Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Machine Learning and Generative AI for Marketing
  • Table Of Contents Toc
Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By : Yoon Hyup Hwang, Nicholas C. Burtch
close
close
Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By: Yoon Hyup Hwang, Nicholas C. Burtch

Overview of this book

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.
Table of Contents (16 chapters)
close
close
14
Other Books You May Enjoy
15
Index

Collaborative filtering

There are various approaches in building recommendation systems and often multiple approaches take part. Some of the frequently used AI/ML-driven approaches in building recommendation systems are:

  • Collaborative filtering: This method uses previous user behaviors, such as pages they viewed, products they purchased, or ratings they have given previously. This algorithm leverages this kind of data to find similar products or content to those that the users have shown interest in previously. For example, if a user viewed a few thriller movies on a streaming platform, some other thriller movies may be recommended to this user. Or, if a customer bought dress shirts and dress shoes on an e-commerce platform, dress pants may be recommended to this customer. The collaborative filtering algorithms are often built based on the similarities between users or items:
    • User-based collaborative filtering uses data to find similar users based on the pages...
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Machine Learning and Generative AI for Marketing
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon