AI for Marketing

Opening Case Study: Generative AI (ChatGPT/Gemini/Claude/…)

Is Your New Friend, Philosopher, and Guide

1 Introduction to AI in Marketing

Defining Artificial Intelligence

Evolution of AI: From Concept to Commercial Powerhouse

1950s: The Dawn of AI

1960s–1970s: Early Programs and Government Interest

1980s: Rise of Expert Systems

1990s–Early 2000s: The Machine Learning Revolution

2010s: The Era of Big Data and Deep Learning

Present Day: AI in Everyday Life and Business

AI’s Role Across Industries

Retail and E-commerce

Healthcare and Pharmaceuticals

Finance and Banking

Manufacturing, Supply Chain, and Logistics

Entertainment and Media

Education and Learning

Energy, Environment, and Agriculture

Tourism, Hospitality, and Events

Government and Policy

Core AI Technologies in Marketing

Machine Learning: The Foundation of AI in Marketing

Natural Language Processing: Understanding Consumer Conversations

Computer Vision: Interpreting and Leveraging Visual Data in Marketing

Robotic Process Automation: Simplifying Marketing Operations

Impact of AI on Traditional Marketing Practices

Shifts in Strategy: From Broad Campaigns to Precision Marketing

Enhanced Customer Experience: Interaction and Responsiveness

Challenges in AI-Driven Marketing

Summary

Discussion Questions

Projects

Reference

Further Reading

2 Fundamentals of Machine Learning for Marketers

Introduction to Machine Learning in Marketing

Understanding Machine Learning in Marketing

Evolution of Machine Learning in Marketing

Basic Concepts of Machine Learning

Key Terminologies in Machine Learning

How Machine Learning Works: Training, Testing, and Validation

Types of Machine Learning in Marketing

From Early AI Research to Modern Marketing Applications

Supervised Learning: Learning from Labelled Data

Unsupervised Learning: Discovering Hidden Patterns

The Role of Reinforcement Learning and Deep Learning in Marketing

Choosing the Right ML Approach for Marketing

Challenges of Machine Learning in Marketing

Bias and Fairness in Machine Learning

Ethical Concerns in AI-Driven Marketing

Algorithmic Bias in Marketing AI

Data Privacy Concerns in ML-Driven Marketing

Transparency of Algorithms and Human Security

Summary

Discussion Questions

Projects

References

Further Reading

3 AI-Driven Consumer Insights and Data Management

The Interplay of AI, Data, and Consumer Insights

Theoretical Foundations for AI and Consumer Insights

The Growing Influence of AI in Marketing

AI and Data Quality

Data Collection, Cleaning, and Preprocessing

Importance of Data Collection

Data Cleaning

Data Preprocessing

Transition to AI-Driven Consumer Insights

Ethical Considerations in AI-Driven Marketing

Data Privacy and Security Issues

Bias and Fairness in AI Algorithms

Guidelines for Ethical AI in Marketing

Summary

Discussion Questions

Projects

References

Further Reading

4 AI-Driven Consumer Insights

The Rise of AI in Understanding Consumers

AI-Driven Consumer Insights

Customer Segmentation with AI

Key Clustering Techniques Used in AI-Based Segmentation

Why AI-Based Segmentation Is Superior to Traditional Approaches?

Predictive Analytics for Customer Behaviour

Key Applications of Predictive Analytics in Marketing

Common AI and ML Models Used in Predictive Analytics

Personalization and Recommendation Systems

Types of AI-Powered Recommendation Systems

Technical Architecture of Recommendation Systems

Strategic Marketing Impact of Recommendation Systems

Integration in Practice

Summary

Discussion Questions

Projects

Further Reading

5 Natural Language Processing and Network Analysis in Marketing

The Role of Language and Networks in Marketing Today

Language and Network

The Rise of Unstructured Data in Marketing

Importance of Language Analysis

Networks: The Other Side of Conversation

The Shift Toward Interpretive and Relational Marketing

Understanding Natural Language Processing

Why Do Marketers Need NLP?

