Business intelligence through data visualization: a case study using marketing campaign dataset

The current data-driven business environment has seen the development of Business Analytics as an essential instrument of learning consumer behavior and improving marketing plans. This paper discusses the manner in which systematic deployment of descriptive, predictive and prescriptive analytics can assist organizations to accumulate, process and analyze high volumes of consumer-related data to understand more about their preferences, purchasing trends, attitudes and decision-making mechanisms. Marketers can determine which segments of their target audience to target, customize marketing messages and optimize price and promotion tactics and enhance customer engagement by turning raw data of several sources including social media, online transactions and customer relationship management systems into actionable insights. The paper indicates that marketing based on analytics will enable companies to know what customers need, predict demand and better respond to the evolving market trends which will enhance customer satisfaction and loyalty to the brand. Moreover, evidence-based decision-making, a stronger marketing strategy formulation as a result of integrating business analytics into marketing strategies, increases opportunities to gain returns on the marketing investment and sustainable competitive advantage. The paper has concluded that the importance of gaining knowledge on consumer behavior via business analytics is no longer a choice but a necessity among organizations who have the desire to structure efficient, customer-focused and performance-driven marketing strategies in a competitive business environment.Top of Form

The aim of this study was to establish digital marketing strategies using the flywheel model in an enterprise in the cycling retail sector. The methodology followed a qualitative approach, using descriptive and bibliographic types of research. A literature review was conducted, followed by an interview to understand the current state of the venture's strategies, and the inductive methodwas used to generate an analysis of the results. Along the same lines, a thematic analysis of the interview was used using the qualitative analysis software Atlas TI, supported by the bibliographic management software Zotero and Publish or Perish to support the research process. The results show that the venture has a digital and relationship marketing strategy, which evidences opportunities for improvement. Subsequently, the flywheel model was used to structure these strategies as a proposal for the venture. The conclusions show that digital marketing strategies contribute to the commercial management of organizations. In addition, the venture shows opportunities to optimizeits digital marketing strategies through the support of the proposals resulting from the research.

Startupreneurs increasingly rely on technology to manage their business, especially in using e-commerce applications and digital business marketing strategies. This research investigates the influence of e-commerce applications and digital business marketing on startup entrepreneur performance. The research method used is an online survey of 150 startup entrepreneurs in Indonesia who are active in various industries. Data was collected using a questionnaire that measures the level of use of e-commerce applications, digital business marketing practices, and startupreneur performance based on factors such as revenue growth, customer satisfaction, and market visibility. The results of the analysis show that the use of e-commerce applications has a significant favourable influence on startupreneur performance. Startupreneurs who actively use e-commerce applications tend to have higher revenue growth and better levels of customer satisfaction. Additionally, digital business marketing strategies were also found to positively affect startup business performance, especially in increasing market visibility and broader market penetration. This research makes an essential contribution to understanding how technology and digital marketing strategies can help improve startupreneur performance. The practical implication of this research is the importance of integrating e-commerce applications and digital business marketing in startup business growth strategies. Steps to improve startupreneur performance could involve investing in digital technology and improving skills in managing online marketing strategies.

In the rapidly evolving landscape of the hotel & hospitality industry, digital marketing strategies have become essential tools for driving growth and enhancing competitiveness among hotels. This research paper explores the diverse approaches that hotels can adopt to control digital marketing for market expansion. The study begins by analysing current trends in consumer behaviour, emphasizing the increasing confidence on online platforms for travel planning and booking. It highlights the importance of maintaining a strong online presence, which includes optimizing hotel websites, strategically utilizing social media, and implementing effective search engine optimization (SEO) techniques. Moreover, the paper explores the role of data analytics in understanding customer preferences and tailoring marketing efforts accordingly. By employing customer relationship management (CRM) systems and data-driven insights, hotels can develop customized marketing campaigns that resonate with their target audience. The research also examines the impact of online reviews and reputation management, highlighting the necessity for hotels to actively engage with guests and respond to feedback in real time. Moreover, the paper discusses the incorporation of emerging technologies, such as artificial intelligence and chatbots, in enhancing customer service and streamlining operations. These innovations not only improve guest experiences but also contribute to operational efficiency, ultimately driving revenue growth. The findings suggest that a comprehensive digital marketing strategy, encompassing various channels and technologies, is crucial for hotels aiming to thrive in a competitive market. The paper concludes by presenting a framework for developing holistic and adaptive digital marketing strategies personalized to the dynamic needs of the hotel industry. It also underscores the importance of digital literacy, strategic investment in technology, and continuous customer feedback integration. By aligning marketing innovation with customer expectations, hotels can enhance operational efficiency, brand loyalty, and long-term profitability in the digital age.

