Marketing relations and communication infrastructure development in the banking sector based on big data mining

Purpose: The article aims to study the methodological tools for applying the technologies of intellectual analysis of big data in the modern digital space, the further implementation of which can become the basis for the marketing relations concept implementation in the banking sector of the Russian Federation‘ economy. Structure/Methodology/Approach: For the marketing relations development in the banking sector in the digital economy, it seems necessary: firstly, to identify the opportunities and advantages of the big data mining in banking marketing; secondly, to identify the sources and methods of processing big data; thirdly, to study the examples of the big data mining successful use by Russian banks and to formulate the recommendations on the big data technologies implementation in the digital marketing banking strategy. Findings: The authors‘ analysis showed that big data technologies processing of open online and offline sources of information significantly increases the data amount available for intelligent analysis, as a result of which the interaction between the bank and the target client reaches a new level of partnership. Practical Implications: Conclusions and generalizations of the study can be applied in the practice of managing financial institutions. The results of the study can be used by bank management to form a digital marketing strategy for long-term communication. Originality/Value: The main contribution of this study is that the authors have identified the main directions of using big data in relationship marketing to generate additional profit, as well as the possibility of intellectual analysis of the client base, aimed at expanding the market share and retaining customers in the banking sector of the economy.peer-reviewe

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

A comparison of theory and practice in market intelligence gathering for Australian micro-businesses and SMEs

Recent government sponsored research has demonstrated that there is a gap between the theory and practice of market intelligence gathering within the Australian micro, small and medium businesses (SMEs). Typically, there is a significant amount of information in literature about 'what needs to be done', however, there is little insight in terms of how market intelligence gathering should occur. This paper provides a novel insight and a comparison between the theory and practices of market intelligence gathering of micro-business and SMEs in Australia and demonstrates an anomoly in so far as typically the literature does not match what actually occurs in practice. A model for market intelligence gathering for micro-businesses and SMEs is also discussed

Next best action – a data-driven marketing approach

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe Next Best Action (NBA) is a framework that is built in order to assign to each client three (or more) actions that are considered to be the best actions to perform with the client. These actions can range from product offering to pro-active retention actions and upselling recommendations. It can be a useful tool to generate leads for ongoing campaigns but also an excellent tool for analysis and a driver for the creation of new campaigns, being a key element in Customer Relationship Management (CRM) as a Data-Driven Marketing approach. Initially planned as a joint collaboration between a Bank and an Insurance Company to improve the Bancassurance business model, three versions of the NBA were built with the first two being tested on a campaign setting showing promising results. The last version, NBA 3.0, later became a sole project of the Insurance Company due to GPDR compliance policies and due to time constraints could not be evaluated

INSIGHTQUEST FROM DATA

Data mining is the process of discovering useful patterns and insights from large datasets, using statistical and machine learning techniques. It involves extracting knowledge from data and transforming it into an understandable structure for further use. Data mining algorithms can be used to analyze various types of data such as text, images, and videos, and can be applied to various domains such as finance, healthcare, and marketing. Data mining has many practical applications, such as customer segmentation, fraud detection, predictive modeling, and recommendation systems. It has become an important tool for businesses and organizations to gain insights from their data and make data-driven decisions. However, it also raises concerns about privacy, data protection, and ethics, as it involves handling large amounts of sensitive data. Therefore, ethical and responsible use of data mining techniques is crucial to ensure the protection of individual rights and the preservation of social values