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This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selecta

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Neha Zaidi

Mohit Maurya

Simon Grima

Pallavi Tyagi

Building AI Driven Marketing Capabilities

Understand Customer Needs and Deliver Value Through AI

Chapter 1: Case Study: Using Social Media for improving Customer Engagement.- Chapter 2: Applying AI for Product Life Cycle Management.- Chapter 3: Empowering Customer Experience with AI Tools and Technologies.- Chapter 4: Interactive Fashion Textiles: Marketing New Technologies to Target Tech-Savvy Millennials.- Chapter 5: AI-Based Decisive Model for Customer Segmentation in Fashion Industry.- Chapter 6: Analyzing customer satisfaction of hotel booking applications: A Sentimental Analysis approach.- Chapter 7: Internet Trends and Customer Sentiment Analysis on Different Online Platforms.- Chapter 8: Role of Artificial Intelligence for Value Chain Creation in Healthcare Marketing.- Chapter 9: Potential Roles for Cyber-Ethical Awareness, Artificial Intelligence and Chatbot Technologies Among Students..- Chapter 10: Content Generated by Netflix: Scoping Review and Analysis.- Chapter 11: Unveiling AI's Ethical Impact in Marketing Through Social Media's Darker Influences.- Chapter 12: Strategic Insights through Customer Value Modelling: Unveiling the Key Drivers of Customer Success.- Chapter 13: Exploration of Artificial Intelligence (AI) in Banking Sector: A Bibliometric Analysis.- Chapter 14: Developing a Marketing Strategy While Maintaining Focus on Customer Value.- Chapter 15: Boom of Artificial Intelligence in food industry.- Chapter 16: Unlocking Emotional Intelligence with AI Marketing: Connecting Brands to Hearts.&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;&lt;/p&gt;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Building and Deploying Ethical AI-Powered Solutions

Practical Generative AI: From Concept to Deployment

Building a Program for Strategy and Oversight

Practical AI Governance

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Medical and Technical Analysis and Signal Processing Perspectives

Exploring Infant Cry

Combining Human Thinking and Creativity with Technology to Inspire Audiences

Authentic Marketing

Enhancing Reflective Teaching in Higher Education

Redefining Teaching and Learning

GenAI in Higher Education

Smart Charging Infrastructures

Dieses Übungsbuch konzentriert sich auf die Interpretation und Anwendung anspruchsvoller Analysemethoden, die in der Marketing- und Managementforschung häufig eingesetzt werden. Mit der wachsenden Bedeutung von datenbasierten Entscheidungen in der Praxis wird die Fähigkeit, Analysen mittels quantitativer Methoden kompetent durchzuführen und zu interpretieren, immer wichtiger. Durch die Kombination von theoretischem Wissen und praktischen Übungen mit Lösungen werden Studierende optimal auf ihre zukünftigen beruflichen Herausforderungen vorbereitet. Zielgruppe sind insbesondere Studierende, die bereits Grundkenntnisse in der uni- und bivariaten deskriptiven Statistik erworben haben. Zudem wird ein Verständnis der inferenzstatistischen Verfahren vorausgesetzt, insbesondere der statistischen Prüfung von Hypothesen.
 
 Die folgenden multivariaten Methoden werden behandelt:
 <ul><li>Analyse von fehlenden Werten und Ausreißern zur Bewertung der Datenqualität und Möglichkeiten der Datenaufbereitungen und -transformationen,</li><li>Scoringverfahren zur Bewertung verschiedener Alternativen mittels mehrdimensionaler Zielkriterien,</li><li>Varianzanalysen zum Vergleich der Mittelwerte mehrerer Gruppen, die durch kategoriale Variablen definiert werden,</li><li>Regressionsanalysen zur Erkennung von Zusammenhängen zwischen metrischen Merkmalen,</li><li>Prognoseverfahren zur Vorhersage zukünftiger Werte auf Basis von Beobachtungen aus der Vergangenheit,</li><li>Clusteranalysen zur Strukturierung von Objekten bzw. Subjekten durch Bildung möglichst homogener Gruppen, die gegenüber den anderen Gruppen möglichst heterogen sind, und</li><li>Entscheidungsbaumanalysen zur Klassifizierung von Objekten bzw. Subjekten zu vorgegebenen Gruppen auf Basis bekannter Eigenschaften.</li></ul>
 Die Autoren
 
 Prof. Dr. Stefan Tuschl
 
 ist Professor für Quantitative Methoden am Department Wirtschaft an der Hochschule für Angewandte Wissenschaften (HAW) in Hamburg. Er ist außerdem Mit-Begründer und Mit-Gesellschafter der kom,ma Forschungs- und Beratungsgesellschaft, eines auf Kommunikations-, Werbe- und Markenforschung sowie individuelle Modellierungen spezialisierten Instituts.
 Prof. Dr. Elke Hörnstein
 war Professorin für Quantitative Methoden am Department Wirtschaft an der Hochschule für Angewandte Wissenschaften (HAW) in Hamburg. Zuvor arbeitete sie am Institut für Verkehrs- und Tourismusforschung, Heilbronn.

