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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 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.

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.

This study aims to investigate the role of aesthetically appealing product design in the context of product failure, focusing on the concept of “beauty premium” and “beauty penalty”. This study seeks to determine whether beauty premium, which emerges prior to product failure, can serve as a protective mechanism for firms facing adverse events and, if not, to elucidate the conditions under which beauty premium transitions into beauty penalty.

The research uses a multi-method approach, combining experiments with empirical analysis using data from the US automobile market, specifically focusing on product recalls. The experiments are designed to establish internal validity, whereas empirical data offers insights into real-world contexts. This study considers the role of emotional and cognitive responses of consumers to visual design and explores the influence of gravity on product failures.

The findings reveal that beauty premium persists after product failure when consumers emotionally respond to visual design. However, beauty premium turns into beauty penalty (i.e. more visually attractive designs exacerbate the decline in purchase intention after product failure) when a product failure is severe and when consumers evaluate visual design cognitively.

This research extends the theoretical understanding of the interplay between visual design, consumers’ cognitive and emotional responses and product failure. It introduces beauty premium and beauty penalty, shedding light on the effects of aesthetics in both pre- and postproduct failure conditions.

This research offers practical guidance for brand managers and product designers. It is essential to maintain functional expectations and aesthetics to secure brand loyalty, especially for consumers who evaluate products cognitively in product failure cases.

This research offers unique contributions. It uncovers the protective role of visual aesthetics in mitigating the loss of consumer purchase intention and sales postproduct failure. It challenges the conventional belief that aesthetically appealing design always yields positive outcomes, introducing the possibility of beauty premium transitioning into beauty penalty. It sheds light on the dynamic relationship between product design, product failure and consumers’ emotional and cognitive responses, providing valuable insights for brand managers, product designers and marketing practitioners in an ever-evolving consumer landscape.