Fundamentals of Natural Language Processing

Where NLP Meets Marketing: Practical Applications

The Importance of Mapping Relationships and Conversations

Text Mining for Marketing Insights

Text Mining and the Value of Unspoken Feedback

Easy and Interpretable Techniques for Text Mining

Advanced but Accessible Methods

Term Frequency–Inverse Document Frequency

Social Network Analysis in Marketing

Mapping Relationships and Conversations in Marketing

Key Concepts in Network Analysis

The Role of SNA in Marketing

Tools for Social Network Analysis

Summary

Discussion Questions

Projects

Reference

Further Reading

6 Hands-On Applications of NLP and SNA in Marketing

Making Sense of Conversations: Customer Insights from 20 Reviews

Word Cloud: What Dominates the Conversation?

Topic Modelling: What Themes Do Customers Talk About?

Network Analysis of Themes: What Is the Relationship Among Themes?

Word Clouds in Marketing Analytics

Characteristics of Word Clouds

Precautions in Using Word Clouds

Tools for Creating Word Clouds

Topic Modelling of Customer Conversations

Characteristics of Topic Modelling

Precautions in Applying Topic Modelling

Tools for Topic Modelling

Visualization Topics for Better Understanding

Network Analysis of Customer Conversations and Themes

Types of Network Analysis in Marketing Texts

Tools for Theme Network Analysis

Summary

Discussion Questions

Projects

Further Reading

Annexure 6A: Sample Dataset for Practice

Annexure 6B: Templates for Marketing Insights Reports

7 Generative AI in Marketing

Generative AI in Marketing: What It Is and Why It Matters

Understanding Generative AI

Core Technologies of Generative AI

Importance of Generative AI for Marketers

Foundation Models of Generative AI

Natural Language Models

Multimodal Models

Model Architectures for Generative AI

Generative Adversarial Networks

Diffusion Models

Variational Autoencoders

Content Creation with Generative AI

Types of Marketing Content Generated by AI

Tools and Templates That Streamline Content Workflows

Comparing Generative Outputs for Brand Voice

Practical Application: HubSpot’s Use of GPT

Summary

Discussion Questions

Project

Further Reading

Annexure 7A: Hands-On Project—Creating and Comparing Product Descriptions Across Generative AI Tools 204

8 Prompt Engineering

Fundamentals of Prompt Engineering

Elements of an Effective Prompt

Common Mistakes in Prompt Design and Their Solutions

Types and Formats of Prompts in Marketing

Prompting Techniques in Marketing Communication

Formats of Prompts Used in Marketing

Multi-turn and Adaptive Prompting

Prompting for Specific Marketing Goals

Frameworks for Writing Effective Prompts

Role–Task–Format

Task–Action–Goal

Before–After–Bridge

Context–Action–Result–Example

Role–Input–Steps–Expectation

Character–Request–Example–Adjustment–Type of Output–Extras/Examples

Clarity–Relevance–Iteration–Specificity–Parameters–Examples

Rephrase–Append–Contextualize–Examples–Follow-up

Role–Emphasis–Limitation–Information–Challenge

Background–Logic–Outline–Goal

Application of Prompt Engineering in Marketing Campaigns

Social Media and Content Marketing

Email and Direct Marketing

Customer Support and Conversational Marketing

Evaluation and Refinement of Prompt Effectiveness

Key Performance Indicators for Prompt Outcomes

Tools and Techniques for Prompt Performance Assessment

Iterative Refinement of Prompts

Summary

Discussion Questions

Projects

Further Reading

Annexure 8A: Report on Emerging Career Landscape for Prompt Engineers in India and Beyond

9 AI in Digital Advertising

The Evolution of Digital Advertising and the Role of AI

Automation in Advertising

Programmatic Advertising and Automating Media Buying with AI

Key Concepts of Programmatic Advertising

How RTB Integrates into the Programmatic Workflow

Benefits of Programmatic Advertising

Tools and Platforms

Challenges in Programmatic Advertising

Future of Programmatic Advertising

AI-Enhanced Ad Targeting and Optimization

Generative AI for Ad Creative

Key Capabilities of Generative AI in Advertising

How Does Generative AI Work?

