Advances in Digital Marketing in the Era of Artificial Intelligence

Cover
Half Title
Title Page
Copyright Page
Contents
Contributors
Preface
Editor
1. AI and Advertising: Unraveling the Dynamics of Consumer Behavior and Brand Engagement
	1.1 Introduction
	1.2 Significance of AI in Advertising
	1.3 Role of AI in Consumer Behavior Analysis
	1.4 Mapping the Customer Journey and Touch Points
	1.5 Ethical Implications and Challenges
		1.5.1 Transparency, Accountability, and Bias
		1.5.2 Impact on Vulnerable Consumers
		1.5.3 Sensitivity to Cultural and Contextual Differences
		1.5.4 Data Privacy and Security
	1.6 Conclusion
	1.7 Recommendations
		1.7.1 Enhancing Transparency and Accountability
		1.7.2 Ethics Education
		1.7.3 Regular Auditing and Evaluation
	References
2. Enhancing Customer Experience through AI-Enabled Content Personalization in E-Commerce Marketing
	2.1 Introduction
		2.1.1 The Essence of Personalization
			2.1.1.1 The Convergence of AI and Content Personalization
		2.1.2 The Promise and Uncharted Territories
	2.2 Enhanced Customer Satisfaction
		2.2.1 Understanding Customer Frustration and the Paradox of Choice
			2.2.1.1 AI-Enabled Personalization: A Solution to Frustration
			2.2.1.2 The Impact on Customer Satisfaction
		2.2.2 Personalization as a Solution to Guide Customers to Relevant Products
		2.2.3 Customer Testimonials and Examples of Satisfaction-Driven Conversions
	2.3 Literature Review
	2.4 Increased Conversion Rates
		2.4.1 Conversion as the Ultimate Goal of E-commerce Marketing
		2.4.2 How AI-Driven Recommendations Directly Impact Conversion Rates
		2.4.3 Statistical Evidence of Conversion Rate Improvements with Personalization
	2.5 Building Customer Loyalty
		2.5.1 Nurturing Emotional Connections between Customers and Brands
		2.5.2 The Role of Personalized Experiences in Cultivating Brand Advocates
		2.5.3 Real-World Instances of Brand Loyalty Driven by Content Personalization
	2.6 Optimizing Marketing Strategies
		2.6.1 Utilizing AI Insights for Refining Marketing Tactics
		2.6.2 Targeting Specific Customer Segments Effectively through Personalization
		2.6.3 Maximizing Resource Allocation Based on Data-Driven Strategies
	2.7 Challenges and Ethical Considerations
		2.7.1 Data Privacy and Ethics
			2.7.1.1 The Importance of Responsible Data Collection and Usage
			2.7.1.2 Adhering to Privacy Regulations and Maintaining Transparency
			2.7.1.3 Ensuring Ethical Handling of Customer Data in Personalization Efforts
		2.7.2 Algorithmic Bias
			2.7.2.1 Understanding the Potential for Bias in AI Models
			2.7.2.2 Monitoring and Addressing Algorithmic Bias in Content Recommendations
			2.7.2.3 Strategies to Mitigate Bias and Ensure Fairness in Personalization
		2.7.3 Striking the Balance
			2.7.3.1 Balancing Automation with Maintaining a Human Touch
			2.7.3.2 The Role of AI in Streamlining Processes While Preserving Empathy
			2.7.3.3 Examples of Successful Human-AI Collaboration in Personalization
		2.7.4 Adapting to Changing Preferences
			2.7.4.1 Acknowledging the Dynamic Nature of Consumer Preferences
			2.7.4.2 How AI Models Can Continuously Learn and Adapt to Evolving Tastes
			2.7.4.3 Ensuring Accuracy and Relevance of Recommendations over Time
	2.8 Implementing AI-Enabled Content Personalization
		2.8.1 Holistic Data Collection
			2.8.1.1 Identifying and Collecting Relevant Customer Data Sources
				2.8.1.1.1 The Significance of Comprehensive Data for Accurate Personalization
			2.8.1.2 Techniques for Data Aggregation and Organization
