AI Marketing

Chapter 1. The Role of Machine Learning in Targeted Advertising

Mitra Madanchian, Hamed Taherdoost

Abstract
1.1. Introduction
1.2. Understanding Machine Learning in Advertising
1.3. Evolution of Ad Targeting
1.4. Key Applications of Machine Learning in Targeted Advertising
1.5. Benefits of Machine Learning in Advertising
1.6. Challenges and Ethical Considerations
1.7. Future Trends in Machine Learning for Advertising
1.8. Conclusion
Reference

Chapter 2. AI-Enhanced Personalisation: Crafting Tailored Experiences for Every Customer

A. GHAYATHRI, NIRMALA MOHAN

2.1. Introduction
2.2. Benefits of AI-Enhanced Personalisation
2.2.1. Enhanced Customer Engagement
2.2.2. Enhanced Conversion Rates
2.2.3. Enhanced Customer Retention
2.2.4. Enhanced Decision Support
2.2.5. Super Relevant Experiences
2.2.6. Saves Time & Lessens Overwhelm
2.2.7. More Engagement & Conversion
2.2.8. Smart Automation
2.2.9. Hyper-Personalized Communication
2.2.10. Continuous Learning
2.3. Objectives of The Study
2.3.1. To Analyse the Role of AI in Personalized Marketing
2.3.2. To Identify AI Techniques and Tools Enabling Personalization
2.3.3. To Assess the Impact of AI-Enhanced Personalization on Customer Engagement and Retention
2.3.4. To Understand the Challenges and Limitations of AI-Enhanced Personalization
2.4. Significance of The Study
2.5. Scope of The Study
2.6. Review of Literature
2.7. Research Methodology
2.8. Research Data
2.9. Sample Size
2.10. Sampling Technique
2.11. Statistical Tools
2.12. Hypothesis of The Study
2.13. Data Analysis and Interpretation
2.14. Findings of The Study
2.15. Suggestions from The Study
2.15.1. Real-Time AI-Powered Personalization by Customer Behaviour
2.15.2. Quantifying the Effect of AI Personalization: Cross-Channel Integration and Customer Journey Mapping
2.15.3. Determining Effective AI Customization Methods to Engage Consumers
2.15.4. Enforcing Ethical AI Practices in Order to Establish Consumer Trust
2.15.5. Prioritizing Privacy Compliance to Avoid Legal and Ethical Pitfalls
2.16. Conclusion
References

Chapter 3. Marketing-Finance Collaboration in the Age of AI: Toward a Reinvented Era Where Predictive Analysis in Cash Management Drives Marketing Performance

BADRANE NOHAYLA, BARZI GHIZLANE, BADRANE HASNAA

Abstract
3.1. Introduction
3.2. Objective and Methodology
3.3. Results Analysis
3.3.1. Evolution of the Relationship between Marketing and Finance: From Divergence to Convergence
3.3.2. When AI Redraws Modern Finance: A New Horizon for Cash Flow Management and Decision-Making
3.3.3. AI-Augmented Cash Management: Toward Intelligent and Proactive Financial Management
3.3.4. Marketing and Cash Management in the AI Era: A Reinvented Strategic Alliance
3.4. Discussion
3.5. Conclusion
References

Chapter 4. Data-Driven Decision-making and Predictive Analytics

Mostafa Ahmadi

Abstract
4.1. Introduction
4.2. Evolution of AI in Predictive Analytics
4.2.1. Growth of Data and Advanced Analytics
4.3. Rise of ML Algorithms
4.4. AI Applications in Marketing and Predictive Analytics
4.4.1. Customer Segmentation
4.4.2. Customer Behavior Prediction
4.4.3. Personalization and Campaign Optimization
4.4.4. Customer Relationship Enhancement
4.5. Dynamic Pricing Strategies
4.5.1. AI-driven Content Generation
4.5.2. Real-Time Sentiment Analysis
4.5.3. AI in Predictive Supply Chain Management
4.5.4. AI in Customer Retention Strategies
4.6. Ethical Considerations in AI-Driven Marketing
4.6.1. Privacy Concerns
4.6.2. Algorithmic Bias
4.6.3. Transparency and Explainability
4.7. Ethical Implications of Data Ownership
4.8. Consumer Trust and Brand Reputation
4.9. Future Developments in AI and Predictive Analytics
4.9.1. Generative AI in Marketing
4.9.2. Collaboration between Marketing and Technical Teams
4.9.3. Ethical AI Adoption
4.9.4. AI and Real-Time Decision Making
4.9.5. Future-Proofing Businesses through AI
4.10. Conclusion

