Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations

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 programmatic micro-decisioning — targeted promotions and advertisements, eCommerce search, recommendations, pricing, and assortment optimization.

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 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 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

Genres: Business, Artificial Intelligence, Programming

506 pages, Hardcover

Published December 2, 2017

Reviews

[Aniruddh Jammoria · Rating 5 out of 5]

One of the most comprehensive books on Data science in marketing. Some foundational math knowledge is required (functions, linear algebra, calculus etc) but I found it extremely useful as a product manager.

[Chakrit Yau · Rating 5 out of 5]

Probably the most comprehensive book on the subject. While it is math intensive, it is beautifully written. Highly recommended.

[Madhavan · Rating 5 out of 5]

This book is just amazing ! A lovely primer and reference book for anyone in the Algorithmic marketing side.