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In today’s competitive era, it has become significant for companies to understand their end consumer and target customers effectively. One of the ways to accomplish this goal is data-driven marketing. The current study seeks to explore the differences between traditional marketing and digital marketing, the pros and cons of data-driven marketing and usage of artificial intelligence (AI) in data-driven marketing. The research objective was met by exploration of published papers in the past 10 years covering the evolution of data-driven marketing, functions of data engineering, application of technology like AI in data-driven marketing and opportunities and challenges. This study is significant as it provides the insight into the relationship between marketing and data engineering and thus helps marketers to frame strategies by leveraging data-driven marketing to improve consumer experience and gain a competitive edge. Moreover, this study is an interdisciplinary study including marketing, engineering and data science. This study focusses on use of innovative methods to improve profitability of business and consumer experience.

This study aims to propose a new connection technique of bypass diode (BD) in photovoltaic (PV) array, which reduces the circulating current (CC) within PV modules as well as conduction loss in partial shaded condition (PSC).

Linearized circuit model of PV panel is proposed for calculating the CC and power loss of novel BD arrangements in PV array. From the analysis the best BD arrangement is applied in series parallel, TCT and honeycomb (HC) PV array structure for simulation and hardware verification. The hardware verification is performed in a 3 × 3 PV array, where individual panel capacity is 200 W.

The proposed BD arrangement reduces the power loss due to CC under PSC by almost 3% compared to conventional BD structure in a PV array.

The proposed BD arrangement is simple and useful in large PV power plants to reduce the CC-based extra power loss under PSC.

This chapter highlights the importance of combining multiple data sources to gain a complete understanding of the marketing environment. Marketing campaigns can be successful only by data-driven decision-making. We need to include multiple data sources so that it may furnish a complete and distinctive view of market dynamics. This chapter begins by probing into the changing landscape of marketing. It reveals that traditional tactics are being replaced by urban, technology-based strategies. It becomes progressively important for organizations to integrate different data sources along with harnessing the power of big data, artificial intelligence (AI), and advanced analytics. It involves the smooth and flawless blending of different datasets to obtain important connections and patterns. Simple data collection and storage will not help. This chapter emphasizes the need for an all-embracing strategy. Since it has a transformative impact on marketing results, it solves the challenges caused by data storage systems. It also provides real-world examples of integrated solutions. It deliberates regulatory issues that must be considered when integrating different data sources. Any marketing data ecosystem needs integration of the internal and external data sources in search of a better understanding of the market. Such can help businesses find new opportunities and insights that, up until then, remain unknown. It may integrate different data types to get the most from marketing strategies in place. If there is integration of data, then decisions can be made with knowledge and purpose.

Techniques for ingesting marketing data encompass systematically organizing, interpreting, and gathering information for practice in marketing analytics. This process starts with collecting data from numerous sources like sales records and customer interactions, followed by transforming and cleaning the data to guarantee accuracy and consistency. This chapter highlights the fundamental part of data ingestion in marketing analytics, underscoring its importance in transforming raw data into some useful insights. We study two types of data ingestion named batch ingestion and real-time ingestion and the practical implementation of ingestion in marketing analytics methods. By careful examination of these approaches, we aim to equip marketers with the tools and knowledge which is necessary for efficient utilization of data and authorize them to generate the decisions and implement that decisions in the data-centric business environment.

Vegan cosmetics are becoming popular among consumers as they are made without animal ingredients. This study aims to analyse the impact of religious beliefs and environmental concerns on consumer–brand relationships using the stimulus-organism-response theory in the vegan cosmetics category.

The primary data has been collected from 281 millennial respondents. The authors used Smart PLS (v.4.0.9.5) to analyse the data and test the proposed hypotheses.

The study findings suggest that Environmental concerns significantly impact attitudes compared to religious beliefs. The mediating role of attitude towards vegan cosmetics between religious beliefs, brand sacredness and mindful consumption was established. It may be because of the inter-variable relationship of religious beliefs-attitude-brand sacredness on the premise of S-O-R.

This revelation contributes significantly to the academic discourse on mindful consumption and holds pragmatic implications for businesses and policymakers aiming to effectively promote environmentally responsible choices among consumers. The findings enrich the past literature on vegan cosmetics, demonstrating that trusting religious belief is a salient determinant of consumers’ attitudes towards vegan cosmetics and mindful consumption. The findings also supported the applicability of stimulus-organism-response (SOR) in the domain of mindful consumption and consumer–brand relationships in the context of emerging markets.

