). Research results showed that customer behavioral data originating from SMEs’ websites can effectively impact key digital marketing performance indicators, such as increasing new visits and reducing organic costs and bounce rate (digital marketing analytics). SMEs in the energy sector can potentially increase their website visibility and customer base by re-engineering their marketing strategy and utilizing customers’ behavioral analytic data.
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