Dieses Übungsbuch konzentriert sich auf die Interpretation und Anwendung anspruchsvoller Analysemethoden, die in der Marketing- und Managementforschung häufig eingesetzt werden. Mit der wachsenden Bedeutung von datenbasierten Entscheidungen in der Praxis wird die Fähigkeit, Analysen mittels quantitativer Methoden kompetent durchzuführen und zu interpretieren, immer wichtiger. Durch die Kombination von theoretischem Wissen und praktischen Übungen mit Lösungen werden Studierende optimal auf ihre zukünftigen beruflichen Herausforderungen vorbereitet. Zielgruppe sind insbesondere Studierende, die bereits Grundkenntnisse in der uni- und bivariaten deskriptiven Statistik erworben haben. Zudem wird ein Verständnis der inferenzstatistischen Verfahren vorausgesetzt, insbesondere der statistischen Prüfung von Hypothesen. 
Die folgenden multivariaten Methoden werden behandelt:<ul><li>Analyse von fehlenden Werten und Ausreißern zur Bewertung der Datenqualität und Möglichkeiten der Datenaufbereitungen und -transformationen,</li><li>Scoringverfahren zur Bewertung verschiedener Alternativen mittels mehrdimensionaler Zielkriterien,</li><li>Varianzanalysen zum Vergleich der Mittelwerte mehrerer Gruppen, die durch kategoriale Variablen definiert werden,</li><li>Regressionsanalysen zur Erkennung von Zusammenhängen zwischen metrischen Merkmalen,</li><li>Prognoseverfahren zur Vorhersage zukünftiger Werte auf Basis von Beobachtungen aus der Vergangenheit,</li><li>Clusteranalysen zur Strukturierung von Objekten bzw. Subjekten durch Bildung möglichst homogener Gruppen, die gegenüber den anderen Gruppen möglichst heterogen sind, und</li><li>Entscheidungsbaumanalysen zur Klassifizierung von Objekten bzw. Subjekten zu vorgegebenen Gruppen auf Basis bekannter Eigenschaften.</li></ul>&nbsp;

Grundlagen, Übungen und Lösungen

Quantitative Datenanalysen in Marketing und Management mit SPSS

Corporate storytelling for branding is an important tool for improving communication and developing a professional culture. This edited volume provides a comprehensive treatment of the nature of corporate storytelling for branding, with a particular focus on Africa and the opportunities in that region to use storytelling to build a brand. Adopting a multi-stakeholder approach, the book covers the theories related to corporate storytelling for branding, how to evaluate the approaches and envisioning the future.
&nbsp;
This book also addresses the challenges faced by African economies in achieving sustainable development goals and how corporate storytelling can help overcome these challenges. By adopting a multi-stakeholder perspective encompassing the role of national governments, companies and consumers, this work provides an integrated perspective that will be valuable to the understanding of this important topic.
&nbsp;
Brighton Nyagadza is a Lecturer in Digital Marketing and Deputy Unit of Assessment Lead (Business &amp; Management Studies) at York St. John University, London, UK. He is a CABS (UK) Certified Business and Management Educator (CMBE) who holds a Ph.D in Marketing. In addition, Brighton is a Fellow of the Academy of Marketing Science, USA.

The New Art and Science of Compelling Corporate Communication

Corporate Storytelling for Branding

The funniest serial killer thriller full of twists and turns you'll read all year

I Think We Should Kill Other People

Depending on who you ask, artificial intelligence is our salvation or our doom; an overhyped, bigoted artist or the ticket to immortality and utopian abundance; just another tech fad or the last invention we need ever make. The AI discourse is confusing, messy, and frustrating. But the technology — and our response to it — will shape the future, whether or not we’re part of the conversation.
Obsolete is&nbsp;for those who are interested in learning more about AI, but unsure of where to start and who to believe. They may feel intimidated by the technical jargon, put off by dry and abstract prose, or skeptical of the loudest voices on the issue (like Elon Musk and Sam Altman). Obsolete will introduce readers to the basics of AI, the idea that it could lead to human extinction, the roiling three-sided debate surrounding extinction fears, and the people and companies trying to build artificial general intelligence (AGI) — that which can outwit humans across the board. The book will grapple with the core arguments animating the AI debates, which are, by turns, uncritically parroted and unduly dismissed. It will also cut through industry hype while seriously entertaining the implications of AGI.
The risk that AGI could result in our extinction is being recognized by a significant and growing number of leading AI researchers, industrialists, and policymakers, along with the wider public. Existential risk from AI has been explored in other books, but Obsolete will be the first to center its analysis on how both capitalist and great power competition make AI more dangerous.
Obsolete will also tackle questions like: Can machines actually outsmart humanity? If so, when could that happen? If AGI is possible, is it inevitable? Why are people trying to build a technology they claim could end the world? Is the idea of AI-driven extinction the product of a big tech conspiracy aiming to hype the technology and control its regulation? Why has the left mostly ignored or dismissed existential risk from AI? Why do some powerful techies welcome human extinction? How could AI enable stable authoritarian regimes? How could killer robots reshape war and the balance of power? What do China and the US want from AI? And why has it become the front line of their brewing Cold War? And finally: What can and should the left do about it?

Power, Profit, And The Race For Machine Superintelligence

Obsolete

Understand How to Use Artificial Intelligence in Your HR Role

AI for People Professionals

Building AI Driven Marketing Capabilities

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