This study aims to provide a comprehensive overview of strategic marketing management in contemporary business environments, focusing on the importance of customer-centric strategies, data-driven approaches to market segmentation, targeting, and positioning (STP), effective brand management, and the increasing significance of digital marketing. A thorough review of existing literature on strategic marketing management, including seminal works and recent research articles, was conducted to synthesize key insights and perspectives. The analysis encompassed various aspects such as customer-centricity, market segmentation, targeting, positioning, brand management, and digital marketing strategies. The findings highlight the evolving nature of marketing practices, driven by shifts in consumer behavior, technological advancements, and competitive dynamics. Key findings include the imperative of prioritizing customer-centricity, approaches to STP strategies, the importance of effective brand management in building strong brand equity, and the growing significance of digital marketing in reaching and engaging with target audiences. The implications of this study are manifold. For practitioners, the findings underscore the importance of aligning marketing strategies with evolving consumer needs and preferences, leveraging data analytics and technology to optimize marketing effectiveness, investing in effective brand management practices, and embracing digital marketing channels and technologies. From a theoretical standpoint, this study contributes to the existing body of knowledge by providing empirical evidence and insights into contemporary practices and challenges in strategic marketing management. It also highlights the need for interdisciplinary collaboration between scholars and practitioners to address complex marketing challenges and drive innovation in marketing practices.

This study aims to comprehensively examine the role of social media as a digital marketing strategy in transforming consumer behavior patterns, with a primary focus on the effectiveness of social media platforms at each stage of the purchase decision-making process. Using a literature review method and a qualitative approach, this research analyzes various findings from relevant literature to understand the dynamics of interaction between digital marketing strategies on social media and changes in contemporary consumer behavior. The results show that social media plays a strategic role in building brand awareness through the dissemination of engaging visual content, collaborations with influencers, and the delivery of personalized marketing messages. In the context of digital marketing strategy, social media facilitates the consideration stage by providing easy access to product reviews, recommendations from social networks, and direct interactions between consumers and brands. During the evaluation and decision-making stages, digital marketing strategies on social media reinforce consumer trust through social proof such as testimonials and positive reviews, as well as enabling retargeting based on consumer behavioral data. Furthermore, in the post-purchase stage, digital marketing strategies on social media serve as a means of fostering customer loyalty through responsive feedback management and the formation of active customer communities. This study concludes that social media as a digital marketing strategy has evolved into a holistic marketing ecosystem, not only transforming purchasing behavior but also creating long-term emotional relationships between brands and consumers. These findings provide important contributions to the development of more effective, ethical, and sustainable digital marketing strategies in the current era of digital transformation.

With the rapid advancement of information technology, business intelligence and data analysis have become integral to the success of modern enterprises. Through the utilization of various computing technologies, organizations can gain a better understanding of their vast amounts of data and optimize their business decisions. Machine learning technology, in particular, has presented significant potential for enterprises. By analyzing historical sales data and relevant factors, machine learning can predict the sales volume of goods. Commonly used algorithms in machine learning for this purpose include regression algorithms and neural network algorithms. By establishing mathematical models, future sales trends can be accurately forecasted. These algorithms have the capability to automatically identify hidden patterns and trends within the data, enabling enterprises to predict demand and adjust their production and supply chain strategies accordingly, thus better meeting market demand. By integrating regression algorithms and neural network algorithms with enterprise data, managers can obtain real-time and precise sales forecasts. These forecast results provide robust support for enterprise decision-making, enabling operators to formulate more informed marketing strategies, optimize inventory management, and make accurate production plans during peak demand periods. Additionally, by analyzing sales data and other relevant information, enterprises can identify key factors that influence sales, such as product characteristics, market trends, and consumer behavior. This deeper understanding of the market and customer needs allows organizations to enhance their competitiveness further. The application of machine learning technology, specifically regression algorithms and neural network algorithms, coupled with comprehensive data analysis, empowers enterprises to make more accurate sales forecasts. These forecasts aid in strategic decision-making, including marketing strategies, inventory management, and production planning, while also enabling businesses to gain valuable insights into market dynamics and customer preferences, ultimately strengthening their competitive advantage.

