Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using Python
Original price was: $49.99.$47.49Current price is: $47.49.
Price: $49.99 - $47.49
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Start transforming your data-driven marketing strategies and increasing customer engagement. Learn how to create compelling marketing content using advanced gen AI techniques and stay in touch with the future AI ML landscape.
Purchase of the print or Kindle book includes a free eBook in PDF format
Key FeaturesEnhance customer engagement and personalization through predictive analytics and advanced segmentation techniquesCombine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challengesUnderstand cutting-edge AI concepts and their responsible use in marketingBook Description
In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it’s a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience.
This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales.
Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you’ll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you’ll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards
By the conclusion of this book, you’ll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.
What you will learnMaster key marketing KPIs with advanced computational techniquesUse explanatory data analysis to drive marketing decisionsLeverage ML models to predict customer behaviors, engagement levels, and customer lifetime valueEnhance customer segmentation with ML and develop highly personalized marketing campaignsDesign and execute effective A/B tests to optimize your marketing decisionsApply natural language processing (NLP) to analyze customer feedback and sentimentsIntegrate ethical AI practices to maintain privacy in data-driven marketing strategiesWho this book is for
This book targets a diverse group of professionals:
Data scientists and analysts in the marketing domain looking to apply advanced AI ML techniques to solve real-world marketing challenges
Machine learning engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposes
Marketing professionals, business leaders, and entrepreneurs who must understand the impact of AI on marketing
Reader are presumed to have a foundational proficiency in Python and a basic to intermediate grasp of ML principles and data science methodologies
Table of ContentsThe Evolution of Marketing in the AI Era & Preparing Your ToolkitDecoding Marketing Performance with KPIsUnveiling the Dynamics of Marketing SuccessHarnessing Seasonality and Trends for Strategic PlanningEnhancing Customer Insight with Sentiment Analysis
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Publisher : Packt Publishing (August 30, 2024)
Language : English
Paperback : 482 pages
ISBN-10 : 1835889409
ISBN-13 : 978-1835889404
Item Weight : 2.25 pounds
Dimensions : 0.99 x 7.5 x 9.25 inches
4 reviews for Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using Python
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steven –
Great primer for anyone looking for a data-driven marketing strategy
As a machine learning engineer with experience in the technical aspects of artificial intelligence but no formal background in marketing, I was intrigued by this bookâs promise to bridge the gap between data science and real-world marketing applications. Running my own artisanal donut business has brought me into the world of marketing, where understanding how to position and promote a product is essential. This book turned out to be a valuable resource for someone like me, blending technical rigor with practical marketing insights. Even from the first few chapters, I was able to get an idea of how to interpret and harness KPI’s that I was not even familiar with, such as cost per acquisition or customer lifetime value, which quickly revealed huge gaps in my Instagram and Google marketing strategies. Having spent most of my professional career working primarily on NLP classification problems, it was also useful to get deep dive into lesser-known areas like casual inference, A/B testing, anomaly analysis, forecasting (ARIMA, etc) that have proven useful even in business in areas outside of just marketing. Some areas, such as the incorporaron of generative models served more as a preview of what is to come, as I my business is still in its early stages and I do not have data at such a scale to warrant RAG type solutions. However, it did give me some great ideas on how to best respond to things like Google Maps reviews and interactions within the UberEats platform, which provides richer data about customers. Overall, I’d definitely recommend this book for anyone with some technical skills who either like a very detailed primer on DS/ML techniques, or understand how to apply their existing knowledge to the marketing domain.
saandeep sreerambatla –
A Comprehensive Guide to AI and Generative Technologies
This book offers a comprehensive exploration of the transformative impact that AI and machine learning are having on the marketing industry. This book is meticulously structured to cater to professionals at the intersection of technology and marketing, aiming to provide them with the tools and knowledge necessary to thrive in an increasingly data-driven landscape.The authors begin by tracing the evolution of marketing in the AI era, providing a solid historical context before diving into the core AI/ML techniques that are shaping the future of the field. The bookâs pragmatic approach is evident in its hands-on examples, which guide readers through the setup of Python environments tailored for marketing projects. This ensures that even those new to the field can follow along and apply the concepts to real-world scenarios.One of the standout aspects of this edition is its focus on actionable insights. Whether itâs through predictive analytics, customer segmentation, or A/B testing, the book consistently emphasizes how AI/ML can be leveraged to make informed marketing decisions. The chapters dedicated to sentiment analysis and personalized recommendations are particularly insightful, demonstrating how businesses can enhance customer engagement and drive growth using these advanced techniques.Moreover, the book doesnât shy away from the ethical implications of AI in marketing. The final chapter provides a thorough examination of the ethical considerations and governance challenges associated with AI, ensuring that readers are not only equipped to implement AI-driven strategies but also to do so responsibly.In summary, “Machine Learning and Generative AI for Marketing, Second Edition” is an invaluable resource for marketers, data scientists, and business leaders who are eager to harness the power of AI/ML in their marketing strategies. Its blend of theory, practical examples, and ethical guidance makes it a must-read for anyone looking to stay ahead in the rapidly evolving world of digital marketing.
Roberto Williams Batista –
A Broad Overview of Machine Learning and Generative AI in Marketing
Positive AspectsThe book covers a broad range of ML techniques used in marketing, offering readers a general understanding of each topic. It serves as a reference list to machine learning (ML) and generative AI applied to marketing, helping data professionals grasp the scope of what’s possible in the field.Areas for ImprovementHowever, the book faces a significant challenge in addressing ML and generative AI in marketing without the involvement of marketing author. This is evident in its lack of coherent content organization, insufficient introductory material for technical readers unfamiliar with marketing, and a missed opportunity to delve deeper into why specific techniques are chosen over alternatives. Experienced professionals may also notice the absence of coverage on advanced topics like graph neural networks, a must-know technology in the field.That said, if you’re a data scientist (or in a similar field) with prior marketing knowledge and you’re looking for an overview of ML techniques in marketing, this book can still be a useful resource.
Jonathon Tepezky –
Really love the hands-on, applied approach taken in this book and how they include plenty of examples with code so you can easily follow along/modify to get a grasp of the concepts. Also a great primer to understand core marketing concepts which were completely new to me. In the GenAI section, they also touch upon theory – not at the level of derivations, you’ll need to find other resources to cover that – but just enough that you can appreciate the approach and how it works