Introduction to Algorithmic Marketing

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