In the rapidly evolving digital landscape, businesses are increasingly dependent on data to drive strategic decisions and maintain a competitive edge. The integration of data engineering techniques with Business Intelligence (BI) has become a key enabler for organizations to transform raw data into actionable insights. This paper explores how modern data engineering practices, such as Extract, Transform, Load (ETL) pipelines, data lakes, and data warehousing, play a pivotal role in enhancing BI systems. These practices streamline the process of data extraction from diverse sources, data transformation for consistency, and storage in optimized repositories to facilitate analysis. Moreover, the implementation of real-time data processing and cloud-based solutions has revolutionized how businesses manage large-scale datasets, enabling the handling of both structured and unstructured data with increased speed and flexibility. The rise of big data technologies such as Hadoop, Spark, and NoSQL databases further extends the ability to perform complex queries and predictive analytics, empowering decision-makers with timely, accurate insights. This abstract underscores the importance of building scalable, efficient data infrastructures through advanced data engineering methodologies. By integrating these techniques, businesses can ensure data quality, improve operational efficiency, and support comprehensive BI systems that drive innovation. Ultimately, this leads to more informed decision-making, enhanced customer experiences, and greater adaptability in the face of market changes. The effective combination of data engineering and BI is critical for leveraging data as a strategic asset in today’s competitive business environment.

With the booming of the digital economy, business marketing strategies are undergoing profound changes, especially in the outdoor sports brand space. This paper takes ARCTERYX as an example to discuss the implementation and effectiveness of its digital marketing strategy. The study begins by defining the concept of digital marketing, emphasizing its data-driven and multi-channel interactive features. Using big digital technology, the brand of ARCTERYX can accurately locate its target audience, analyze consumer behavior, develop individualized marketing strategies, and enhance brand influence. In addition, the ARCTERYX actively utilized social media to create viral marketing campaigns such as the “ARCTERYX Shower Challenge”, which attracted a large amount of attention and created extensive brand communication. Social media not only enhances consumers’ sense of participation but also stimulates a higher interaction rate through the “herd mentality” and promotes brand recognition. Social media not only enhances consumers’ sense of participation but also stimulates a higher interaction rate through the “herd mentality” and promotes brand identity. To sum up, the success of Beginner’s brand lies in the combination of data analysis and consumer psychology, and the brand’s image and sales growth through innovative digital strategy and precise market positioning. This case serves as an important benchmark for other brands to follow in digital marketing and indicates the future direction of marketing.

With the booming of the digital economy, business marketing strategies are undergoing profound changes, especially in the outdoor sports brand space. This paper takes ARCTERYX as an example to discuss the implementation and effectiveness of its digital marketing strategy. The study begins by defining the concept of digital marketing, emphasizing its data-driven and multi-channel interactive features. Using big digital technology, the brand of ARCTERYX can accurately locate its target audience, analyze consumer behavior, develop individualized marketing strategies, and enhance brand influence. In addition, the ARCTERYX actively utilized social media to create viral marketing campaigns such as the “ARCTERYX Shower Challenge”, which attracted a large amount of attention and created extensive brand communication. Social media not only enhances consumers’ sense of participation but also stimulates a higher interaction rate through the “herd mentality” and promotes brand recognition. Social media not only enhances consumers’ sense of participation but also stimulates a higher interaction rate through the “herd mentality” and promotes brand identity. To sum up, the success of Beginner’s brand lies in the combination of data analysis and consumer psychology, and the brand’s image and sales growth through innovative digital strategy and precise market positioning. This case serves as an important benchmark for other brands to follow in digital marketing and indicates the future direction of marketing.

