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

1. The Subject of Algorithmic Marketing
2. The Definition of Algorithmic Marketing
3. Historical Backgrounds and Context
4. Programmatic Services
5. Who Should Read This Book?
6. Summary

Chapter 2 - Review of Predictive Modeling

1. Descriptive, Predictive, and Prescriptive Analytics
2. Economic Optimization
3. Machine Learning
4. Supervised Learning
5. Representation Learning
6. More Specialized Models
7. Summary

Chapter 3 - Promotions and Advertisements

1. Environment
2. Business Objectives
3. Targeting Pipeline
4. Response Modeling and Measurement
5. Building Blocks: Targeting and LTV Models
6. Designing and Running Campaigns
7. Resource Allocation
8. Online Advertisements
9. Measuring the Effectiveness
10. Architecture of Targeting Systems
11. Summary

Chapter 4 - Search

1. Environment
2. Business Objectives
3. Building Blocks: Matching and Ranking
4. Mixing Relevance Signals
5. Semantic Analysis
6. Search Methods for Merchandising
7. Relevance Tuning
8. Architecture of Merchandising Search Services
9. Summary

Chapter 5 - Recommendations

1. Environment
2. Business Objectives
3. Quality Evaluation
4. Overview of Recommendation Methods
5. Content-based Filtering
6. Introduction to Collaborative Filtering
7. Neighborhood-based Collaborative Filtering
8. Model-based Collaborative Filtering
9. Hybrid Methods
10. Contextual Recommendations
11. Non-Personalized Recommendations
12. Multiple Objective Optimization
13. Architecture of Recommender Systems
14. Summary

Chapter 6 - Pricing and Assortment

1. Environment
2. The Impact of Pricing
3. Price and Value
4. Price and Demand
5. Basic Price Structures
6. Demand Prediction
7. Price Optimization
8. Resource Allocation
9. Assortment Optimization
10. Architecture of Price Management Systems
11. Summary