Ethical Considerations in Maintaining Authenticity

Real-Time Optimization of Digital Campaigns

Functioning of Real-Time Optimization

Key Techniques in Real-Time Optimization

Measuring Ad Effectiveness and Turning Clicks into Clarity

Meaning of Ad Effectiveness in Today’s Context

Key Metrics in the AI Era

AI Tools Powering Effectiveness Measurement

Challenges in Measuring Ad Effectiveness

Long-Term Impacts and Future Directions

Summary

Discussion Questions

Projects

References

Further Reading

Annexure 9A: Links for Some Beautifully Created AI Ads

10 AI for Customer Relationship Management

Understanding CRM in the Age of AI

Exploring Leading AI-Powered CRM Tools

Automating Customer Service with AI

Enhancing Personalization at Scale

Applying Predictive Analytics in CRM

Addressing Strategic Impact and Future Trends

Redefining CRM as a Strategic Function

Impact Across Business Functions

Identifying Key Functions of AI in CRM

Future Trends in AI-Powered CRM

Summary

Discussion Questions

References

Further Reading

11 AI and Services Marketing

AI Integration in Services

Service Design and AI-Enhanced Blueprinting

Service Blueprinting in the AI Era

Journey Analytics and Micro-Moment Detection

Process Automation and Frontline Support

Personalized and Intelligent Customer Experiences

Personalization vs. Customization in AI Context

Customer Experience Metrics and AI Dashboards

Emotion AI, Sentiment Analysis, and Feedback Loops

No-Code AI Implementation for Service Teams

Basics of Prompt Design for Service Bots

No-Code AI Tools for Workflow Automation

AI Prompt Templates for Service Response Scenarios

Workforce Development for AI-Enabled Services

Human–AI Collaboration in Service Delivery

Empathy Development in Automated Environments

Training Programmes for Service Employee Upskilling

Service Marketing Triangle Transformation

Company to Customer: Enhancing External Marketing with AI

Company to Employee: Redefining Internal Marketing with AI

Employee to Customer: Augmenting Interactive Marketing with AI

Summary

Discussion Questions

Projects

Further Reading

Annexure 11A: AI Use Cases in the Service Sector

Annexure 11B: AI Prompt Templates for Service Scenarios

12 AI in Product Development and Innovation

Rethinking Innovation in the Age of Intelligence

Marketing-Led Innovation in the AI Era

Market Insight and Product Ideation

Strategic Role of Marketing

Continuous Innovation Loops

AI-Driven Product Design

Generative Design and Prototyping

Collaborative Co-creation

Prompt Engineering Ideation

AI-Enabled Market Research and Consumer Insight

AI in Product Lifecycle Management

Product Introduction Decisions

Growth Forecasting and Inventory

Maturity Optimization

Decline and Repositioning Strategies

Frameworks and Strategic Models

AI-Driven Innovation Funnel

TRIZ and AI Integration

Jobs-to-be-Done with Machine Learning

Summary

Discussion Questions

Projects

Reference

Further Reading

13 Robots, Drones, and AI in Marketing

Robotics and Drones in the Marketing Landscape

Robotics in Marketing

Types of Robots in Marketing

Retail Experience Enhancement

Events and Experiential Marketing

Drones in Marketing

Drone-Based Advertising

Visual Content Creation

Product Delivery and Sampling

Human–Robot Interaction in Consumer Experience

Anthropomorphism and Trust

Customer Service Automation

Emotional Engagement

Challenges and Cautions

Challenges, Ethics, and Future of Robotics in Marketing

Operational Risks and Limitations

Consumer Privacy and Data Ethics

Inclusivity and Accessibility

Future Trends

Summary

Discussion Questions

References

Further Reading

Annexure 13A: Comparing Marketing Technologies—Robots, Drones, Chatbots, RPA, Virtual Agents, AR/VR

14 AI, Analytics, and Decision-Making

AI in Decision-Making

AI-Driven Decision-Making

Data-Driven Marketing Strategies

Key Elements of Data-Driven Strategy

Building a Decision Pipeline

The Five Stages of the Marketing Decision Pipeline

Excel AI in Marketing Practice

Indian Super Store’s Expanding Growth

Limitations of AI-Driven Insights in Excel

Excel AI vs. Advanced Platforms

Summary

Discussion Questions

Further Reading

Annexure 14A: Turning Data into Decisions with Excel AI—Segment-Smart–Identify and Optimize Key Marketing Segments

