Big Data Analytics For Improved Accuracy Efficiency And Decision Making In Digital Marketing
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Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing
The availability of big data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue, and profitability especially in digital marketing. Data plays a huge role in understanding valuable insights about target demographics and customer preferences. From every interaction with technology, regardless of whether it is active or passive, we are creating new data that can describe us. If analyzed correctly, these data points can explain a lot about our behavior, personalities, and life events. Companies can leverage these insights for product improvements, business strategy, and marketing campaigns to cater to the target customers. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing aids understanding of big data in terms of digital marketing for meaningful analysis of information that can improve marketing efforts and strategies using the latest digital techniques. The chapters cover a wide array of essential marketing topics and techniques, including search engine marketing, consumer behavior, social media marketing, online advertising, and how they interact with big data. This book is essential for professionals and researchers working in the field of analytics, data, and digital marketing, along with marketers, advertisers, brand managers, social media specialists, managers, sales professionals, practitioners, researchers, academicians, and students looking for the latest information on how big data is being used in digital marketing strategies.
Sustainable Investments in Green Finance
Never has the world been so heavily marked by the escalating challenges of climate change, dwindling natural resources, and escalating environmental crises. Finding viable solutions has become increasingly imperative as a result of these challenges. The daunting issue at hand is the dire need for financing sustainable initiatives that can effectively combat these global challenges. This financial gap often poses a significant roadblock, hindering progress towards a more environmentally conscious and sustainable future. Sustainable Investments in Green Finance serves as a resource to examine this issue and provide solutions. This meticulously researched book offers real-world case studies that not only highlight the gravity of these challenges but also present tangible solutions. Readers will explore a myriad of sustainable finance mechanisms, collaborative stakeholder efforts, and visionary project objectives that have successfully navigated these obstacles. The book also delves into topics such as the renewable energy revolution, sustainable infrastructure transformations, green bonds, climate finance, nature-based solutions, circular economy innovations, and socially responsible investing.
Algorithmic Approaches to Financial Technology: Forecasting, Trading, and Optimization
Today, algorithms steer and inform more than 75% of modern trades. These mathematical constructs play an intricate role in automating processes, predicting market trends, optimizing portfolios, and fortifying decision-making in the financial domain. In an era where algorithms underpin the very foundation of financial services, it is imperative to hold a deep understanding of the intricate web of computational finance. Algorithmic Approaches to Financial Technology: Forecasting, Trading, and Optimization takes a comprehensive approach, spotlighting the fusion of artificial intelligence(AI) and algorithms in financial operations. The chapters explore the expansive landscape of algorithmic applications, from scrutinizing market trends to managing risks. The emphasis extends to AI-driven personnel selection, implementing trusted financial services, crafting recommendation systems for financial platforms, and critical fraud detection. This book serves as a vital resource for researchers, students, and practitioners. Its core strength lies in discussing AI-based algorithms as a catalyst for evolving market trends. It provides algorithmic solutions for stock markets, portfolio optimization, and robust financial fraud detection mechanisms.