Reference

Chapter 5. AI-Driven Finance Function and Marketing: A Close Partnership for Performance
Barzi Ghizlane, Badrane Nohayla, Badrane Hasnaa

Abstract

5.1. Introduction
5.2. Methodology
5.3. Results
5.3.1. Towards a More Agile, Automated, Intelligent, and Strategic Finance Function
5.3.2. Importance of Artificial Intelligence in Financial Activities
5.3.3. The Evolution of the Marketing Department in an Intelligent Environment
5.3.4. The Interaction Between the Financial Function and Marketing Through AI
5.3.5. Challenges and Issues of Artificial Intelligence in Financial and Marketing Activities
5.4. Discussion
5.5. Conclusion

Reference

Chapter 6. AI-Driven Content Creation and Curation
Mitra Madanchian, Alireza Rafiee, Hamed Taherdoost

Abstract
6.1. Introduction
6.2. AI-Powered Content Creation
6.2.1. AI-Generated Blogs and Articles
6.2.2. Product Descriptions and Marketing Content
6.2.3. AI in Video, Image, and Audio Content Generation
6.3. AI in Content Curation and Personalization
6.4. Enhancing Creativity and Efficiency with AI
6.4.1. AI-Assisted Brainstorming and Idea Generation
6.4.2. AI-Driven Content Optimization Tools
6.4.3. Automated A/B Testing for Marketing Content
6.4.4. Role of AI in Improving SEO and Content Discoverability
6.5. AI in Visual and Multimedia Content Marketing
6.6. Ethical and Legal Considerations in AI-Driven Content
6.7. AI and the Future of Content Marketing
6.8. Conclusion
References

Chapter 7. Optimizing Marketing Campaigns with AI Automation
T. Shirley Devakirubai

Abstract

7.1. Introduction
7.2. Review of Literature
7.3. Research Questions
7.4. Customer Profiling and Predictive Analysis in Targeted Marketing
7.5. Real-Time Data Analysis and Predictive Modelling in Effective Campaign Performance
7.6. Challenges in AI Marketing Campaigns
7.7. Conclusion

Reference

Chapter 8. Enhancing Customer Loyalty with AI Solutions
Mitra Madanchian, Alireza Rafiee

Abstract
8.1. Introduction
8.2. AI Technologies Powering Customer Loyalty
8.3. Personalization and Customer Engagement
8.4. Predictive Analytics for Customer Retention
8.5. Conversational AI and Customer Support
8.6. Ethical Considerations and Data Privacy
8.7. Future Trends and Strategic Implications
8.8. Conclusion

Reference

Chapter 9. Strategic Selection and Integration of AI Tools in Data-Driven Marketing
Mitra Madanchian, Hamed Taherdoost

Abstract
9.1. Introduction
9.2. Understanding Data-Driven Marketing
9.3. The Role of AI in Data-Driven Marketing
9.4. Criteria for Selecting AI Tools for Marketing
9.5. Data Infrastructure and Preparation
9.6. Developing a Data-Driven AI Marketing Strategy
9.7. Challenges and Considerations
9.8. Conclusion
Reference

Chapter 10. AI-Driven Marketing Strategies in the Digital Era
Jash Khatri, Hamed Taherdoost

Abstract
10.1. Introduction
10.1.1. Background
10.1.2. Problem Statement
10.1.3. Research Questions
10.1.4. Objectives
10.1.5. Scope
10.2. Defining Key Terms
10.2.1. Artificial Intelligence
10.2.2. Marketing
10.3. AI-Driven Marketing Strategies
10.3.1. Customer Segmentation and Targeting
10.3.2. Personalization, Automation, and Customer Experience
10.4. Challenges and Ethical Considerations
10.4.1. Real World Case: Target
10.5. Methodology
10.6. Analysis
10.6.1. AI-Driven Marketing Strategies
10.6.2. Challenges and Ethical Considerations
10.7. Discussion
10.8. Future Research
10.9. Conclusion
References