		2.8.2 Algorithm Selection and Training
			2.8.2.1 Choosing Appropriate AI Algorithms and Machine Learning Models
			2.8.2.2 Importance of Tailored Algorithms for Specific E-commerce Objectives
			2.8.2.3 Training Models Using Historical Data to Predict Customer Preferences
		2.8.3 Real-Time Personalization
			2.8.3.1 Implementing Systems for Delivering Real-Time Recommendations
			2.8.3.2 How Real-Time Personalization Enhances User Experience
			2.8.3.3 Adapting Recommendations On-the-Fly Based on Customer Behavior
		2.8.4 Continuous Testing and Enhancement
			2.8.4.1 The Iterative Process of Refining Personalization Algorithms
			2.8.4.2 A/B Testing and Its Role in Optimizing Personalization Strategies
			2.8.4.3 Strategies for Analyzing Results and Making Data-Driven Improvements
				2.8.4.3.1 Incorporating Customer Feedback
			2.8.4.4 Leveraging Customer Feedback to Enhance Personalization
			2.8.4.5 Integrating Customer Suggestions into the Recommendation Process
			2.8.4.6 Using Feedback to Align Recommendations with Customer Expectations
	2.9 Future Aspects in AI-Enabled Content Personalization in E-commerce Marketing
	2.10 Conclusion
	References
3. Redefining Marketing Strategies for Higher Education through AI Applications: A Critical Review
	3.1 Introduction
	3.2 Landscape of Higher Education in India
	3.3 Finding Solution through the Marketing Approach for HE Ecosystem
	3.4 Leveraging AI for student acquisition
	3.5 Integrating AI for Improving Service and Satisfaction in Education Industry
	3.6 Conclusion
	References
4. Enhancing Customer Targeting in E-Commerce and Digital Marketing through AI-Driven Personalization Strategies
	4.1 Introduction
		4.1.1 Evolution of Digital Marketing
			4.1.1.1 Objectives
	4.2 Customer Targeting in E-Commerce and Digital Marketing
		4.2.1 The Importance of Customer Targeting
		4.2.2 Challenges in Customer Targeting
	4.3 AI-Driven Personalization Techniques
		4.3.1 Recommender Systems
		4.3.2 Natural Language Processing (NLP)
		4.3.3 Machine Learning and Predictive Analytics
		4.3.4 Chatbots and Virtual Assistants
		4.3.5 Opportunities and Challenges of AI-Driven Personalization in Digital Marketing and E-Commerce
			4.3.5.1 Opportunities
			4.3.5.2 Challenges
	4.4 Impact on Product Sales
		4.4.1 Increased Conversion Rates
		4.4.2 Reduced Cart Abandonment
		4.4.3 Enhanced Customer Loyalty
	4.5 Literature Review
		4.5.1 Personalized Recommendations
		4.5.2 Predictive Analytics for Customer Segmentation
		4.5.3 Natural Language Processing (NLP) for Content Personalization
		4.5.4 Testing and Optimization
		4.5.5 Customer Churn Prediction and Retention Strategies
	4.6 Ethical Considerations
		4.6.1 Transparency and Fairness
		4.6.2 Data Privacy and Security
		4.6.3 Informed Consent
		4.6.4 Bias Mitigation
		4.6.5 User Control
		4.6.6 Algorithmic Accountability
		4.6.7 Long-Term Customer Value
	4.7 Conclusion
	References
5. Current Advances on the Application of Blockchain Solutions in Digital Marketing: Opportunities and Threats
	5.1 Marketing Applications of Blockchain
		5.1.1 Branding and Blockchain
		5.1.2 Online Advertising and Blockchain
		5.1.3 Pricing and Blockchain
		5.1.4 Privacy and Blockchain
	5.2 Possible Implications of Blockchain in Marketing
	5.3 Practical Uses of Blockchain in Marketing
	5.4 Market-based Assets
		5.4.1 Brand Equity
		5.4.2 Customer Equity
	5.5 Opportunities of Use of Blockchain in Marketing
		5.5.1 Market Research
		5.5.2 Supply Chains
		5.5.3 Digital Platforms
	5.6 Threats of Employing Blockchain for Marketing
