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