Introduction to
Algorithmic Marketing
Algorithmic Marketing
by Ilya Katsov
Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers. It summarizes various techniques tested by major technology, advertising, and retail companies, and it glues these methods together with economic theory and machine learning. The book covers the main areas of marketing that require micro-decisioning – targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.
Print length
508 pages
Publication date
December 2, 2017
ISBN
978-0692142608
Editorial Reviews
"A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."
—Ali Bouhouch, CTO, Sephora Americas
“Its all possible now. This book brings practicality to concepts that just a few years ago would have been dismissed as mere theory. It features principled framing that captures what the best marketers innately feel but cannot express.”
—Eric Colson, Chief Algorithms Officer, Stitch Fix
“Introduction to Algorithmic Marketing isn’t just about machine learning and economic modeling. It’s ultimately a framework for running business and marketing operations in the AI economy.”
—Kyle McKiou, Sr. Director of Data Science, The Marketing Store
“If you’re tired of the vague fluff about AI in marketing, and you want to understand the real substance of what’s possible today and how it works, then you must read An Introduction to Algorithmic Marketing. This is the best book in the field of marketing technology and operations that I’ve read yet.”
—Scott Brinker, Author of Hacking Marketing, Editor of chiefmartec.com
“This book is a live portrait of digital transformation in marketing. It shows how data science becomes an essential part of every marketing activity. The book details how data-driven approaches and smart algorithms result in deep automation of traditionally labor-intensive marketing tasks. Decision-making is getting not only better but much faster, and this is crucial in our ever-accelerating competitive environment. It is a must-read for both data scientists and marketing officers—even better if they read it together.”
—Andrey Sebrant, Director of Strategic Marketing, Yandex
“The book gives the executives, middle managers, and data scientists in your organization a set of concrete, actionable, and incremental recommendations on how to build better insights and decisions, starting today, one step at a time.”
—Victoria Livschitz, founder and CTO, Grid Dynamics
“This book provides a much-needed collection of recipes for marketing practitioners on how to use advanced methods of machine learning and data science to understand customer behavior, personalize product offerings, optimize the incentives, and control the engagement – thus creating a new generation of data-driven analytic platform for marketing systems.”
—Kira Makagon, Chief Innovation Officer, RingCentral
“This book by Ilya Katsov draws from the deep domain expertise he developed at Grid Dynamics in delivering innovative, yet practical digital marketing solutions to large organizations and helping them successfully compete, remain relevant, and adapt in the new age of data analytics.”
—Eric Benhamou, Founder and General Partner, Benhamou Global Ventures
Resources
Find models from this book on GitHub at https://github.com/ikatsov/tensor-house/
About the Author
Ilya Katsov is a VP of Technology at Grid Dynamics, a global consulting company that specializes in emerging technology. Ilya works on innovative data science and AI solutions for large enterprises. Prior to joining Grid Dynamics, Ilya worked at Intel Research on wireless communication technologies. He is the author of two books:
Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations (2017)
The Theory and Practice of Enterprise AI (2022) (book's site)
Non-English Editions
The book is available in Japanese, Korean, and Russian. See the editions page for more details.