Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations (Hardcover)

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

《算法行銷入門》是一本針對行銷策略師、數據科學家、產品經理和軟體工程師的高級行銷自動化綜合指南。該書總結了主要科技、廣告和零售公司測試過的各種技術，並將這些方法與經濟理論和機器學習相結合。該書涵蓋了需要程式化微決策的主要行銷領域，包括定向促銷和廣告、電子商務搜索、推薦、定價和組合優化。

「這是一本對於任何進行算法行銷轉型之旅的人來說都是全面且不可或缺的參考資料。」
- Ali Bouhouch, Sephora Americas 首席技術官

「這是一本數據科學家和行銷主管必讀的書籍，如果他們一起閱讀就更好了。」
- Andrey Sebrant, Yandex 战略行銷總監

「該書為您組織中的高管、中層管理人員和數據科學家提供了一系列具體、可行且漸進的建議，以便從今天開始一步一步地建立更好的洞察和決策。」
- Victoria Livschitz, Grid Dynamics 創始人兼首席技術官

《目錄》

第1章 - 簡介
- 算法行銷的主題
- 算法行銷的定義
- 歷史背景和背景知識
- 程式化服務
- 誰應該閱讀這本書？
- 摘要

第2章 - 預測建模回顧
- 描述性、預測性和規劃性分析
- 經濟優化
- 機器學習
- 監督學習
- 表示學習
- 更專業的模型
- 摘要

第3章 - 促銷和廣告
- 環境
- 商業目標
- 定向流程
- 響應建模和測量
- 建構模塊：定向和LTV模型
- 設計和執行活動
- 資源分配
- 在線廣告
- 效果測量
- 定向系統架構
- 摘要

第4章 - 搜索
- 環境
- 商業目標
- 建構模塊：匹配和排名
- 混合相關性信號
- 語義分析
- 商品搜索的搜索方法
- 相關性調整
- 商品搜索服務架構
- 摘要

第5章 - 推薦
- 環境
- 商業目標
- 質量評估
- 推薦方法概述
- 基於內容的過濾
- 協同過濾簡介
- 基於鄰域的協同過濾
- 基於模型的協同過濾
- 混合方法
- 上下文推薦
- 非個性化推薦
- 多目標優化
- 推薦系統架構
- 摘要

第6章 - 定價和組合
- 環境
- 定價的影響
- 價格和價值
- 價格和需求
- 基本價格結構
- 需求預測
- 價格優化
- 資源分配
- 組合優化
- 價格管理系統架構
- 摘要