		5.6.1 Ownership Verification
		5.6.2 Digital Assets
		5.6.3 Organizational Restrictions
		5.6.4 Digital Rights and Identity
		5.6.5 Customer Trust and Privacy
	5.7 Case Studies
		5.7.1 Case Study of Branding Transparency Using Blockchain
		5.7.2 Case Study of Customer Loyalty Using Blockchain
	5.8 Conclusion and Discussions
	References
6. Analysis and Result Prediction for Indian Premier League Using Machine Learning Algorithms
	6.1 Introduction
	6.2 An Overview on Existing Systems
	6.3 Architecture of the Proposed Model
		6.3.1 Data Pre-Processing
		6.3.2 Feature Extraction
		6.3.3 Partitioning the Dataset
		6.3.4 Algorithm Selection
		6.3.5 Algorithms Used for Performance Evaluation
		6.3.6 Support Vector Machine
		6.3.7 Decision Tree
		6.3.8 Random Forest
		6.3.9 Logistic Regression
		6.3.10 Model Training
		6.3.11 Machine Learning Model Evaluation
		6.3.12 Prediction and Updating
	6.4 Results and Discussion
		6.4.1 Datasets
		6.4.2 Evaluation Matrics
		6.4.3 Performance Analysis of the Classifiers
		6.4.4 Prediction
	6.5 Conclusion
	References
7. Study of Factors Affecting Intention to Use Artificial Intelligence by Marketers: A ChatGPT Case
	7.1 Introduction
	7.2 Literature Review
		7.2.1 Artificial Intelligence and Marketing
			7.2.1.1 What Is ChatGPT?
		7.2.2 Application of AI in Marketing
			7.2.2.1 Challenges of Integrating ChatGPT into Marketing Strategies
		7.2.3 Toward a Conceptualization of the Intention to Use ChatGPT
		7.2.4 Toward a Better Understanding of Behavioral Intention in the Context of ChatGPT
			7.2.4.1 Conceptual Framework and Research Hypothesis
				7.2.4.1.1 Impact of Perceived Usefulness and Perceived Ease of Use on Attitude and Behavioral Intention
					7.2.4.1.1.1 Perceived Usefulness
					7.2.4.1.1.2 Perceived Ease of Use
					7.2.4.1.1.3 Attitude toward Using ChatGPT
				7.2.4.1.2 Trust and Perceived Risk and Intention to Use ChatGPT
	7.3 Methodology of the Quantitative Study
		7.3.1 Research Methodology
			7.3.1.1 Data Collection Method
			7.3.1.2 Measurement Scales
			7.3.1.3 Survey Pre-Test
		7.3.2 Model Validation, Study Results, and Discussion
			7.3.2.1 Model Validation and Results
				7.3.2.1.1 Pilot Study
					7.3.2.1.1.1 Sample's Characteristics
					7.3.2.1.1.2 Exploratory Factor Analysis
					7.3.2.1.1.3 Factor Analysis for Perceived Usefulness (PU)
					7.3.2.1.1.4 Factor Analysis for Attitude (Att)
					7.3.2.1.1.5 Factor Analysis for Intention to Use (Intuse)
	7.4 Factor Analysis for Perceived Risk (PR)
	7.5 Factor Analysis for Trust (Trust)
		7.5.1 Main Survey
			7.5.1.1 Structural Model Evaluation
			7.5.1.2 Hypotheses Testing
	7.6 Discussion
	7.7 Conclusion
	References
8. The Effectiveness of Using Artificial Intelligence Techniques in Advertising on Social Media
	8.1 Introduction
	8.2 Objectives of the Study
	8.3 Literature Review
		8.3.1 Artificial Intelligence
		8.3.2 Applications of Artificial Intelligence Technology in Marketing
		8.3.3 The Effect of Artificial Intelligence on Online Advertising
		8.3.4 The Attitude toward the Poster Advertising
		8.3.5 Conceptual Model and Research Hypothesis
	8.4 Research Methodology
		8.4.1 Choice of Advertising Poster
		8.4.2 Sampling and Data Collection
		8.4.3 Operationalization of Psychometric Concepts and Properties of Measurement Scales
			8.4.3.1 Reliability and Validity Test
	8.5 Testing Hypotheses and Discussing Results
		8.5.1 Effect of Attention on Memorization
		8.5.2 Effect of Memorization on Attitude toward Advertising
		8.5.3 Effect of Attitude toward Advertising on Intention Behavior
	8.6 Analysis of Averages
		8.6.1 Analysis of Attention Averages
		8.6.2 Analysis of Memorization Averages
		8.6.3 Analysis of Averages of Attitude toward Advertising
		8.6.4 Analysis of Advertising Engagement Averages
		8.6.5 Analysis of Averages of Behavior Change Intention
	8.7 Managerial and Marketing Implications
	8.8 Limitations and Future Avenues of Research
	8.9 Conclusion
	References
9. The Impact of Blockchain on Digital Commerce
	9.1 Introduction
		9.1.1 Hypotheses of the Study
		9.1.2 The Study's Significance
		9.1.3 Study Objectives
	9.2 Definition of Blockchain
	9.3 Blockchain in Marketing
	9.4 Blockchain Applications in Global Trade
		9.4.1 Effects of Using Blockchain
	9.5 Study Design
	9.6 Study Methodology
	9.7 Data Collection
	9.8 Results
	9.9 Conclusion
	References
10. Chatbots: A Computerized Communication Specialized Device
	10.1 Introduction
		10.1.1 Background of the Study
		10.1.2 Chapter Objectives, Investigation Questions, and Limitations
		10.1.3 Theoretical Framework
		10.1.4 Research Strategy and Data Collection
		10.1.5 Chapter Structure
	10.2 Communiqué Networks
		10.2.1 Types of Communication Channel
		10.2.2 Establishing a Profitable Promotional Specialized Device
		10.2.3 The Most Recent Trends in the Technique the Brand Connects with Clienteles
	10.3 Chatbots
		10.3.1 Significance
		10.3.2 The Use of Chatbots in Business
		10.3.3 Consumer Requirements
		10.3.4 Customer Awareness
	10.4 Research Questions and Exploration Directions
	10.5 Methodology
		10.5.1 Recruitment of Participants
		10.5.2 Analysis and Guide for the Interview
	10.6 Results
		10.6.1 Participant Experiences and Direct User Experience
			10.6.1.1 The Inquiries from Participants
			10.6.1.2 Successful Query Responses?
			10.6.1.3 Attitudes toward the Chatbot, Both Favorable and Unfavorable
			10.6.1.4 User Perceptions of the Chatbot's Capabilities
			10.6.1.5 User Opinions on the Chatbot's Presentation and Appearance
		10.6.2 User Motivations for Chatbot
		10.6.3 User Recommendations for Future Improvements
	10.7 Findings
		10.7.1 The Customer Service Chatbot User Experience
		10.7.2 Client Inspirations for Involving Chatbots for Client Support
	10.8 Implications of Findings
		10.8.1 Applicability to Theory
		10.8.2 Practical Implications
	10.9 Study Limitations and Future Research
		10.9.1 Reactions to the Exploration Questions
		10.9.2 Consistent Quality and Authenticity
		10.9.3 Proposal for Additional Research
	References
		Electronic References
	Appendix 1: Interview
11. Artificial Intelligence and More Effective Advertising: Unlocking the Power of Data and Automation
	11.1 Introduction: The Transformative Impact of Artificial Intelligence on Advertising
		11.1.1 The AI Revolution in Advertising
			11.1.1.1 Understanding AI in Advertising
				11.1.1.1.1 Machine Learning Algorithms
				11.1.1.1.2 Natural Language Processing (NLP)
				11.1.1.1.3 Predictive Analytics
			11.1.1.2 The Relevance of AI in Advertising
				11.1.1.2.1 Data Abundance
				11.1.1.2.2 Personalization Imperative
				11.1.1.2.3 Real-Time Decision-Making
				11.1.1.2.4 Precision Targeting
			11.1.1.3 Leveraging AI for Consumer Insights
				11.1.1.3.1 Data as the Foundation
				11.1.1.3.2 Data Collection and Aggregation
				11.1.1.3.3 Data Processing at Scale
			11.1.1.4 Audience Segmentation and Targeting
				11.1.1.4.1 Behavioral Insights
				11.1.1.4.2 Predictive Segmentation
				11.1.1.4.3 Hyper-Personalization
			11.1.1.5 Predictive Analytics
				11.1.1.5.1 Anticipating Trends
				11.1.1.5.2 Meeting Future Needs
				11.1.1.5.3 Real-Time Optimization
	11.2 Applications of AI in Advertising
		11.2.1 Audience Targeting and Segmentation
			11.2.1.1 Behavioral Analysis
			11.2.1.2 Predictive Segmentation
			11.2.1.3 Hyper-Personalization
		11.2.2 Creative Optimization
			11.2.2.1 Content Generation
			11.2.2.2 Image Recognition
			11.2.2.3 Sentiment Analysis
		11.2.3 Media Buying and Optimization
			11.2.3.1 Programmatic Advertising
			11.2.3.2 Real-Time Bidding (RTB)
			11.2.3.3 Campaign Performance Enhancement
	11.3 Ethical Considerations and Challenges
		11.3.1 Privacy Concerns
			11.3.1.1 Data Collection and Privacy
			11.3.1.2 Transparency and Consent
		11.3.2 Algorithmic Biases
			11.3.2.1 Implicit Biases in AI Algorithms
			11.3.2.2 Fair and Ethical Representation
		11.3.3 Accountability and Transparency
			11.3.3.1 Algorithm Accountability
			11.3.3.2 Algorithm Transparency
		11.3.4 Consumer Trust and Consent
			11.3.4.1 Trust in AI-Enhanced Advertising
			11.3.4.2 Obtaining Informed Consent
		11.3.5 Adherence to Regulations
	11.4 Case Studies and Success Stories
		11.4.1 Case Study: Netflix - Personalization Redefined
			11.4.1.1 Background
			11.4.1.2 AI in Action
			11.4.1.3 Results
		11.4.2 Success Story: Amazon - The Power of Predictive Analytics
			11.4.2.1 Background
			11.4.2.2 AI in Action
			11.4.2.3 Results
		11.4.3 Case Study: Coca-Cola - Content Creation Reinvented
			11.4.3.1 Background
			11.4.3.2 AI in Action
			11.4.3.3 Results
		11.4.4 Success Story: Spotify - Dynamic Audio Ads
			11.4.4.1 Background
			11.4.4.2 AI in Action
			11.4.4.3 Results
	11.5 Conclusion
	References
12. Chatbot and Digital Communication
	12.1 Introduction
		12.1.1 Understanding Potential Opportunities and Barriers Related to the Integration of ChatGPT in Higher Education and Investigating the Readiness of Tunisian Students to Use ChatGPT as a Learning Tool
		12.1.2 How Social Institutions Impact Behavior and Learning Process?
		12.1.3 The Impact of Social Institutions on Behavior and Learning Process Considering the Mediating Role of Students' MIs
		12.1.4 The Impact of Social Institutions on Behavior and the Learning Process Considering the Mediating Role of Students' MIs and Capabilities
	12.2 Theoretical Concepts
		12.2.1 The Capability Approach
		12.2.2 Conversion Factors: Social Intuitions and Multiples Intelligences
			12.2.2.1 Social Institutions: Family, Peers, and School
			12.2.2.2 Multiple Intelligences (MIs)
		12.2.3 Artificial Intelligence (AI) and ChatGPT
		12.2.4 Cheating Behavior: Bandura's Social Cognitive Learning Theory (SCLT)
		12.2.5 Creative Behavior: Piaget's Theory
	12.3 Methodology and Method
		12.3.1 Qualitative Research
		12.3.2 In-depth Interview
		12.3.3 Guide Interview
		12.3.4 Thematic Analysis
	12.4 Data Analysis
		12.4.1 Participants Characteristics
		12.4.2 Emerged Categories and Frequencies
			12.4.2.1 Perceived Social Institutions' Influence
			12.4.2.2 Perceived MIs
			12.4.2.3 Perceived Capabilities
			12.4.2.4 Perceived ChatGPT Using Purpose
		12.4.3 Matrices: Crossing Conceptual Variables
		12.4.4 Inference
		12.4.5 Results and Discussion
	12.5 Conclusion
		12.5.1 Practical Recommendations
		12.5.2 Future Research
		12.5.3 Limitations
	References
13. Are You Real?: When Anthropomorphism Misleads the Consumer
	13.1 Introduction
	13.2 Background
		13.2.1 Virtual Influencer Marketing
		13.2.2 Profile of Virtual Influencers
		13.2.3 Gender of Virtual Influencers
		13.2.4 Parasocial Interaction
	13.3 Methodology
	13.4 Results and Discussion
		13.4.1 Word Frequency Analysis
		13.4.2 Thematic Analysis
			13.4.2.1 Positive Evaluation
				13.4.2.1.1 Admiration
				13.4.2.1.2 Curiosity
				13.4.2.1.3 Desire of Identification
			13.4.2.2 Negative Evaluation
				13.4.2.2.1 Uncanny and Rejection
				13.4.2.2.2 Disappointment
				13.4.2.2.3 Sarcasm and Irony
	13.5 Practical Implications
	13.6 Limitations and Perspectives
	13.7 Conclusion
	References
14. Natural Language Processing to Track Cognitive, Emotional and Social Change on Reddit Mental Health and Non-Mental Health Groups during Covid-19
	14.1 Introduction
	14.2 Literature review
		14.2.1 AI and Marketing
		14.2.2 Marketing and Mental Health
		14.2.3 Mental Health and Social Media
	14.3 Methodology
	14.4 Analysis and Interpretation of Results
		14.4.1 Exploratory Data Analysis
		14.4.2 Dataset Shifts
		14.4.3 Covid-19 Related Themes Change for Different Groups
		14.4.4 Negative Sentiment Change for Different Groups
		14.4.5 Discussed Topics Change
		14.4.6 Discussion of Results
	14.5 Conclusion
	References
15. Do Consumers Favor a Social Presence When Using Voice Chatbots?
	15.1 Introduction
	15.2 Review of the Literature and Development of Hypotheses
		15.2.1 Theoretical Framework
		15.2.2 Chatbot Fundamentals
		15.2.3 Relationship between Variables and Research Hypotheses
	15.3 Design Methodology
		15.3.1 Data Collection Method and Sample
		15.3.2 Measurement Instruments
	15.4 Results
		15.4.1 Reliability and Validity of Constructs
	15.5 Additional Tests: Mediating Effects
	15.6 Robustness Test
	15.7 Discussion and Conclusion
	15.8 Theoretical Implications
	15.9 Managerial Implications
	15.10 Limitations and Future Research Directions
	References
16. The Moderating Role of Perceived Interaction in a Chatbot's Impact on Conversational Commerce: From Intention to Usage to Actual Usage
	16.1 Introduction
	16.2 Theoretical Background
		16.2.1 Artificial Intelligence in Online Marketing
			16.2.1.1 Definition of Artificial Intelligence
			16.2.1.2 Artificial Intelligence in E-commerce
				16.2.1.2.1 Conversational Commerce
				16.2.1.2.2 Perceived Interactivity
		16.2.2 The Determinants of Adopting Artificial Intelligence
			16.2.2.1 Hedonic and Utilitarian Motivation
			16.2.2.2 Social Presence of a Chatbot
		16.2.3 Chatbots
			16.2.3.1 Definition of a Chatbot
			16.2.3.2 Intention to Use a Chatbot
			16.2.3.3 Actual Use of Chatbot
		16.2.4 Development of Hypotheses
			16.2.4.1 Impact of Utilitarian and Hedonic Motivations on Usage Intention
			16.2.4.2 Impact of Utilitarian and Hedonic Motivations on Social Presence
			16.2.4.3 Impact of Social Presence on Usage Intention
			16.2.4.4 Impact of the Intention to Use a Chatbot on Current Usage
			16.2.4.5 Moderating Effect of the Perceived Interactivity between the Intention to Use a Chatbot and Actual Use
		16.2.5 Research Methodology and Discussion of Results
			16.2.5.1 Research Methodology
			16.2.5.2 Discussion of Results
	16.3 Conclusion
	References
17. Role of Technology for Pharmaceutical Marketing in the Era of Artificial Intelligence: A Bibliometric Study
	17.1 Introduction
	17.2 Drivers of Online Pharmacy
	17.3 Literature Review
	17.4 Method
	17.5 Results
		17.5.1 Cluster I: Consumer Preferences
		17.5.2 Cluster II: Supply Side Dynamics
	17.6 Conclusion
	References
18. Market Segmentation: Machine Learning in Marketing with Clustering Model
	18.1 Introduction
	18.2 Methodology
	18.3 Results
	18.4 Conclusions
	References
References
	Electronic References
Index