This study proposes to conceptualize and explicate the progress of end-to-end data pipelines to enable seamless data flow, addressing the mounting demand for instantaneous information in the vibrant field of marketing analytics. Ensuring timely access to inclusive analytics, it empowers marketers to make conversant decisions promptly. The study espouses a conceptual approach, starting with the classification and identification of various data sources crucial for thorough marketing analytics. It probes into the complexities of designing adaptable and scalable architectures for extensive data pipelines, ensuring effective data collection, processing, and storage. Emphasizing case analyses of marketing analytics tools, the study delivers practical insights and exhaustive examinations of existing solutions. The findings offer a broad understanding of the key components and strategies necessary for realizing smooth data flow in real-time marketing analytics. This investigation contributes expressively to the field by outlining a robust framework for continuous data flow, identifying the benefits and challenges associated with data pipelines. From a hands-on standpoint, it provides organizations with appreciated insights for building effective end-to-end data pipelines, bestowing strategies for implementing real-time marketing analytics outlines that enhance decision-making processes. The originality and value of this research lie in its universal approach to addressing the challenges of real-time marketing analytics, offering a fresh viewpoint on creating seamless data flow frameworks. The gathered information has the latent to drive innovation in marketing analytics, serving as a crucial resource for companies aiming to gain a competitive edge in today’s data-driven market. This inclusive approach ensures that the study’s contributions are both theoretically significant and practically applicable.

This chapter introduces the specifics and intricacies of data security in marketing applications, more specifically aiming towards an organization’s responsibility and need to protect their own and their customer’s data. From identifying and understanding vulnerabilities to finding effective and robust ways to mitigate them, this chapter analyses all the variables and components when it comes to data security. With concepts such as data integrity, data confidentiality, and data availability, this chapter explains the scope and gist of data security. These concepts are also called the pillars of data security. Additionally, vulnerabilities originating from both, internal and external attacks, are mentioned and discussed in this chapter. Mitigation and prevention strategies are discussed as well in this chapter to give a clear base for making a secure and robust data security system. By using advanced technologies for protecting the data, various methods of data storage, authorization, and authentication, an organization can combat the issue of data security. Furthermore, this chapter discusses the legality and legal areas of digital security in India, the United States, and the European Union, which helps in understanding the regulations and laws all over the world. Additionally, for an organization, methods of gaining their customers’ trust and providing them with a transparent picture of the data security measures are explained along with appropriate examples. Finally, details about maintenance and updates are also given. To conclude, this chapter helps the reader navigate through the murky waters of the complicated world of data security in today’s new and upcoming digital age.

This study intends to unmask the antecedents of metaverse adoption intention (MAI) within the Indian hospitality industry. To achieve this objective, we extended the “unified theory of acceptance and use of technology” (UTAUT) by integrating additional constructs: “perceived cyber risk” (PCR), “trust” (TR), “personal innovativeness in IT” (PIT) and “hedonic motivation” (HM).

A quantitative research methodology was employed, utilizing data gathered through an online survey disseminated via Google Forms to evaluate the constructs of the study. Convenience and snowball sampling were used, and the proposed relationships were analyzed using “partial least squares structural equation modeling” (PLS-SEM).

The study presents a robust model explaining MAI among Indian hospitality consumers, with a high R² of 81%, indicating strong predictive relevance. Core UTAUT constructs (PE, EE, SI and FC) and additional constructs (TR, HM and PIT) positively influence MAI, while PCR negatively impacts it.

This research enhances the existing body of knowledge by providing a deeper insight into metaverse adoption among hospitality consumers in India. The proposed framework provides a more nuanced perspective on metaverse adoption, extending beyond conventional technological factors.

This study is distinctive in its integration of four additional constructs- PCR, TR, PIT and HM- into the original UTAUT model, thereby addressing a gap in the existing literature. Furthermore, it advances the understanding of the metaverse by identifying key antecedents influencing MAI among Indian hospitality consumers.

In the digital marketing era, the strategic importance of data infrastructure has never been more pronounced. This chapter examines 34 case studies across various industries to uncover robust data infrastructures’ pivotal role in enhancing data-driven marketing campaigns. Among these, the selected five studies showcase organizations like Netflix, Amazon, Spotify, Airbnb, and Starbucks, demonstrating innovative uses of technology convergence, including artificial intelligence (AI), big data analytics, and cloud computing, to achieve marketing objectives. Their success stories are a testament to the power of robust data infrastructures, inspiring businesses to leverage their data assets more effectively. Through detailed analysis, this chapter identifies key challenges related to data quality, accessibility, and the integration of disparate technologies within marketing frameworks. It further explores solutions that have led to improved customer personalization, dynamic pricing models, and significant return on investment (ROI) enhancements. These findings provide practical insights that can be directly applied to your business. Additionally, this chapter highlights the emerging focus on sustainable infrastructure practices and the criticality of a unified data approach in optimizing marketing strategies. By synthesizing insights from leading market players, this study helps a comprehensive understanding of how modern data infrastructure underpins the success of data-driven marketing, offering valuable lessons for businesses aiming to leverage their data assets more effectively.