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Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations, by Ilya Katsov
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Review
"At a time when power is shifting to consumers, while brands and retailers are grasping for fleeting moments of attention, everyone is competing on data and the ability to leverage it at scale to target, acquire, and retain customers. This book is a manual for doing just that. Both marketing practitioners and technology providers will find this book very useful in guiding them through the marketing value chain and how to fully digitize it. A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."— Ali Bouhouch, CTO, Sephora Americas "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 "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. Elegant math articulates the important relationships that are so elusive to traditional business modeling. The book is unapologetic for its lack of spreadsheet examples — much of the world can not be represented linearly in just a few dimensions and devoid of uncertainty. Instead, the book embraces rigorous framing that yields better insights into real phenomenon. It's written neither for the data scientist nor the marketer, but rather for the two combined! Its this partnership between these two departments that will lead to real impact. This book is where that partnership should begin."— Eric Colson, Chief Algorithms Officer, Stitch Fix "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 ---"This books delivers a complete end-to-end blueprint on how to fully digitize your company's marketing operations. Starting from a conceptual architecture for the future of digital marketing, it then delves into detailed analysis of best practices in each individual area of marketing operations. 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; serial entrepreneur, founder of RedAril and Octane "While virtually every business manager today grasps the conceptual importance of data analytics and machine learning, the challenge of implementing actual competitive solutions rooted in data science remains quite daunting. The scarcity of data scientist talent, combined with the difficulty of adapting academic models, generic open-source software and algorithms to industry-specific contexts are among the difficulties confronting digital marketers around the world. 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; former CEO and Chairman of 3Com and Palm ---
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Product details
Hardcover: 506 pages
Publisher: Ilia Katcov (December 2, 2017)
Language: English
ISBN-10: 0692989048
ISBN-13: 978-0692989043
Product Dimensions:
6 x 1.5 x 9 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
11 customer reviews
Amazon Best Sellers Rank:
#528,502 in Books (See Top 100 in Books)
This book is a good summary of industry's experience, explained by the architect with scientific background. Unlike from many other books, author had no goal to write some pre-defined amount of pages, but had a goal to make the comprehensive overview, merging all sources and experience available to a single knowledge source.It's hard to read, it's hard to understand, because of it's saturated with a lot of mathematical explanations. This book is overkill for most of the engineers and companies. Almost for sure, this book will require from you a lot of knowledge you don't have yet and will use all your brain power for every single page.But it worth the time and effort spent IMHO. All you need to know in the same place. Properly organized and explained in details.You know, with professional books, the biggest challenge is not to afford a book's price, but make the right decision, should you invest your time in reading or not, because it's much more valuable. My IMHO - this book worth the time investment.It's available for free in PDF from the author's site, so you can read it before order. But I bought the paper version.P.S. I worked with Ilya in the same company, when I had a title of Principal Engineer and was focused mostly on eCommerce projects. I had a draft of the book from the author before it was published. I can be biased, but did my best to be professional.
I couldn't understand the target audience of the book. It is neither a textbook nor a self-study book. The author simply compiled a list of mathematical models that can be applied in the marketing but didn't provide concrete examples with data to show how actually models are used. All you need is the table of content of the book and then you can read the Wikipedia pages for better understanding. I almost felt like the author rushed to get published and didn't actually want the reader to understand the concepts sufficiently and be comfortable enough to apply. The severe lack of example analysis using data sets corresponding to the compiled theories vividly manifests this shortcoming. in short, you can buy this book if you are interested to know which mathematical theories and tools people use in marketing without any expectation of being able to apply the theories.
The book is divided into six large and highly distinct chapters or parts, each of which deserves a dedicated review.The introductory chapter focuses on the relationship between marketing and mathematical optimization. It draws a big picture of marketing automation and its relationship with data science. I think that it connects business problems with technology in a very meaningful way and establishes a new fundamental framework that advances understanding of modern marketing.The second chapter focuses on machine learning and provides a decent overview of machine learning methods such as regression and clustering. While its aim is to “briefly review†the key methods, it is a bit overloaded with mathematical detail. Although the chapter is well structured, it can come across as overly complex for readers who are not familiar with ML and statistics, yet excessively shallow for readers who are.The third chapter is dedicated to targeted ads and promotions. This is where the book turns really valuable, as it delivers a very detailed and comprehensive description of methods for customer data analytics. Having read another great book on this subject, Winston’s Marketing Analytics: Data-Driven Techniques with Microsoft Excel, I have found this one to be more rigorous and advanced.The third and fourth chapters engage in a discussion about the implementation of search and recommendation services. While I see it as a good theoretical study, I think that it would benefit from more practical examples, using search engines like ElasticSearch or Solr.The last topic is price management. The chapter provides a number of techniques for retailers, but manufacturing and wholesaling are not covered. I especially appreciate that it describes practical models used by companies like RueLaLa and that it is not predominantly theoretical.Overall, I find this book to be well written, practically useful, and crisp. Despite covering many topics, it helps the reader master a solid methodology for applying ML and AI techniques in marketing.
This is probably the deepest and most comprehensive book on data-driven marketing. The scope is impressive; in addition to the introduction, the book has four massive chapters that cover promotions, searches, recommendations, and pricing. But what really stands out is the strong focus on the industrial reports and models used by leading companies such as LinkedIn and MediaMath.Keep in mind that it is a very technical book that assumes a good knowledge of statistics, and it is full of optimization models and numerical examples. This is good for developers and data scientists who want to understand the economic context and implementation details but not so good for business users, in my opinion.
Very rarely you come across a book that you just don't want to buy for yourself but also for your team. This book belongs in that category. Author has done justice to the marketing topics and able to compartmentalize marketing data science in right domains. I personally have 2 copies of the book and 3rd copy (paperback) on the way and that would be going out to the team.
A top-notch text that demonstrates true mastery in statistics and machine learning. Most of the knowledge here is portable to domains outside of the specific niche of "algorithmic marketing". If you're on the fence, just buy it. This will be a long-term facet in my ML library. Well done.
best book on applied data science with Marketing
Really nice, good price
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