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Published by Packt Publishing 8/30/2024, 2024
ISBN 10: 1835889409 ISBN 13: 9781835889404
Language: English
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Add to basketPaperback or Softback. Condition: New. Machine Learning and Generative AI for Marketing: Take your data-driven marketing strategies to the next level using Python 1.81. Book.
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Published by Packt Publishing Limited, 2024
ISBN 10: 1835889409 ISBN 13: 9781835889404
Language: English
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Elektronisches Buch in PDF formatKey Features: Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques Combine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challenges Understand 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 (gen AI ) helps the creation of highly personalized, engaging content that resonates with the target audienceThis book provides a comprehensive toolkit for harnessing the power of gen AI 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 salesStarting with the basics of Python for data analysis and progressing to sophisticated ML and gen AI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content and hands-on examples, you'll learn 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 standardsBy the conclusion of this book , you'll be equipped to design, launch, a nd manage marketing campaigns that are not just successful but also cutting-edge.What You Will Learn: Master key marketing KPIs with advanced computational techniques Use explanatory data analysis to drive marketing decisions Leverage ML models to predict customer behaviors, engagement levels, and customer lifetime value Enhance customer segmentation with ML and develop highly personalized marketing campaigns Design and execute effective A/B tests to optimize your marketing decisions Apply natural language processing (NLP) to analyze customer feedback and sentiments Integrate 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 methodologiesTable of Contents The Evolution of Marketing in the AI Era & Preparing Your Toolkit Decoding Marketing Performance with KPIs Unveiling the Dynamics of Marketing Success Harnessing Seasonality and Trends for Strategic Planning Enhancing Customer Insight with Sentiment Analysis Leveraging Predictive Analytics and A/B Testing for Customer Engagement(N.B. Please use the Read Sample option to see further chapters).
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