15 Ask, Analyse, Act: Marketing Decision-Making with Generative AI

Generative AI in Marketing Decision-Making

Key Concepts of Generative AI and ChatGPT

Key Capabilities of ChatGPT for Marketing Decision-Making

Importance for Marketing Decision-Making

Demonstration Focus

ChatGPT as an Analytical Assistant

Field Knowledge Through Conversation

Prompt Engineering Principles

Clarification of Variable Roles

Data Preprocessing and Exploration with Prompts

Dataset Fields

Prompt-by-Prompt Workflow

Bivariate and Multivariate Analysis with Prompts

Step-by-Step Analysis with Prompts

Data Preparation for Modelling

Predictive Modelling with ChatGPT

Strategic Takeaways

Ethics, Accuracy, and Human Oversight

Limitations of Generative AI in Analytics

Bias, Hallucination, and False Pattern Recognition

Human-in-the-Loop for Strategic Control

Validation of AI-Generated Insights

Summary

Discussion Questions

Further Reading

Annexure 15A: Building Your Own AI-Powered Marketing Analyst

16 Ethics, Privacy, and Future Trends in AI-Driven Marketing

Bias and Responsibility in AI-Powered Branding

Ethical Challenges in AI Marketing Practice

Algorithmic Bias and Fairness

Transparency, Accountability, and Explainability

Privacy, Consent, and Data Governance

The New Landscape of Consumer Privacy

Consent Management and Consumer Autonomy

Data Security and Risk Mitigation

Regulatory Frameworks and Governance in AI Marketing

Global Regulations

Building an AI Governance Framework

Emerging AI Technologies and Their Marketing Implications

Generative AI 2.0 and Multimodal Models

Emotion AI and Behavioural Sensing

Personal AI Agents and Autonomous Campaigns

The Future of Work and Marketing Roles

Augmentation, Not Replacement

New Skillsets for AI-Literate Marketers

Summary

Discussion Questions

Further Reading

Closing Case Study: Crafting an AI-Powered Marketing Proposal

Appendix A: AI for Marketing—Interactive Video Learning Resource

Appendix B: AI Prompt Library for Marketers

Appendix C: AI Frameworks and Models Summary

Glossary

Subject Index

Company Index

“This volume gets the balance right—rigour without jargon, practice without shortcuts. AI for Marketing translates complex techniques into decisions that matter in classrooms, boardrooms, and field execution. The cases, prompt-led exercises, and metrics-first
approach will help faculty teach better and managers deliver outcomes faster. A timely, practitioner-ready text for institutions serious about building AI-capable marketing talent.”

—Rajeev Kumra, Director, T A Pai Management Institute

Manipal Academy of Higher Education (MAHE), India “A sharp, practitioner-ready guide that connects AI with the realities of content, commerce, and communities. This book helps decision-makers turn data into narratives and narratives into measurable growth.”

—Annurag Batra, Chairman & Editor-in-Chief, BW Businessworld Founder, exchange4media Group

“AI for Marketing masterfully bridges technological innovation and human insight, offering a globally relevant and ethically grounded roadmap for marketers seeking to harness AI to create intelligent, empathetic, and data-driven strategies.”

—Silvia Cachero Martínez, Associate Professor of Marketing

“School of Economics and Business, Oviedo, Asturias “The authors have successfully simplified the complexity of AI, making it a clear learning experience for managers and students. It combines academic precision with strategic foresight, making it ideal for contemporary business schools and universities.”

—Nimit Chowdhary, Vice Chancellor, Rajasthan Technical University, India

“AI for Marketing explores the fusion of human curiosity and computational power, showing how data and empathy can work together to create smarter, personalized customer experiences. This book is a timely guide for any technology-driven enterprise.”

—Navneeta Borooah, Global Director of Marketing, Capgemini

“AI for Marketing is a timely and insightful synthesis that bridges algorithms and empathy. It not only explains the science behind AI but also redefines marketing as a discipline of data-driven human understanding.”

—Agnieszka Tetla, University of Economics in Katowice, Poland