What this book covers
Chapter 1, The Evolution of Marketing in the AI Era and Preparing Your Toolkit, explores the evolution of marketing in the AI era, core AI/ML techniques shaping marketing’s future, and setting up a Python environment for marketing projects.
Chapter 2, Decoding Marketing Performance with KPIs, explains that every data-driven or AI/ML-driven marketing strategy starts with optimization and enhancement goals tied to key performance metrics (KPIs). Understanding and analyzing these KPIs is critical for evaluating the effectiveness of marketing campaigns and strategies. This chapter discusses how to identify and compute crucial marketing KPIs, leveraging them for explanatory data analysis, and using these insights to drive marketing decisions.
Chapter 3, Unveiling the Dynamics of Marketing Success, investigates the underlying dynamics contributing to marketing success. By utilizing data science and AI/ML techniques, you can gain deep insights into the factors driving successful marketing campaigns. The topics covered include analyzing customer interactions, identifying successful marketing patterns, and optimizing strategies based on data-driven insights.
Chapter 4, Harnessing Seasonality and Trends for Strategic Planning, discusses seasonality and trends, which play a vital role in shaping marketing strategies. This chapter focuses on leveraging AI/ML to identify and capitalize on predictable fluctuations in consumer behavior and market dynamics. By understanding these patterns, you can strategically plan and execute campaigns that align with consumer interests and market conditions.
Chapter 5, Enhancing Customer Insight with Sentiment Analysis, discusses customer sentiment, which is a valuable indicator of brand perception and customer satisfaction. This chapter explores how sentiment analysis, powered by AI/ML, can enhance customer insights. You will learn how to analyze customer feedback, reviews, and social media interactions to tailor your strategies to improve customer experience and engagement.
Chapter 6, Leveraging Predictive Analytics and A/B Testing for Customer Engagement, covers predictive analytics and A/B testing, which are essential tools for enhancing customer engagement. This chapter explains how to use AI/ML models to predict customer behavior and engagement levels. It also covers the design and execution of effective A/B tests to optimize marketing decisions and improve user experiences.
Chapter 7, Personalized Product Recommendations, explores personalized product recommendations, which significantly enhance the likelihood of purchase by aligning offerings with customer preferences and behaviors. This chapter covers various techniques for generating personalized recommendations, including market basket analysis, collaborative filtering, and other recommendation methods.
Chapter 8, Segmenting Customers with Machine Learning, discusses customer segmentation, which is a critical aspect of targeted marketing. This chapter discusses using ML to segment customers based on various factors, such as purchase behavior, demographics, and interests. It covers techniques like K-means clustering and leveraging large language models (LLMs) for deeper customer segmentation insights.
Chapter 9, Creating Compelling Content with Zero-Shot Learning, introduces zero-shot learning (ZSL), a technique that allows AI models to generate relevant marketing content without prior direct examples. ZSL enhances creativity and speed, allowing you to produce content for new categories and contexts using learned patterns and knowledge extrapolation. You will find practical examples and best practices for integrating ZSL into marketing strategies, showcasing how to create dynamic content for product descriptions, blog posts, and social media.
Chapter 10, Enhancing Brand Presence with Few-Shot Learning and Transfer Learning, covers few-shot learning (FSL), which is a method that’s used to adapt AI models to new tasks using only a small number of labeled examples.
This chapter covers the principles of FSL, including meta-learning, which enable models to generalize effectively to new scenarios. The practical examples demonstrate how you can apply FSL to marketing campaigns, emphasizing its importance in capturing nuanced aspects of brand ethos and refining marketing strategies based on customer feedback.
Chapter 11, Micro-Targeting with Retrieval-Augmented Generation, this chapter explores retrieval-augmented generation (RAG) as a tool for precision marketing, combining GenAI with real-time information retrieval. This hybrid approach allows the creation of highly personalized content by accessing and incorporating current data during the generation process. This chapter details RAG’s operational framework and its application in micro-targeting, showcasing how it can improve consumer engagement and conversion by providing contextually appropriate and accurate content.
Chapter 12, The Future Landscape of AI and ML in Marketing, consolidates key AI and ML concepts from previous chapters and discusses their future applications in marketing. It covers emerging technologies such as multi-modal GenAI, advanced model architectures, and the integration of AI with augmented and virtual reality. These advancements promise to create more dynamic, responsive, and personalized marketing strategies. This chapter provides insights into how these technologies will shape the future of marketing, enabling more immersive consumer experiences and innovative marketing practices.
Chapter 13, Ethics and Governance in AI-Enabled Marketing, addresses the ethical considerations and governance challenges associated with AI technologies in marketing. It explores data privacy, algorithmic bias, and the need for transparency, emphasizing their impact on consumer trust and brand integrity. The chapter also covers major regulatory frameworks such as GDPR and CCPA, providing strategies for responsible AI deployment, model transparency, and compliance. Finally, it discusses practical guidelines for mitigating bias, ensuring data privacy, and establishing robust governance structures to promote ethical AI use in marketing.
