AI News, Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations

Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations

<p><b>Introduction to Algorithmic Marketing</b> 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 programmatic micro-decisioning — targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.<br><br></p> <p>'A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing.'<br> —Ali Bouhouch, CTO, Sephora Americas<br></p> <p>'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.'<br> —Kyle McKiou, Sr. Director of Data Science, The Marketing Store<br></p> <p>'It is a must-read for both data scientists and marketing officers — even better if they read it together.'<br> —Andrey Sebrant, Director of Strategic Marketing, Yandex<br><br></p> <p> <b>Table of Contents</b><br> </p> <p> Chapter 1 - Introduction<br> - The Subject of Algorithmic Marketing<br> - The Definition of Algorithmic Marketing<br> - Historical Backgrounds and Context<br> - Programmatic Services<br> - Who Should Read This Book?<br> - Summary<br> </p> <p> Chapter 2 - Review of Predictive Modeling<br> - Descriptive, Predictive, and Prescriptive Analytics<br> - Economic Optimization<br> - Machine Learning<br> - Supervised Learning<br> - Representation Learning<br> - More Specialized Models<br> - Summary<br> </p> <p> Chapter 3 - Promotions and Advertisements<br> - Environment<br> - Business Objectives<br> - Targeting Pipeline<br> - Response Modeling and Measurement<br> - Building Blocks: Targeting and LTV Models<br> - Designing and Running Campaigns<br> - Resource Allocation<br> - Online Advertisements<br> - Measuring the Effectiveness<br> - Architecture of Targeting Systems<br> - Summary<br> </p> <p> Chapter 4 - Search<br> - Environment<br> - Business Objectives<br> - Building Blocks: Matching and Ranking<br> - Mixing Relevance Signals<br> - Semantic Analysis<br> - Search Methods for Merchandising<br> - Relevance Tuning<br> - Architecture of Merchandising Search Services<br> - Summary<br> </p> <p> Chapter 5 - Recommendations<br> - Environment<br> - Business Objectives<br> - Quality Evaluation<br> - Overview of Recommendation Methods<br> - Content-based Filtering<br> - Introduction to Collaborative Filtering<br> - Neighborhood-based Collaborative Filtering<br> - Model-based Collaborative Filtering<br> - Hybrid Methods<br> - Contextual Recommendations<br> - Non-Personalized Recommendations<br> - Multiple Objective Optimization<br> - Architecture of Recommender Systems<br> - Summary<br> </p> <p> Chapter 6 - Pricing and Assortment<br> - Environment<br> - The Impact of Pricing<br> - Price and Value<br> - Price and Demand<br> - Basic Price Structures<br> - Demand Prediction<br> - Price Optimization<br> - Resource Allocation<br> - Assortment Optimization<br> - Architecture of Price Management Systems<br> - Summary<br> </p>