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 programmatic micro-decisioning - targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.
"A comprehensive and indispensable reference for anyone undertaking the transformational journey towards algorithmic marketing."
―Ali Bouhouch, CTO, Sephora Americas
"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
Table of Contents
Chapter 1 - Introduction
- The Subject of Algorithmic Marketing
- The Definition of Algorithmic Marketing
- Historical Backgrounds and Context
- Programmatic Services
- Who Should Read This Book?
- Summary
Chapter 2 - Review of Predictive Modeling
- Descriptive, Predictive, and Prescriptive Analytics
- Economic Optimization
- Machine Learning
- Supervised Learning
- Representation Learning
- More Specialized Models
- Summary
Chapter 3 - Promotions and Advertisements
- Environment
- Business Objectives
- Targeting Pipeline
- Response Modeling and Measurement
- Building Blocks: Targeting and LTV Models
- Designing and Running Campaigns
- Resource Allocation
- Online Advertisements
- Measuring the Effectiveness
- Architecture of Targeting Systems
- Summary
Chapter 4 - Search
- Environment
- Business Objectives
- Building Blocks: Matching and Ranking
- Mixing Relevance Signals
- Semantic Analysis
- Search Methods for Merchandising
- Relevance Tuning
- Architecture of Merchandising Search Services
- Summary
Chapter 5 - Recommendations
- Environment
- Business Objectives
- Quality Evaluation
- Overview of Recommendation Methods
- Content-based Filtering
- Introduction to Collaborative Filtering
- Neighborhood-based Collaborative Filtering
- Model-based Collaborative Filtering
- Hybrid Methods
- Contextual Recommendations
- Non-Personalized Recommendations
- Multiple Objective Optimization
- Architecture of Recommender Systems
- Summary
Chapter 6 - Pricing and Assortment
- Environment
- The Impact of Pricing
- Price and Value
- Price and Demand
- Basic Price Structures
- Demand Prediction
- Price Optimization
- Resource Allocation
- Assortment Optimization
- Architecture of Price Management Systems
- Summary
"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
"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