Background/Purpose: Advancement of technology has been proven in every field. To establish a good business or any organization is very crucial nowadays. Businesses are moving tremendously and competitors also high in number. It is very important to take a good decision based on reviews and feedback of customer, etc. By combining the strength of technology and business knowledge, Business Intelligence allows you to make good decisions that are fully informed and stay one step ahead of the competition. With the help of business intelligence tools businesses can use their data in a better way. Business intelligence incorporates Data mining, Data Analytic, Data Visualization and Machine learning to help organization for the analysis of data. This article provides the analysis of Business intelligence and machine learning techniques used in e-commerce website and ABCD framework to inspect the key factors. Objective: Analyses the business intelligence technology and sentiment analysis on E-commerce website. Design/Methodology/Approach: The information and details for this case study is obtained from different scholarly articles published in various journals and company websites. Findings/Result: The study of this paper delivers the importance of customer behavior and how it helps in growth of the industry using machine learning and business intelligence. Originality/Value/Novelty: The result of this paper gives an explanation of business intelligence and machine learning approach for customer feedback in e-commerce companies and advantages of e-commerce websites. Paper Type: Case study paper to study the advantages of business intelligence and machine learning for customer satisfaction.

In this savvy world of technology, most consumers want something that is quick, convenient, and can fulfill their needs. Hence, it is parallel to the businesses nowadays that have the tendency to use online platforms to market their products. Thus, social media becomes one of the most popular online selling mediums among business firms. In a social media marketing strategy, one of the most important elements that needs to be highlighted by the firm is the content marketing itself. In other words, to succeed in a social media marketing strategy, it is necessary for the firm to provide high-quality content marketing for consumers. Considering that nowadays most consumers in the world use social media as one of the mediums through which they shop, the author suggests Social Media Content Marketing (SMCM) plays a role in influencing consumer behaviour. Social media will work more effectively if the content marketing displayed is of high quality and meets the needs of consumers who buy online. Therefore, this SMCM model is one of the digital marketing strategies that need to be emphasized in online marketing, which in turn will have a positive impact on consumer behaviour. This paper will discuss further the SMCM on online purchases.

This study investigated the impact of Brand Image, Courtesy, and Religiosity on Purchase Decisions among Gen Z consumers in Central Java Province. A quantitative research approach was employed, utilizing Structural Equation Modeling (SEM) to analyze data collected from 400 respondents. The findings indicated that both Brand Image and Courtesy had significant positive effects on Purchase Decisions. Additionally, Religiosity was found to mediate these relationships significantly, highlighting its crucial role in shaping consumer behavior. These results suggest that integrating cultural and religious values into marketing strategies can enhance customer engagement and loyalty. The study provides new insights into the interplay between brand perception, interpersonal interactions, and personal values in influencing consumer decisions. This research underscores the importance of culturally and religiously sensitive marketing practices for effectively engaging and retaining customers. The implications of these findings are significant for both marketers and academics, providing a foundation for future research in consumer behavior and marketing strategy.

The rapid advancement of Artificial Intelligence (AI) has significantly transformed digital consumer behaviour and reshaped contemporary marketing strategies. This study examines the evolving relationship between AI technologies and digital consumer engagement, focusing on how data-driven intelligence influences marketing decision-making and organizational strategy. The research adopts a bibliometric research design to analyse scholarly literature related to digital consumer behaviour and artificial intelligence in marketing. Data were extracted from the Scopus database covering the period from 2021 to 2025, ensuring the inclusion of recent developments in AI-driven marketing practices. Using the PRISMA framework, a systematic screening process was conducted, where 612 initial records were identified, reduced to 438 after removing duplicates, and further filtered to 248 relevant articles included in the final analysis. The selected publications were analysed using VOSviewer software to generate keyword co-occurrence network visualizations, enabling the identification of major research clusters and thematic relationships within the literature. The findings reveal five dominant research themes: artificial intelligence and data-driven analytics, digital marketing and social media engagement, consumer behaviour and customer experience, technology acceptance and digital transformation, and data privacy with ethical AI governance. The results indicate that AI-driven technologies such as machine learning, predictive analytics, and recommendation systems are increasingly enabling firms to develop hyper-personalized marketing strategies and enhance customer engagement across digital platforms. Furthermore, the study highlights the importance of human–AI collaboration, emphasizing that while AI enhances analytical capabilities and decision-making efficiency, human oversight remains essential for strategic interpretation, ethical governance, and maintaining consumer trust. The study concludes that organizations seeking competitive advantage in the digital economy must move beyond simple automation toward AI-augmented strategic frameworks that integrate advanced analytics with human expertise. By combining technological capabilities with ethical governance and organizational learning, firms can create sustainable marketing strategies that respond effectively to evolving consumer expectations in the AI-driven marketplace.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors